cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fbw96t94j3.2
title: Results of the soil erosion modulus in the source region of the Yellow River from 2001 to 2022 calculated by the GA-CatBoost model

description: This study collected 537 samples of 137Cs from the source region of the Yellow River between 2015 and 2022, forming the basis for developing a comprehensive soil erosion assessment model. The dataset highlights the estimation results of the optimal model across the entire study area.
Version 2 of this dataset contains the results of sensitivity analyses based on the original (Version 1) outputs. Sensitivity analyses were performed for the following variables: precipitation, temperature, snow, NDVI, DVI, NDVGI, NDWI, SAVI, population density, and SOC. For the three meteorological variables (precipitation, temperature, and snow), we used the average values from 1963 (the onset of ^137Cs fallout) to the sampling year. For all other variables, due to data availability constraints, we used the average values from 2000 to the sampling year in the machine learning models. This dataset provides annual results for each sensitivity analysis from 2001 to 2022. The analyses demonstrate that our research findings are robust with respect to model performance, the spatial distribution of soil erosion, and the temporal trends of total soil erosion.
DOI: 10.15468/dl.a79tcj
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone mexicana L.",
 "Country is Tanzania, United Republic of",
 "HasCoordinate is true"
 ]
}

 The dataset includes 73 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081642-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b4dj82
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone mexicana L.",
 "Country is Uganda",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081643-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/2dwndv656g
title: When markets turn bearish: How green and brown investments weather the storm?

description: index1.dta is used for the main results, while eqindex.dta is used for robustness check.
DOI: 10.17632/2dwndv656g.1
title: When markets turn bearish: How green and brown investments weather the storm?

description: index1.dta is used for the main results, while eqindex.dta is used for robustness check.
DOI: 10.5281/zenodo.14374117
title: Neoantigen-2-FragPipe-Discovery

description: In this tutorial, we will guide you through a bioinformatics workflow aimed at merging neoantigen databases with known human protein sequences, preparing the data for proteomics analysis using FragPipe. This workflow involves processing FASTA files, filtering for unique sequences, and validating the FASTA databases before using FragPipe to perform peptide identification and validation of neoantigens.

Throughout the tutorial, you will learn how to integrate multiple datasets, ensuring that you can perform analyses such as the identification of potential neoantigens, which are critical for cancer immunotherapy and vaccine development. The tools and steps covered here are important for any bioinformatics pipeline dealing with proteomics and neoantigen discovery.
DOI: 10.5281/zenodo.14374118
title: Neoantigen-2-FragPipe-Discovery

description: In this tutorial, we will guide you through a bioinformatics workflow aimed at merging neoantigen databases with known human protein sequences, preparing the data for proteomics analysis using FragPipe. This workflow involves processing FASTA files, filtering for unique sequences, and validating the FASTA databases before using FragPipe to perform peptide identification and validation of neoantigens.

Throughout the tutorial, you will learn how to integrate multiple datasets, ensuring that you can perform analyses such as the identification of potential neoantigens, which are critical for cancer immunotherapy and vaccine development. The tools and steps covered here are important for any bioinformatics pipeline dealing with proteomics and neoantigen discovery.
DOI: 10.15468/dl.4cm6z8
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Argemone mexicana L.",
 "Country is Viet Nam",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081644-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/97yt4gy32t
title: Original data

description: The portion of the raw data used in this article
DOI: 10.17632/97yt4gy32t.1
title: Original data

description: The portion of the raw data used in this article
DOI: 10.34847/nkl.653bj918
title: Magkana, site_0015, secteur_5037

description: Le site est localisé à l’Est du village d’Armenochori, sur des éléments de plateau calcaire disséqués, dénivelés les uns par rapport aux autres, couverts d’une forêt claire de caroubiers, en voie de disparition, de pins et accessoirement d’oliviers peu entretenus. C’est une zone qui a été depuis longtemps utilisé comme saltus pour le petit bétail. La zone est caractérisé par sa discontinuité et par la multiplication des sites associant des éléments de plateaux facilement égouttés, des corniches de quelques mètres de commandement orientées vers le Nord, l’Est ou le Sud et des versants concaves plus ou moins raides, tendus sous les corniches.
description: Source: Études Chypriotes XVI, 2005, p. 28-31
description: Coverage: District de Limassol
description: Prospection de territoire d'Amathonte
description: coverage: District de Limassol
DOI: 10.5281/zenodo.14375466
title: Neoantigen-3-PepQuery2-Verification

description: Verification is an essential step in the process of analyzing neoantigens to ensure that the identified peptides or proteins are accurate and reliable. Neoantigens, which are novel antigens arising from tumor-specific mutations, are crucial for cancer immunotherapy. However, the prediction and identification of these neoantigens must be rigorously validated to ensure they are truly present and capable of eliciting an immune response. Without proper verification, the therapeutic potential of neoantigens cannot be realized, leading to wasted resources and failed treatments.

In this tutorial, we will guide you through a series of bioinformatics tools used to verify novel peptides and proteins predicted from mass spectrometry data. These tools help confirm the presence and specificity of the identified neoantigens by comparing them to protein databases, checking for validation against spectral data, and performing sequence analysis.
DOI: 10.5281/zenodo.15090517
title: Neoantigen-3-PepQuery2-Verification

description: Verification is an essential step in the process of analyzing neoantigens to ensure that the identified peptides or proteins are accurate and reliable. Neoantigens, which are novel antigens arising from tumor-specific mutations, are crucial for cancer immunotherapy. However, the prediction and identification of these neoantigens must be rigorously validated to ensure they are truly present and capable of eliciting an immune response. Without proper verification, the therapeutic potential of neoantigens cannot be realized, leading to wasted resources and failed treatments.

In this tutorial, we will guide you through a series of bioinformatics tools used to verify novel peptides and proteins predicted from mass spectrometry data. These tools help confirm the presence and specificity of the identified neoantigens by comparing them to protein databases, checking for validation against spectral data, and performing sequence analysis.
DOI: 10.17632/k3zv4sc5yk
title: synthetic_transactions_50k

description: Data Type: This dataset contains 50,000 entries and 10 columns, all of which are of boolean data type (bool). This indicates that each column represents a specific item (e.g., Bread, Butter, Coffee) and the values are either True or False, likely indicating the presence or absence of that item in a transaction.

Description:

Count: Each column has 50,000 non-null entries, meaning there are no missing values in this dataset.

Unique Values: All columns have 2 unique values (True and False), as expected for boolean data.

Top Value: For all columns, the most frequent value is False, with frequencies ranging from 33,807 to 34,162. This suggests that the items are generally not present in most transactions.
DOI: 10.17632/k3zv4sc5yk.1
title: synthetic_transactions_50k

description: Data Type: This dataset contains 50,000 entries and 10 columns, all of which are of boolean data type (bool). This indicates that each column represents a specific item (e.g., Bread, Butter, Coffee) and the values are either True or False, likely indicating the presence or absence of that item in a transaction.

Description:

Count: Each column has 50,000 non-null entries, meaning there are no missing values in this dataset.

Unique Values: All columns have 2 unique values (True and False), as expected for boolean data.

Top Value: For all columns, the most frequent value is False, with frequencies ranging from 33,807 to 34,162. This suggests that the items are generally not present in most transactions.
DOI: 10.5281/zenodo.14375692
title: Neoantigen-4-PepPointer-Annotation

description: Neoantigens are tumor-specific antigens that arise from somatic mutations in cancer cells. These mutations result in the generation of abnormal peptides that can be presented by the immune system’s Major Histocompatibility Complex (MHC) molecules. Neoantigens are gaining significant attention in cancer immunotherapy, as they hold the potential to be used in personalized vaccines and therapies aimed at stimulating the immune system to target and destroy tumor cells.

The process of identifying neoantigens begins with the analysis of genomic data to detect somatic mutations, followed by the prediction of the corresponding peptide sequences that can bind to MHC molecules. This process involves a combination of bioinformatics tools and techniques, including variant calling, peptide prediction, and MHC binding affinity analysis. Successful identification of neoantigens can pave the way for personalized treatment strategies that improve patient outcomes in cancer therapy.

This tutorial focuses on the Neoantigen Annotation pipeline, which is designed to assist researchers and clinicians in annotating and predicting neoantigens from high-throughput genomic data. By the end of this tutorial, you will be equipped with the skills to extract meaningful insights from genomic datasets, predict immunogenic peptides, and understand how these peptides interact with the immune system. These insights are essential for advancing the field of personalized immunotherapy and improving cancer treatment outcomes.
DOI: 10.5281/zenodo.14375693
title: Neoantigen-4-PepPointer-Annotation

description: Neoantigens are tumor-specific antigens that arise from somatic mutations in cancer cells. These mutations result in the generation of abnormal peptides that can be presented by the immune system’s Major Histocompatibility Complex (MHC) molecules. Neoantigens are gaining significant attention in cancer immunotherapy, as they hold the potential to be used in personalized vaccines and therapies aimed at stimulating the immune system to target and destroy tumor cells.

The process of identifying neoantigens begins with the analysis of genomic data to detect somatic mutations, followed by the prediction of the corresponding peptide sequences that can bind to MHC molecules. This process involves a combination of bioinformatics tools and techniques, including variant calling, peptide prediction, and MHC binding affinity analysis. Successful identification of neoantigens can pave the way for personalized treatment strategies that improve patient outcomes in cancer therapy.

This tutorial focuses on the Neoantigen Annotation pipeline, which is designed to assist researchers and clinicians in annotating and predicting neoantigens from high-throughput genomic data. By the end of this tutorial, you will be equipped with the skills to extract meaningful insights from genomic datasets, predict immunogenic peptides, and understand how these peptides interact with the immune system. These insights are essential for advancing the field of personalized immunotherapy and improving cancer treatment outcomes.
DOI: 10.17632/7gpd5chb2f
title: synthetic_transactions_noisy

description: Data Type: This dataset also contains 50,000 entries and 10 columns. However, all columns are of object data type (object), indicating that the values are stored as strings. This suggests that there might be inconsistencies or "noise" in the data, which would need to be addressed before analysis.

Description:

Count: Each column has 50,000 non-null entries, indicating no missing values.

Unique Values: All columns have 4 unique values. This confirms the presence of "noise," as ideal boolean data should only have two unique values (True/False). The additional unique values are likely variations of "True" and "False" (e.g., 'TRUE', 'True', 'FALSE', 'False' or other typos), or other unexpected entries.

Top Value: The most frequent value for all columns is 'FALSE' (in uppercase), with frequencies ranging from 32,108 to 32,479. This is similar to the synthetic_transactions_50k.csv dataset, where the majority of transactions do not contain these items.
DOI: 10.17632/7gpd5chb2f.1
title: synthetic_transactions_noisy

description: Data Type: This dataset also contains 50,000 entries and 10 columns. However, all columns are of object data type (object), indicating that the values are stored as strings. This suggests that there might be inconsistencies or "noise" in the data, which would need to be addressed before analysis.

Description:

Count: Each column has 50,000 non-null entries, indicating no missing values.

Unique Values: All columns have 4 unique values. This confirms the presence of "noise," as ideal boolean data should only have two unique values (True/False). The additional unique values are likely variations of "True" and "False" (e.g., 'TRUE', 'True', 'FALSE', 'False' or other typos), or other unexpected entries.

Top Value: The most frequent value for all columns is 'FALSE' (in uppercase), with frequencies ranging from 32,108 to 32,479. This is similar to the synthetic_transactions_50k.csv dataset, where the majority of transactions do not contain these items.
DOI: 10.15468/dl.u5xds2
title: Occurrence Download

description: A dataset listing the 12573 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Pl@ntNet automatically identified occurrences"
 ]
}

 The dataset's 12573 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081634-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.33skxj
title: Occurrence Download

description: A dataset containing 12421 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 Melierax poliopterus Cabanis, 1868",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 12421 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081646-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14375916
title: Neoantigen-5a-HLA-Binding-Prediction

description: Neoantigen prediction for HLA binding is a critical component of personalized cancer immunotherapy. Neoantigens, which are tumor-specific antigens resulting from mutations in cancer cells, can be recognized by the immune system, making them promising targets for tailored immunotherapies. Human leukocyte antigen (HLA) molecules play a key role in presenting these neoantigens on the surface of cells, where they can be detected by T-cells, triggering an immune response.

This tutorial focuses on predicting HLA binding affinities for potential neoantigens, an essential step in identifying effective targets for immunotherapy. The workflow includes the use of tools such as OptiType and seq2HLA to determine HLA genotypes, as well as data reformatting and querying methods to manage complex data outputs.
DOI: 10.5281/zenodo.14375917
title: Neoantigen-5a-HLA-Binding-Prediction

description: Neoantigen prediction for HLA binding is a critical component of personalized cancer immunotherapy. Neoantigens, which are tumor-specific antigens resulting from mutations in cancer cells, can be recognized by the immune system, making them promising targets for tailored immunotherapies. Human leukocyte antigen (HLA) molecules play a key role in presenting these neoantigens on the surface of cells, where they can be detected by T-cells, triggering an immune response.

This tutorial focuses on predicting HLA binding affinities for potential neoantigens, an essential step in identifying effective targets for immunotherapy. The workflow includes the use of tools such as OptiType and seq2HLA to determine HLA genotypes, as well as data reformatting and querying methods to manage complex data outputs.
DOI: 10.17632/kpf792wgsf.2
title: Dataset and Code for IoT-Based WISNE-SDN Detection and DDOS Attack mitigation using machine learning techniques

description: There are using two datasets, one is UNSW_2018_IoT_Botnet_Final_10_best_Training.csv and dataset_sdn.csv, the dataset is,
1. UNSW_2018_IoT_Botnet_Final_10_best_Training.csv is a refined training dataset derived from the larger UNSW 2018 IoT Botnet dataset. It focuses on the ten most significant features selected through feature selection techniques, enabling efficient and effective model training. The dataset is designed for intrusion detection and includes both benign and malicious traffic, particularly emphasizing botnet activity in IoT environments. Each record consists of traffic flow attributes such as packet counts, byte sizes, durations, and statistical measures relevant to flow behavior. This selection aims to balance model accuracy and computational cost, making it suitable for building lightweight machine learning models for real-time IoT threat detection. The dataset reflects realistic IoT traffic scenarios by incorporating attacks like DDoS, information theft, and reconnaissance, captured using various tools and emulation techniques.
2. dataset_sdn.csv is a dataset structured for evaluating security mechanisms in Software Defined Networking (SDN) environments. It contains network flow records that represent both normal and attack traffic, enabling classification and detection tasks. The dataset is typically generated in a controlled SDN testbed and includes features like source and destination IP addresses, ports, protocols, packet lengths, flow durations, and flags. Attack types might include DDoS, probing, and spoofing attacks, reflecting vulnerabilities specific to SDN-based networks. The dataset is often used to test anomaly-based intrusion detection systems in SDN controllers, where traffic patterns can be analyzed and malicious behavior flagged using machine learning techniques. It serves as a crucial resource for research in SDN security and intelligent traffic management. and also developing codes added in this datas.
DOI: 10.17632/tt6rbx7s3h
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