cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/8xykwpw7g8
title: Eco-Morphological Exploration of Avian Communities and Floral Diversity: Bird-Tree Interactions at Waghai (Dang) Forest, South Gujarat, India

description: The tropical deciduous forests of the Dang district in South Gujarat represent one of the most biodiverse regions in Western India. This study was conducted to document the avifaunal diversity and analyze how tree species composition, phenology, and structural complexity influence bird community dynamics. The primary objective was to quantify species richness and abundance while mapping the ecological niches occupied by different bird guilds in relation to specific tree species. Data were collected using point counts and opportunistic observations. For every bird sighting, the following parameters were recorded: species identity (Order, Family, Common Name), number of individuals, activity (e.g., feeding, calling, perching), and detailed substrate information (tree species name, GBH, tree height, and phenology). Canopy positioning was further categorized into vertical (Lower, Middle, Upper) and horizontal (Inner, Outer) strata to understand microhabitat preferences. A total of 1,003 individuals were recorded, representing 68 bird species belonging to 39 families and 16 orders. The study revealed a highly diverse community with a Shannon-Wiener Diversity Index (H') of 3.60, indicating a well-distributed and ecologically stable population. The Order Passeriformes was the most dominant, with the family Leiothrichidae (e.g., Jungle Babblers) showing the highest abundance. Other significant families included Columbidae (Pigeons and Doves) and Estrildidae (Munias). The analysis of feeding guilds showed a predominance of Insectivorous and Granivorous birds. A critical finding was the strong association between bird presence and specific tree species. Acacia nilotica, Tectona grandis, and Ficus species emerged as vital substrates for perching and feeding activities. Phenological data indicated that trees in the Fruiting and Flowering stages attracted significantly higher bird counts, particularly nectarivorous species like the Purple Sunbird (Cinnyris asiaticus) and frugivores. Birds utilized different canopy layers based on their feeding habits. Insectivores were predominantly found in the middle and upper canopies, while granivores were often associated with the lower canopy or ground level. The "Inner" canopy was frequently used for perching and nesting, providing protection, whereas the "Outer" canopy was the primary site for active feeding and calling. The high species richness and diversity index underscore the conservation importance of the Waghai forest region. The significant correlation between tree phenology and bird activity suggests that maintaining a diverse tree species composition is essential for sustaining the avifaunal community. These findings provide a baseline for future conservation efforts and habitat management in the Dang district, highlighting the need to protect old-growth trees and diverse floral species to maintain ecological balance.
DOI: 10.17632/8xykwpw7g8.2
title: Eco-Morphological Exploration of Avian Communities and Floral Diversity: Bird-Tree Interactions at Waghai (Dang) Forest, South Gujarat, India

description: The tropical deciduous forests of the Dang district in South Gujarat represent one of the most biodiverse regions in Western India. This study was conducted to document the avifaunal diversity and analyze how tree species composition, phenology, and structural complexity influence bird community dynamics. The primary objective was to quantify species richness and abundance while mapping the ecological niches occupied by different bird guilds in relation to specific tree species. Data were collected using point counts and opportunistic observations. For every bird sighting, the following parameters were recorded: species identity (Order, Family, Common Name), number of individuals, activity (e.g., feeding, calling, perching), and detailed substrate information (tree species name, GBH, tree height, and phenology). Canopy positioning was further categorized into vertical (Lower, Middle, Upper) and horizontal (Inner, Outer) strata to understand microhabitat preferences. A total of 1,003 individuals were recorded, representing 68 bird species belonging to 39 families and 16 orders. The study revealed a highly diverse community with a Shannon-Wiener Diversity Index (H') of 3.60, indicating a well-distributed and ecologically stable population. The Order Passeriformes was the most dominant, with the family Leiothrichidae (e.g., Jungle Babblers) showing the highest abundance. Other significant families included Columbidae (Pigeons and Doves) and Estrildidae (Munias). The analysis of feeding guilds showed a predominance of Insectivorous and Granivorous birds. A critical finding was the strong association between bird presence and specific tree species. Acacia nilotica, Tectona grandis, and Ficus species emerged as vital substrates for perching and feeding activities. Phenological data indicated that trees in the Fruiting and Flowering stages attracted significantly higher bird counts, particularly nectarivorous species like the Purple Sunbird (Cinnyris asiaticus) and frugivores. Birds utilized different canopy layers based on their feeding habits. Insectivores were predominantly found in the middle and upper canopies, while granivores were often associated with the lower canopy or ground level. The "Inner" canopy was frequently used for perching and nesting, providing protection, whereas the "Outer" canopy was the primary site for active feeding and calling. The high species richness and diversity index underscore the conservation importance of the Waghai forest region. The significant correlation between tree phenology and bird activity suggests that maintaining a diverse tree species composition is essential for sustaining the avifaunal community. These findings provide a baseline for future conservation efforts and habitat management in the Dang district, highlighting the need to protect old-growth trees and diverse floral species to maintain ecological balance.
DOI: 10.17632/7n4whkjf62
title: A role-based NLP sentiment analysis of acceptance, trust, and action in autonomous mobility

description: The Published Research Data for “Who moves with the robotaxi? A role-based NLP sentiment analysis of acceptance, trust, and action in autonomous mobility” provides the full evidence base and intermediate analytical outputs underpinning the role-based findings reported in the manuscript (including the stakeholder-specific action drivers and barriers summarised in Table 2). The repository is organised into three role-stratified folders, Pedestrian_Filter, Passenger_filter, and Motorist_Filter, corresponding to discourse produced by pedestrians, passengers, and motorists, respectively. Within each role folder, we deposit (i) the raw public comment corpus used in the study (with direct identifiers removed and records retained in a structured, inspectable format), (ii) pre-processed text files used for modelling (e.g., cleaned/normalised strings and analysis-ready fields), and (iii) the node-level outputs generated by the NLP pipeline that enable a transparent audit trail from data to interpretation (e.g., per-comment role assignment, sentiment/stance signals, semantic representations, and cluster membership/weights). We additionally include the role-specific thematic cluster tables and the corresponding representative expressions/exemplar snippets used to illustrate each theme, allowing readers to evaluate how frequently a theme appears, how it is linguistically expressed, and how it varies by stakeholder role. A top-level README and data dictionary describe file structure, variable definitions, and decision rules, enabling independent verification of the presence and prevalence of the themes discussed and reducing concerns about selective exemplification. In short, the dataset supports traceability by linking each interpretive claim in the manuscript to the underlying role-specific clusters and illustrative expressions, while providing sufficient intermediate outputs for readers to reproduce and scrutinise the core analytical steps.
DOI: 10.17632/7n4whkjf62.1
title: A role-based NLP sentiment analysis of acceptance, trust, and action in autonomous mobility

description: The Published Research Data for “Who moves with the robotaxi? A role-based NLP sentiment analysis of acceptance, trust, and action in autonomous mobility” provides the full evidence base and intermediate analytical outputs underpinning the role-based findings reported in the manuscript (including the stakeholder-specific action drivers and barriers summarised in Table 2). The repository is organised into three role-stratified folders, Pedestrian_Filter, Passenger_filter, and Motorist_Filter, corresponding to discourse produced by pedestrians, passengers, and motorists, respectively. Within each role folder, we deposit (i) the raw public comment corpus used in the study (with direct identifiers removed and records retained in a structured, inspectable format), (ii) pre-processed text files used for modelling (e.g., cleaned/normalised strings and analysis-ready fields), and (iii) the node-level outputs generated by the NLP pipeline that enable a transparent audit trail from data to interpretation (e.g., per-comment role assignment, sentiment/stance signals, semantic representations, and cluster membership/weights). We additionally include the role-specific thematic cluster tables and the corresponding representative expressions/exemplar snippets used to illustrate each theme, allowing readers to evaluate how frequently a theme appears, how it is linguistically expressed, and how it varies by stakeholder role. A top-level README and data dictionary describe file structure, variable definitions, and decision rules, enabling independent verification of the presence and prevalence of the themes discussed and reducing concerns about selective exemplification. In short, the dataset supports traceability by linking each interpretive claim in the manuscript to the underlying role-specific clusters and illustrative expressions, while providing sufficient intermediate outputs for readers to reproduce and scrutinise the core analytical steps.
DOI: 10.17632/6nhtnmn2yk.1
title: url phishing

description: The dataset employed in this study is a large-scale, clustered phishing detection dataset designed to support advanced machine learning (ML), deep learning (DL), and hybrid AI-based approaches for identifying phishing and malicious URLs. 

The specific dataset under consideration, referred to as the Cluster dataset, contains 147,292 individual samples, each corresponding to a unique URL instance. These instances represent both malicious (phishing) and benign (legitimate) URLs collected from multiple heterogeneous sources, ensuring diversity in terms of domain structure, hosting infrastructure, and attack sophistication. The dataset is structured for binary classification, making it suitable for supervised learning paradigms.

Each sample is described by 112 numerical features, all of which are derived from URL strings, domain metadata, DNS records, and network-level observations. The exclusive use of numeric features eliminates the need for extensive encoding or tokenization steps, allowing direct compatibility with a wide range of ML and DL algorithms.

Class Labels and Distribution

The target variable in the dataset is denoted as label, which follows a binary encoding scheme:

Label = 1: Indicates phishing or malicious URLs

Label = 0: Indicates legitimate or benign URLs

Out of the total 147,292 samples, the dataset includes:

61,294 malicious URLs (positive class)

85,998 benign URLs (negative class)

This distribution reflects a moderate class imbalance, with benign URLs slightly dominating the dataset. Such imbalance is typical of real-world cybersecurity datasets, where legitimate traffic generally exceeds malicious activity. The presence of this imbalance makes the dataset particularly useful for evaluating classifier robustness, precision–recall trade-offs, and cost-sensitive learning strategies.
 URL-Centric Feature Design
URLs remain one of the most widely exploited vectors for phishing attacks, serving as entry points for credential theft, malware delivery, and social engineering campaigns. Modern phishing URLs often employ lexical obfuscation, domain impersonation, excessive parameterization, and short-lived infrastructure to evade detection.
To address these challenges, the dataset emphasizes URL-based characteristics that capture both surface-level patterns and deep structural cues associated with malicious intent. The selected features aim to balance interpretability, discriminative power, and computational efficiency, making them suitable for both traditional ML models and complex DL architectures.
Feature Composition and Categorization
The 112 features in the dataset can be broadly categorized into the following groups:
Lexical and Character-Level Features
Structural and Length-Based Features
Directory and Parameter Analysis Features
Domain and Host-Based Features
Network and Infrastructure-Level Features
Security and Certificate-Related Features
DOI: 10.17632/pw2m6v6smt
title: Suitability-space species trait dataset for priority invasive alien plants (Ailanthus altissima; Acer negundo) in the Danubian riparian corridor

description: Description 
Woody invasive alien plant species (IAS) can be characterised by their realised suitability niche derived from modelled associations between occurrences and environmental gradients. This dataset serves as a species trait database in the form of model-derived “suitability-space traits” (predictor–suitability association signatures), together with harmonised 1-km predictors, domain tables, and scenario-specific normalised suitability surfaces for the Danubian riparian corridor. This enables users to fit alternative SDM model families, quantify predictor–suitability associations (“species suitability-space traits”), assess transferability across domains, and test bias-correction strategies using accessibility-linked predictors.

The release contains: (1) an AOI polygon (Danubian riparian corridor extent); (2) screened environmental predictors aligned to a common 1-km grid in ETRS89-LAEA (EPSG:3035), provided as transformed (where relevant) and z-standardised rasters (mean 0, SD 1) with the scaling table; (3) species-specific occurrence bundles (Ailanthus altissima; Acer negundo) with three 1-km aggregated domain tables: Danube calibration data using a target-group background (TGB) design (presence vs TGB pseudoabsence; includes spatial CV fold IDs), and two independent evaluation domains (Romania and Serbia) with 1-km presence/absence tables. All tables include a stable 1-km cell identifier (cell_id). (4) Scenario-specific PN suitability GeoTIFFs (Figs. S1–S8): each raster is probability-like normalised to sum to 1 within the Danube AOI.

PN GeoTIFF naming template
PN_{species}_{scenario}_1km_EPSG3035.tif, where species ∈ {Ailanthus_altissima, Acer_negundo} and scenario ∈ {S1_baseline, S2_bias_aware, S3_romania, S4_serbia_riparian}.

Primary occurrence sources (Danube calibration; opportunistic citizen science)
GBIF (Global Biodiversity Information Facility; aggregated backbone and publisher feeds): https://www.gbif.org

iNaturalist: https://www.inaturalist.org

Observation.org: https://observation.org

Pl@ntNet: https://plantnet.org

NABU|naturgucker: https://www.naturgucker.de
 ; https://www.nabu.de

External-domain evaluation data sources
Romania: Anastasiu, P. et al. (2024). Alien plant species distribution in Romania… Biodiversity Data Journal, 12, e119539. https://doi.org/10.3897/BDJ.12.e119539

Serbia: Anđelković, A. A. et al. (2022). Plant invasions in riparian areas… NeoBiota, 71, 23–48. https://doi.org/10.3897/neobiota.71.69716

This dataset accompanies the manuscript by Halabuk et al., “From citizen science records to invasive plant indicators: multi-domain evaluation and scenario-based model selection for the Danube corridor”.

Acknowledgement
Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I01-03-V04-00005.
DOI: 10.17632/pw2m6v6smt.1
title: Suitability-space species trait dataset for priority invasive alien plants (Ailanthus altissima; Acer negundo) in the Danubian riparian corridor

description: Description 
Woody invasive alien plant species (IAS) can be characterised by their realised suitability niche derived from modelled associations between occurrences and environmental gradients. This dataset serves as a species trait database in the form of model-derived “suitability-space traits” (predictor–suitability association signatures), together with harmonised 1-km predictors, domain tables, and scenario-specific normalised suitability surfaces for the Danubian riparian corridor. This enables users to fit alternative SDM model families, quantify predictor–suitability associations (“species suitability-space traits”), assess transferability across domains, and test bias-correction strategies using accessibility-linked predictors.

The release contains: (1) an AOI polygon (Danubian riparian corridor extent); (2) screened environmental predictors aligned to a common 1-km grid in ETRS89-LAEA (EPSG:3035), provided as transformed (where relevant) and z-standardised rasters (mean 0, SD 1) with the scaling table; (3) species-specific occurrence bundles (Ailanthus altissima; Acer negundo) with three 1-km aggregated domain tables: Danube calibration data using a target-group background (TGB) design (presence vs TGB pseudoabsence; includes spatial CV fold IDs), and two independent evaluation domains (Romania and Serbia) with 1-km presence/absence tables. All tables include a stable 1-km cell identifier (cell_id). (4) Scenario-specific PN suitability GeoTIFFs (Figs. S1–S8): each raster is probability-like normalised to sum to 1 within the Danube AOI.

PN GeoTIFF naming template
PN_{species}_{scenario}_1km_EPSG3035.tif, where species ∈ {Ailanthus_altissima, Acer_negundo} and scenario ∈ {S1_baseline, S2_bias_aware, S3_romania, S4_serbia_riparian}.

Primary occurrence sources (Danube calibration; opportunistic citizen science)
GBIF (Global Biodiversity Information Facility; aggregated backbone and publisher feeds): https://www.gbif.org

iNaturalist: https://www.inaturalist.org

Observation.org: https://observation.org

Pl@ntNet: https://plantnet.org

NABU|naturgucker: https://www.naturgucker.de
 ; https://www.nabu.de

External-domain evaluation data sources
Romania: Anastasiu, P. et al. (2024). Alien plant species distribution in Romania… Biodiversity Data Journal, 12, e119539. https://doi.org/10.3897/BDJ.12.e119539

Serbia: Anđelković, A. A. et al. (2022). Plant invasions in riparian areas… NeoBiota, 71, 23–48. https://doi.org/10.3897/neobiota.71.69716

This dataset accompanies the manuscript by Halabuk et al., “From citizen science records to invasive plant indicators: multi-domain evaluation and scenario-based model selection for the Danube corridor”.

Acknowledgement
Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I01-03-V04-00005.
DOI: 10.17632/9hfzb63k2w
title: Candidate SDM model pool for priority woody invasive plant species (Ailanthus altissima, Acer negundo) in the Danubian riparian corridor

description: Candidate models were generated to support multi-objective, post-hoc selection of SDMs for woody invasive alien plant species (IAS) in the Danubian riparian corridor. Occurrence data were compiled from opportunistic citizen-science observations and harmonised under a target-group background design, then aggregated to a common 1-km grid that served as the modelling unit across the Danube calibration domain and two independent external evaluation domains (Romania and Serbia). Environmental predictors were screened as described in the associated manuscript.

Model calibration used penalised logistic Generalised Additive Models (GAMs) implemented in Python (pyGAM, LogisticGAM). Candidate configurations were sampled via a Latin hypercube sampling (LHS) design over (i) the smoothness/regularisation parameter λ (sampled log-uniformly across orders of magnitude) and (ii) model complexity (integer number of environmental predictors). For each LHS draw, a predictor subset was randomly sampled from the screened predictor pool under these constraints, and a model was fitted on the Danube calibration domain.

Each candidate model was evaluated using (1) spatially blocked cross-validation on the Danube domain (AUC) and (2) external transfer evaluation (AUC) on Romania and Serbia. These metrics support post-hoc selection following a standard MCDA logic: users can change objective weights (e.g., prioritise external transferability vs internal validation, or penalise complexity) and select the candidate that best matches their criteria. The rationale and definition of selection criteria are provided in the manuscript.

Model artefacts are included to enable re-use. The performance table links each candidate to a stored model via the model_tag / model_name identifier. All fitted candidate models are provided in models_MULTI_LHC.zip (1000 models per species), allowing users to reproduce alternative selections and generate maps for their chosen candidate(s). Model-ready predictors for inference are provided in eco_1000m_z.zip as transformed (where applicable) and z-standardised GeoTIFF rasters aligned to a 1-km ETRS89-LAEA (EPSG:3035) grid. AOI.zip contains the AOI polygon (shapefile) used for optional masking. The scaling-parameter CSV included in eco_1000m_z.zip documents how the provided standardised rasters were produced and is not required if users run inference directly with the provided z-standardised rasters.

Associated manuscript
This dataset accompanies the manuscript by Halabuk et al., “From citizen science records to invasive plant indicators: multi-domain evaluation and scenario-based model selection for the Danube corridor”.

Acknowledgement: “Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I01-03-V04-00005.”