cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29850644.v1
title: Discursive strategies and paths forward for EU chemical legislation: bridging German stakeholder narratives on pollution and competitiveness for the announced REACH revision

description: The European Union’s (EU) chemicals policy attempts to balance human health and environmental protection with economic performance, innovation, and substance circulation. Responding to increasing pollution, assessment inadequacies, and economic hardships, the <i>von der Leyen II</i> Commission has announced a revision of the EU’s central chemical regulation, REACH (<i>Registration, Evaluation, Authorisation and Restriction of Chemicals</i>). We analyse stakeholders’ – NGOs, regulators, industrialists, researchers – interactions on policy implementation issues and future-oriented positions surrounding this revision. Given Germany’s pivotal role in EU’s chemical industries and politics, our <i>discursive agency analysis</i> investigates narratives of German stakeholders within broader EU chemical policy discourse. The dataset comprises explorative interviews, stakeholder workshop transcripts, and policy documents. We identify a discursive lock-in between two competing narratives: one prioritising chemical pollution reduction, the other boosting global competitiveness of German and EU chemical industries. This lock-in risks limiting the REACH revision to incremental changes, as stakeholders remain entrenched without resolving fundamental conflicts. To transcend stalemate, we explore underlying assumptions in both narratives that warrant scrutiny. A key opportunity lies in stakeholders’ expressed willingness to collaborate on developing and applying <i>New Approach Methodologies</i>. We propose forming a new discursive coalition that bridges market opportunities with substance grouping and chemical simplification strategies.
DOI: 10.15468/dl.bn43pb
title: Occurrence Download

description: A dataset containing 188610 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Lacerta agilis"
 ]
}

 The dataset includes 188610 records from 780 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006914-260120142942310/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.31135126
title: Raw data on the number of seeds belonging to 92 plant species attached to clothing surfaces and trapped in pockets and cuffs amongst three vegetation types (study plots), and collected during six walks in three transects and three dates per each study plot. These data were used in the publication: “How does seed diversity captured differ from that trapped by clothing?”

description: One of the most effective mechanisms for the long-distance spread of plants is epianthropochory, that is, the unintentional dispersal of seeds or other plant propagules by humans, for example, through their attachment to clothing. However, little is known about the role of the clothing’s elements other than their surface or fabric type in shaping the diversity of transported diaspores. We aimed to explore differences in taxonomic, phylogenetic, and functional diversity of seeds attached to clothes, and trapped into pockets and cuffs, including vegetation type role in shaping these differences. We designated three research plots, located in eastern Poland in suburban areas of two villages: Gołaszyn – two study plots and Krynka Łukowska - one study plot. To achieve study aim we performed the transect-walking experiment, and used non-metric multidimensional scaling ordination and mixed-effect regression methods in analyses of seeds’ diversity.<b> </b>We found prominent effects of the vegetation type on phylogenetic and functional diversity of seeds captured on clothing and trapped in pockets and cuffs, while the effects linked with taxonomic diversity were lower. The phylogenetic diversity of trapped seeds was always higher than those attached to clothes, indicating that traps can transport less closely related species in terms of their phylogenesis, expressed by a potentially wider spectrum of evolutionary adaptations to dispersal.
DOI: 10.6084/m9.figshare.31135126.v1
title: Raw data on the number of seeds belonging to 92 plant species attached to clothing surfaces and trapped in pockets and cuffs amongst three vegetation types (study plots), and collected during six walks in three transects and three dates per each study plot. These data were used in the publication: “How does seed diversity captured differ from that trapped by clothing?”

description: One of the most effective mechanisms for the long-distance spread of plants is epianthropochory, that is, the unintentional dispersal of seeds or other plant propagules by humans, for example, through their attachment to clothing. However, little is known about the role of the clothing’s elements other than their surface or fabric type in shaping the diversity of transported diaspores. We aimed to explore differences in taxonomic, phylogenetic, and functional diversity of seeds attached to clothes, and trapped into pockets and cuffs, including vegetation type role in shaping these differences. We designated three research plots, located in eastern Poland in suburban areas of two villages: Gołaszyn – two study plots and Krynka Łukowska - one study plot. To achieve study aim we performed the transect-walking experiment, and used non-metric multidimensional scaling ordination and mixed-effect regression methods in analyses of seeds’ diversity.<b> </b>We found prominent effects of the vegetation type on phylogenetic and functional diversity of seeds captured on clothing and trapped in pockets and cuffs, while the effects linked with taxonomic diversity were lower. The phylogenetic diversity of trapped seeds was always higher than those attached to clothes, indicating that traps can transport less closely related species in terms of their phylogenesis, expressed by a potentially wider spectrum of evolutionary adaptations to dispersal.
DOI: 10.15468/dl.b7ccp5
title: Occurrence Download

description: A dataset containing 1023 records in GBIF matching the query:
SELECT
 '169' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= 0.0
 AND decimalLatitude &lt;= 4.045127286108716
 AND decimalLongitude &gt;= -69.95197278929533
 AND decimalLongitude &lt;= -64.8555317946495
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 1023 records were derived from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006983-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/9bk-85t-a00
title: Albinaria idaea idaea (L. Pfeiffer, 1850)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.ydf9rb
title: Occurrence Download

description: A dataset containing 1629 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Vouauxiella lichenicola (Linds.) Petr. & Syd."
 ]
}

 The dataset includes 1629 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001540-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/kbc97x9y26
title: STOXX 3000 Sustainability Reporting Text Measures

description: This dataset provides firm-level, text-derived indicators based on sustainability reporting by STOXX 3000 companies. The current version contains a binary variable identifying whether a firm-year sustainability report includes references to the Sustainable Development Goals (SDGs). The SDG indicator is constructed through automated text processing of publicly available sustainability reports. no copyrighted raw text is included in this dataset.
Each observation is uniquely identified by ISIN and year and is accompanied by core firm metadata, including:

- Reporting year
- Country of incorporation
- Industry sector (STOXX classification)
- Index component classification (large, mid, or small cap)

These variables allow users to merge the dataset easily with data from external financial or sustainability databases.

The repository also includes replication code (R) used to generate the SDG-dummy variable and produce the empirical results for the associated publication. The code illustrates the text-processing workflow while ensuring that underlying copyrighted documents are not redistributed.

Future versions of this dataset will extend the available text-derived measures. Planned additions include indicators constructed using alternative dictionaries, custom lexicons, or thematic classifications applied to sustainability disclosures. Only derived variables will be released; raw corporate text will not be shared.

Intended use:
Researchers can use the dataset for replication, robustness checks, comparative textual analysis, or as a foundation for expanded sustainability research on STOXX 3000 firms.
DOI: 10.17632/kbc97x9y26.1
title: STOXX 3000 Sustainability Reporting Text Measures

description: This dataset provides firm-level, text-derived indicators based on sustainability reporting by STOXX 3000 companies. The current version contains a binary variable identifying whether a firm-year sustainability report includes references to the Sustainable Development Goals (SDGs). The SDG indicator is constructed through automated text processing of publicly available sustainability reports. no copyrighted raw text is included in this dataset.
Each observation is uniquely identified by ISIN and year and is accompanied by core firm metadata, including:

- Reporting year
- Country of incorporation
- Industry sector (STOXX classification)
- Index component classification (large, mid, or small cap)

These variables allow users to merge the dataset easily with data from external financial or sustainability databases.

The repository also includes replication code (R) used to generate the SDG-dummy variable and produce the empirical results for the associated publication. The code illustrates the text-processing workflow while ensuring that underlying copyrighted documents are not redistributed.

Future versions of this dataset will extend the available text-derived measures. Planned additions include indicators constructed using alternative dictionaries, custom lexicons, or thematic classifications applied to sustainability disclosures. Only derived variables will be released; raw corporate text will not be shared.

Intended use:
Researchers can use the dataset for replication, robustness checks, comparative textual analysis, or as a foundation for expanded sustainability research on STOXX 3000 firms.
DOI: 10.17632/jd2zr5pdn4
title: OverlayRef

description: Reference Materials for Research Paper Sustainable Practices in Overlay Productivity for International Airports An Integrative Review
DOI: 10.17632/jd2zr5pdn4.1
title: OverlayRef

description: Reference Materials for Research Paper Sustainable Practices in Overlay Productivity for International Airports An Integrative Review
DOI: 10.15468/dl.bhda8u
title: Occurrence Download

description: A dataset containing 1513 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Opuntia hyptiacantha"
 ]
}

 The dataset includes 1513 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005702-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17699902
title: Dados do estudo " A 200 metros, vire à esquerda: a entoação dos comandos de GPS"

description: Estes dados fazem parte de um estudo que buscou descrever a entoação da voz do GPS e compará-la com a voz humana. Para atingir esse objetivo, o corpus de análise utilizou 20 frases do Google Maps, 20 frases do Waze e a realização dessas frases por duas participantes nativas do português brasileiro na variedade alagoana, adultas, do sexo feminino e com ensino superior completo. Escolhemos uma voz do sexo feminino, pois o padrão nos aplicativos é a voz feminina. As frases de um sistema de GPS foram equivalentes às do outro sistema. A escolha desses dois aplicativos se deve à sua popularidade em trajetos de carro, em transportes públicos e a pé. Utilizamos a versão 11.28.2 do Google Maps, que possui pacotes de voz para vários países, incluindo duas vozes para o Português Brasileiro (PB), uma feminina e outra masculina. Usamos a versão 4.82.5.1 do Waze, que possui nove vozes para o PB. Escolhemos uma voz feminina para cada aplicativo, pois é o padrão dos sistemas. Para garantir a anonimização das participantes e a segurança doprocedimento, a pesquisa foi aprovada pelo Comitê de Ética em Pesquisa (CEP) da UFAL. Antes do experimento, as duas participantes leram e assinaram um Termo de Consentimento Livre e Esclarecido (TCLE). Os dados estão anotados a partir do software de análise acústica Praat.
DOI: 10.5281/zenodo.17699901
title: Dados do estudo " A 200 metros, vire à esquerda: a entoação dos comandos de GPS"

description: Estes dados fazem parte de um estudo que buscou descrever a entoação da voz do GPS e compará-la com a voz humana. Para atingir esse objetivo, o corpus de análise utilizou 20 frases do Google Maps, 20 frases do Waze e a realização dessas frases por duas participantes nativas do português brasileiro na variedade alagoana, adultas, do sexo feminino e com ensino superior completo. Escolhemos uma voz do sexo feminino, pois o padrão nos aplicativos é a voz feminina. As frases de um sistema de GPS foram equivalentes às do outro sistema. A escolha desses dois aplicativos se deve à sua popularidade em trajetos de carro, em transportes públicos e a pé. Utilizamos a versão 11.28.2 do Google Maps, que possui pacotes de voz para vários países, incluindo duas vozes para o Português Brasileiro (PB), uma feminina e outra masculina. Usamos a versão 4.82.5.1 do Waze, que possui nove vozes para o PB. Escolhemos uma voz feminina para cada aplicativo, pois é o padrão dos sistemas. Para garantir a anonimização das participantes e a segurança doprocedimento, a pesquisa foi aprovada pelo Comitê de Ética em Pesquisa (CEP) da UFAL. Antes do experimento, as duas participantes leram e assinaram um Termo de Consentimento Livre e Esclarecido (TCLE). Os dados estão anotados a partir do software de análise acústica Praat.
DOI: 10.17632/495h8779p6.1
title: Kurdish Social Media Opinions

description: The corpus was compiled through a systematic data collection process from authentic digital sources, including Kurdish-language social media, public forums, and news commentary sections. This strategy ensures the dataset reflects the dynamic and colloquial nature of the language as used by native speakers in organic, real-world settings. To ensure thematic relevance and linguistic diversity, the data collection was stratified across a range of topics of significant interest to Kurdish-speaking communities, such as politics, culture, social affairs, sports, and local current events. This domain-specific approach enhances the practical applicability of the resulting model.
A rigorous multi-stage data preprocessing pipeline was implemented to ensure corpus integrity. Initially, raw user comments were subjected to a filtering process where non-Kurdish text, unintelligible statements, and irrelevant entries were removed. Subsequently, the remaining texts underwent a normalisation procedure to address orthographic variations and common informal writing conventions typical of computer-mediated communication.
The core of the annotation scheme involved the manual classification of each textual unit into discrete sentiment categories: sadness, happiness, anger, disgust, fear, and sarcastic. This categorisation was performed by native Kurdish speakers, who were trained to interpret linguistic cues, contextual nuances, and cultural subtleties. The focus of the annotation extended beyond simple lexical polarity (e.g., the presence of positive words) to encompass a more holistic assessment of the author's intent and overall opinion, thereby adding a layer of pragmatic understanding to the dataset.
The resulting annotated corpus serves as a critical resource for advancing Kurdish language technology. It provides a reliable ground-truth dataset for training and evaluating machine learning and deep learning models tailored for sentiment analysis. This work establishes a foundational benchmark for future research in Kurdish NLP and contributes to the broader effort of developing inclusive language technologies for under-resourced languages.
DOI: 10.17632/mr67c82zw7
title: TrashNet++: A Real-Time Multi-Class Waste Recognition Dataset for Smart City and IoT Applications

description: The WasteVision dataset is a high-quality image dataset collected from the real world with the aim of assisting in object detection and classification for non-biodegradable solid waste in cities. It contains 2,600 high-resolution RGB images (with 8,320 objects) annotated and labeled into seven classes of wastes. The collection aims to provide strong support for building computer vision models in smart city applications, specifically in low-resource settings.

The photographs were shot from two socioeconomically and environmentally distinct urban zones of Bangladesh:
- Khulna Sadar (22.8456° N Latitude, 89.5403° E Longitude)
- Dhaka Pallabi (23.8261° N Latitude, 90.3654° E Longitude)

To simulate the uncertainty of actual sensors, the images in the collection were captured by three mid-range camera devices:
- A Canon EOS 1300D digital single-lens reflex
- An iPhone 12 Pro with a 12-megapixel camera
- A Samsung Galaxy A51, a second 12-megapixel midrange smartphone

Each image is saved in JPEG (.jpg) format and resized to 640×640 pixels evenly with bicubic interpolation. The resolution was chosen to enable standardizing training pipelines and interoperability with famous object detection models such as YOLOv5 and YOLOv8. The data is marked up in YOLO bounding box format as (normalized coordinates x-center, y-center, width, height) and class IDs for each object. The annotations were all done manually via Roboflow with great accuracy and then also verified by multiple annotators to maintain consistency. For each image, there is a corresponding .txt file with bounding box information, which is saved in separate label directories reflecting the YOLO directory layout.

The data is in three structured subsets:
- Training set has 1,825 images and 5,913 waste object annotations.
- Validation set has 515 images and 1,634 annotations.
- Test set has 260 images and 773 annotations.

This separation makes it easy to have plug-and-play compatibility with deep learning workflows, especially YOLO-based pipelines, with a layout like images/train/, images/val/, images/test/ and corresponding labels/ directories.

The information includes seven impact-dense waste types according to urban frequency, health risk, and ecological importance:
- Plastic: Includes plastic boxes, packaging, and bottles, which dominate urban waste streams.
- Metal: Includes metal rods, sheets, and boxes; typically industrial and construction.
- Glass: Includes crushed glass and bottles made of glass, typically in bins or mixed with other trash.
- Can: Typically aluminum beverage cans discarded in commercial districts or near public events.
- Cable: Refers to electric cables and wires, which is the growing problem of technology waste. 
- E-waste: Comprises dumped chargers, circuit boards, and faulty electronics, with high possibilities of toxicity.
- Medical Waste: Comprises used masks, gloves, syringes, and medicine vials, especially from hospitals or roadside dumps following the pandemic.
DOI: 10.17632/mr67c82zw7.2
title: TrashNet++: A Real-Time Multi-Class Waste Recognition Dataset for Smart City and IoT Applications

description: The WasteVision dataset is a high-quality image dataset collected from the real world with the aim of assisting in object detection and classification for non-biodegradable solid waste in cities. It contains 2,600 high-resolution RGB images (with 8,320 objects) annotated and labeled into seven classes of wastes. The collection aims to provide strong support for building computer vision models in smart city applications, specifically in low-resource settings.

The photographs were shot from two socioeconomically and environmentally distinct urban zones of Bangladesh:
- Khulna Sadar (22.8456° N Latitude, 89.5403° E Longitude)
- Dhaka Pallabi (23.8261° N Latitude, 90.3654° E Longitude)

To simulate the uncertainty of actual sensors, the images in the collection were captured by three mid-range camera devices:
- A Canon EOS 1300D digital single-lens reflex
- An iPhone 12 Pro with a 12-megapixel camera
- A Samsung Galaxy A51, a second 12-megapixel midrange smartphone

Each image is saved in JPEG (.jpg) format and resized to 640×640 pixels evenly with bicubic interpolation. The resolution was chosen to enable standardizing training pipelines and interoperability with famous object detection models such as YOLOv5 and YOLOv8. The data is marked up in YOLO bounding box format as (normalized coordinates x-center, y-center, width, height) and class IDs for each object. The annotations were all done manually via Roboflow with great accuracy and then also verified by multiple annotators to maintain consistency. For each image, there is a corresponding .txt file with bounding box information, which is saved in separate label directories reflecting the YOLO directory layout.

The data is in three structured subsets:
- Training set has 1,825 images and 5,913 waste object annotations.
- Validation set has 515 images and 1,634 annotations.
- Test set has 260 images and 773 annotations.

This separation makes it easy to have plug-and-play compatibility with deep learning workflows, especially YOLO-based pipelines, with a layout like images/train/, images/val/, images/test/ and corresponding labels/ directories.

The information includes seven impact-dense waste types according to urban frequency, health risk, and ecological importance:
- Plastic: Includes plastic boxes, packaging, and bottles, which dominate urban waste streams.
- Metal: Includes metal rods, sheets, and boxes; typically industrial and construction.
- Glass: Includes crushed glass and bottles made of glass, typically in bins or mixed with other trash.
- Can: Typically aluminum beverage cans discarded in commercial districts or near public events.
- Cable: Refers to electric cables and wires, which is the growing problem of technology waste. 
- E-waste: Comprises dumped chargers, circuit boards, and faulty electronics, with high possibilities of toxicity.
- Medical Waste: Comprises used masks, gloves, syringes, and medicine vials, especially from hospitals or roadside dumps following the pandemic.
DOI: 10.17632/yky98xv8rm
title: Document Supplementary Tables

description: Supplementary materials of manuscript