cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rfzmm89r5d.1
title: Molecular mechanism of gallium nitrate in inhibiting bacterial biofilm formation through pykF modulation

description: In the context of bacterial regulation, gallium nitrate—a non-redox analog of iron(III)—inhibits bacterial biofilms and virulence. This study aims to elucidate the potential mechanisms and modes of action through which gallium nitrate regulates bacterial activity and function.
We investigated the antimicrobial properties of gallium nitrate, its impact on bacterial biofilms, and gallium-related bacterial signaling pathways. Following the observation that the antibiofilm activity of gallium nitrate significantly upregulated the expression of pyruvate kinase (the pykF gene), we performed in vitro and in vivo studies to explore how gallium regulates the expression, activity, and function of bacterial pykF.
Crystal violet staining, XTT assay, confocal laser scanning microscopy, and scanning electron microscopy confirmed that gallium nitrate effectively inhibited bacterial biofilm formation and activity. Subsequent transcriptomic sequencing and validation screening identified a potential close association between pykF and gallium nitrate-mediated bacterial inhibition. Finally, both in vitro and in vivo experiments demonstrated that pykF knockout significantly increased bacterial viability as well as biofilm formation ability.
We demonstrated that gallium nitrate regulates bacterial biofilm formation and virulence. Furthermore, its antimicrobial activity was attributed to the upregulation of pykF. Therefore, simultaneous intervention targeting both pykF and gallium may offer a more effective therapeutic strategy against recalcitrant bacterial infections caused by biofilms. Our findings also provide a novel rationale for clinically reducing biofilm formation and mitigating device-associated infections.
DOI: 10.17632/bkxcchbsxb
title: Peel performance of composite sandwich parts manufactured by a novel in-situ consolidation robotic 3D printing technique

description: The repository includes the following:
1- Specimen design
2- Specimen manufacturing
3- Drum peel testing
4- Physical observations
5- Micrographs
DOI: 10.17632/bkxcchbsxb.2
title: Peel performance of composite sandwich parts manufactured by a novel in-situ consolidation robotic 3D printing technique

description: The repository includes the following:
1- Specimen design
2- Specimen manufacturing
3- Drum peel testing
4- Physical observations
5- Micrographs
DOI: 10.48544/37a7971a-56b2-4ad6-8b91-cc1169dcf99a
title: Opvoeden doe je samen!
DOI: 10.15468/dl.uy87ys
title: Occurrence Download

description: A dataset containing 24284 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Weed plants of the European part of Russia"
 ]
}

 The dataset includes 24284 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007332-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/n8fnmb89jr
title: Molecular Dynamics Simulation Data for Prediction of Mechanical Properties of Epoxy with Graphene Quantum Dots

description: This data inlcudes the MD modeling and simulation data for enhancing the mechanical properties of epoxy with graphene quantum dots. MD simulations are performed with the LAMMPS software. Fixed-bond class-2 forcefield called IFF was utilized for MD simulations. Tensile test results of MD simulations are analyzed using LUNAR developed by Josh Kemppainen at CMMR Lab, Michigan Technological University.
DOI: 10.17632/n8fnmb89jr.2
title: Molecular Dynamics Simulation Data for Prediction of Mechanical Properties of Epoxy with Graphene Quantum Dots

description: This data inlcudes the MD modeling and simulation data for enhancing the mechanical properties of epoxy with graphene quantum dots. MD simulations are performed with the LAMMPS software. Fixed-bond class-2 forcefield called IFF was utilized for MD simulations. Tensile test results of MD simulations are analyzed using LUNAR developed by Josh Kemppainen at CMMR Lab, Michigan Technological University.
DOI: 10.15468/dl.z6fs5v
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Animalia)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "Country is one of (Georgia, Russian Federation, Azerbaijan)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2010",
 "Modified is greater than or equal to 2025-11-17"
 ]
}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007334-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/vw4pbvdx7h
title: Bangladeshi Native Birds Image Dataset (13 Species, 390 Images)

description: This dataset contains 390 original images of 13 native Bangladeshi bird species . The images are manually curated and labeled for fine-grained classification tasks.
DOI: 10.17632/vw4pbvdx7h.1
title: Bangladeshi Native Birds Image Dataset (13 Species, 390 Images)

description: This dataset contains 390 original images of 13 native Bangladeshi bird species . The images are manually curated and labeled for fine-grained classification tasks.
DOI: 10.17632/pvrdx7hwhz.1
title: A Natural Language Processing Framework for Toxicity Detection in Low-Resource Languages: A Case Study on the Twi Language

description: This dataset contains 2,001 text entries labeled for toxicity classification. Each entry represents a user-generated comment along with an assigned toxicity label. The dataset is structured into two columns:

COMMENT– A text field containing comments written primarily in Akan (Twi). These comments include expressions of gratitude, feedback, conversational messages, and general communication typical of social or online interactions.
LABEL– A categorical variable indicating whether the comment is 'toxic' or 'non-toxic'.
Current labels present in the dataset: 'non-toxic' (and any others present in the full file, if applicable).
Key Features:
• Total records: 2,001
• Language: Primarily Akan (Twi)
• Classification type: Binary toxicity classification
There are no missing values (both columns have 2,001 non-null entries)
Data types: ‘COMMENT’: string and ‘LABEL’`: string
This dataset can support research in:
• Toxic language detection in low-resource languages
• Natural Language Processing (NLP) for African languages
• Machine learning model training for text classification
• Sociolinguistic analysis of online conversational content
The File Format is CSV file: Toxicity_dataset.csv
It contains two columns: 'COMMENT' and ‘LABEL'
DOI: 10.17632/8bhy999ssy.2
title: Nogor Bahon AI Dataset (NBD)

description: This dataset was created using vehicle pictures gathered in Chattogram, Bangladesh. It includes 28 unique categories of vehicles regularly observed in the city. Data was collected in a variety of contexts to replicate the city's actual traffic circumstances. To improve generalization, vehicles were photographed in a variety of lighting conditions, angles, and backgrounds. It can be used to train, validate, and test machine learning algorithms. This material is very valuable for computer vision research purposes. Vehicle detection, classification, recognition, and traffic monitoring are all potential domains. It may also fund future projects centered on intelligent transportation systems. The dataset attempts to develop automated traffic management technologies. The dataset's comprehensiveness is ensured by its inclusion of several categories.
DOI: 10.17632/gj3ytm3n7v
title: Playing With Blocs: Quantifying Decoupling

description: Replication files for "Playing With Blocs: Quantifying Decoupling".
DOI: 10.17632/gj3ytm3n7v.1
title: Playing With Blocs: Quantifying Decoupling

description: Replication files for "Playing With Blocs: Quantifying Decoupling".
DOI: 10.17632/bfc7yhshvv
title: Induction of ferromagnetism in multiferroic LaFeO3 by doping with Ni2+

description: Experimental results and treatments on LaFeO3 nickel doping research.
DOI: 10.17632/bfc7yhshvv.2
title: Induction of ferromagnetism in multiferroic LaFeO3 by doping with Ni2+

description: Experimental results and treatments on LaFeO3 nickel doping research.
DOI: 10.17632/s9hpmhrpwp.1
title: Joint Production Simulation Cu Co - Fikru, Ohler, Romani

description: This R program simulates the joint production of copper and cobalt, with random parameters that define the marginal cost of extraction, environmental cost, and technical efficiency. Output is provided as summary statistics, and graphical visualizations
DOI: 10.5281/zenodo.17709186
title: MUSE - Outdoor pedestrian counts

description: Pedestrians detected from the analysis of Very High Resolution (VHR) satellite images from Pleiades Neo and Maxar with 15 cm resolution over central Madrid on 28 February 2023, 31 July 2023 and 2 June 2024. The detection of pedestrians was performed by a deep learning model high-Resolution Network (HRNet) architecture.
DOI: 10.5281/zenodo.17709187
title: MUSE - Outdoor pedestrian counts

description: Pedestrians detected from the analysis of Very High Resolution (VHR) satellite images from Pleiades Neo and Maxar with 15 cm resolution over central Madrid on 28 February 2023, 31 July 2023 and 2 June 2024. The detection of pedestrians was performed by a deep learning model high-Resolution Network (HRNet) architecture.
DOI: 10.15468/dl.h37x48
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ovophis zayuensis)",
 "Country is one of (China, India, Indonesia, Pakistan, Bangladesh, Japan, Philippines, Viet Nam, Türkiye, Iran (Islamic Republic of), Thailand, Myanmar, Korea, Republic of, Korea (Democratic People’s Republic of), Iraq, Afghanistan, Saudi Arabia, Uzbekistan, Malaysia, Yemen, Nepal, Sri Lanka, Kazakhstan, Syrian Arab Republic, Cambodia, Jordan, Azerbaijan, United Arab Emirates, Tajikistan, Israel, Lao People’s Democratic Republic, Lebanon, Kyrgyzstan, Turkmenistan, Singapore, Oman, Palestine, State of, Kuwait, Georgia, Mongolia, Armenia, Qatar, Bahrain, Timor-Leste, Cyprus, Bhutan, Maldives, Brunei Darussalam)",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007335-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n9nqa8
title: Occurrence Download

description: A dataset containing 908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Attalea allenii",
 "HasCoordinate is true"
 ]
}

 The dataset includes 908 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007336-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/pvvkv2j6dx
title: Portuguese Financial, Legal, and Property Documents Dataset

description: This dataset was developed to support research on automatic document classification using Large Language Models (LLMs) within the financial and banking domain. The underlying research hypothesis is that, despite its relatively small size, a dataset that is diverse in document structure, origin, and linguistic variation can still enable reliable evaluation of LLM-based classification pipelines. The data aims to demonstrate how well modern language models can distinguish between document types that share overlapping terminology, complex structure, or noisy text, which are common conditions in real-world financial institutions.

The dataset contains 270 publicly available Portuguese PDF documents, evenly distributed across nine classes (30 documents each). These classes represent document types commonly handled in financial and administrative processes: Annual Report, Trial Balance, Commercial Registry Certificate, Deed, Property Valuation Report, Land Registry Certificate, Property and Urban Register, Energy Certificate, and a final Other category. The “Other” class includes documents that either partially resemble the main categories or present unique characteristics, enabling the evaluation of how classification models handle out-of-distribution content.

A notable feature of the dataset is its heterogeneity since it includes: born-digital PDFs, digitized and scanned documents as well as documents containing handwritten notes or annotations, introducing realistic noise and OCR challenges. This variation not only reflects what is commonly found in typical banking workflows but also increases the relevance of the dataset for studying preprocessing and extraction strategies in LLM-based pipelines.

Preliminary examination of the dataset highlights clear structural and semantic patterns across classes, for example, the tabular and numerical structure of Trial Balances versus the legal and narrative style of Deeds. At the same time, it underscores subtle distinctions between similar categories such as Land Registry Certificates and Property and Urban Registers, where classification may rely on contextual cues rather than simple keyword matching. These characteristics make the dataset a useful testbed for LLM-based document understanding tasks.

Researchers can use this dataset in diverse ways such as: train and evaluate LLM-based solutions, benchmark OCR + LLM pipelines on mixed-quality PDFs, analyze linguistic and structural ambiguity between related financial and legal documents, develop end-to-end document understanding systems, including text extraction, summarization, or metadata tagging.

Overall, this dataset offers a compact yet realistic sample of financial and legal documents encountered in practice and is well suited for research on document classification, OCR robustness, and LLM-based document processing.
DOI: 10.17632/pvvkv2j6dx.1
title: Portuguese Financial, Legal, and Property Documents Dataset

description: This dataset was developed to support research on automatic document classification using Large Language Models (LLMs) within the financial and banking domain. The underlying research hypothesis is that, despite its relatively small size, a dataset that is diverse in document structure, origin, and linguistic variation can still enable reliable evaluation of LLM-based classification pipelines. The data aims to demonstrate how well modern language models can distinguish between document types that share overlapping terminology, complex structure, or noisy text, which are common conditions in real-world financial institutions.

The dataset contains 270 publicly available Portuguese PDF documents, evenly distributed across nine classes (30 documents each). These classes represent document types commonly handled in financial and administrative processes: Annual Report, Trial Balance, Commercial Registry Certificate, Deed, Property Valuation Report, Land Registry Certificate, Property and Urban Register, Energy Certificate, and a final Other category. The “Other” class includes documents that either partially resemble the main categories or present unique characteristics, enabling the evaluation of how classification models handle out-of-distribution content.

A notable feature of the dataset is its heterogeneity since it includes: born-digital PDFs, digitized and scanned documents as well as documents containing handwritten notes or annotations, introducing realistic noise and OCR challenges. This variation not only reflects what is commonly found in typical banking workflows but also increases the relevance of the dataset for studying preprocessing and extraction strategies in LLM-based pipelines.

Preliminary examination of the dataset highlights clear structural and semantic patterns across classes, for example, the tabular and numerical structure of Trial Balances versus the legal and narrative style of Deeds. At the same time, it underscores subtle distinctions between similar categories such as Land Registry Certificates and Property and Urban Registers, where classification may rely on contextual cues rather than simple keyword matching. These characteristics make the dataset a useful testbed for LLM-based document understanding tasks.

Researchers can use this dataset in diverse ways such as: train and evaluate LLM-based solutions, benchmark OCR + LLM pipelines on mixed-quality PDFs, analyze linguistic and structural ambiguity between related financial and legal documents, develop end-to-end document understanding systems, including text extraction, summarization, or metadata tagging.

Overall, this dataset offers a compact yet realistic sample of financial and legal documents encountered in practice and is well suited for research on document classification, OCR robustness, and LLM-based document processing.
DOI: 10.17632/v3cs9mgmr2
title: 2D DIC Generator, 3D DIC Generator, and Speckle Pattern Scoring Python Code

description: Code used to generate the patterns for 2D and 3D DIC for laser-ablation printing as well as scoring the various patterns in question. The processes these scripts were used in is further elaborated in the article: "Optimization of Digital Image Correlation Patterning with Laser-Based Methods".
DOI: 10.17632/v3cs9mgmr2.2
title: 2D DIC Generator, 3D DIC Generator, and Speckle Pattern Scoring Python Code

description: Code used to generate the patterns for 2D and 3D DIC for laser-ablation printing as well as scoring the various patterns in question. The processes these scripts were used in is further elaborated in the article: "Optimization of Digital Image Correlation Patterning with Laser-Based Methods".
DOI: 10.17632/42ngksxpwt
title: Career metaphors from Australian geoscientists

description: This dataset is used in the following paper: Tiddy, C., Perera, S., Sardeshmukh, S. R. & Andrahannadi, U. (in review). Using metaphors to explore the influence of career perceptions on gender diversity in the Australian geosciences. Journal of Women and Minorities in Science and Engineering.

This dataset contains visual metaphors (e.g., an image, picture, movie, movie role, or cartoon character) provided by Australian geoscientists describing 1. their own career (Supplementary Data Table S1 - SELF perception metaphors) and 2. the career of the opposite gender (Supplementary Table S2 - OTHERS perception metaphors). Metaphors are used as they can give structure and provide organization to our perceptions of complex topics and have previously been successfully used to conceptualize career perceptions (e.g., Inkson, 2004; 2006; Mignot, 2004; Smith-Ruig, 2008).

Data was collected via semi-structured interviews of 68 (40 women, 28 men) Australian geoscientists from a range of career stages and with roughly equal representation from the sectors of academia, government, and industry (Table 4). Interviewees were selected through expert purposive sampling (Etikan et al., 2016) to ensure diversity of career paths, career stages and geoscience fields of expertise. A range of organizations of various sizes and types were targeted. Interviews were based on a set series of questions and probes aimed at exploring participants’ career from undergraduate studies to their current position. An additional 16 responses (14 women, 2 men) related to career metaphors were collected through an online questionnaire that included target questions with the same wording as used in interviews (see Table 4). The questionnaire was set up in association with a conference presentation, which provided an opportunity to collect additional responses to the specific metaphor aspect of the broader study. Audience members who had not been involved in an interview were invited to fill out a survey. The responses were filtered to include only Australian-based working geoscientists and have been used here to compliment the data collected in the interview process. Participants were recruited between 1st May 2019 and 31st May 2021.

In total, 94% of women and 80% of men provided metaphors for their own careers. 78% of women and 83% of men provided metaphors for the career of the opposite gender. 

It is noted that all participants in this study identified as either a cisgender man or cisgender woman, therefore the study only considers these two genders. Gender is used here in the context of gender identity (as opposed to biological sex). Most interview and all survey respondents identified as white/Caucasian.
DOI: 10.17632/42ngksxpwt.2
title: Career metaphors from Australian geoscientists

description: This dataset is used in the following paper: Tiddy, C., Perera, S., Sardeshmukh, S. R. & Andrahannadi, U. (in review). Using metaphors to explore the influence of career perceptions on gender diversity in the Australian geosciences. Journal of Women and Minorities in Science and Engineering.

This dataset contains visual metaphors (e.g., an image, picture, movie, movie role, or cartoon character) provided by Australian geoscientists describing 1. their own career (Supplementary Data Table S1 - SELF perception metaphors) and 2. the career of the opposite gender (Supplementary Table S2 - OTHERS perception metaphors). Metaphors are used as they can give structure and provide organization to our perceptions of complex topics and have previously been successfully used to conceptualize career perceptions (e.g., Inkson, 2004; 2006; Mignot, 2004; Smith-Ruig, 2008).

Data was collected via semi-structured interviews of 68 (40 women, 28 men) Australian geoscientists from a range of career stages and with roughly equal representation from the sectors of academia, government, and industry (Table 4). Interviewees were selected through expert purposive sampling (Etikan et al., 2016) to ensure diversity of career paths, career stages and geoscience fields of expertise. A range of organizations of various sizes and types were targeted. Interviews were based on a set series of questions and probes aimed at exploring participants’ career from undergraduate studies to their current position. An additional 16 responses (14 women, 2 men) related to career metaphors were collected through an online questionnaire that included target questions with the same wording as used in interviews (see Table 4). The questionnaire was set up in association with a conference presentation, which provided an opportunity to collect additional responses to the specific metaphor aspect of the broader study. Audience members who had not been involved in an interview were invited to fill out a survey. The responses were filtered to include only Australian-based working geoscientists and have been used here to compliment the data collected in the interview process. Participants were recruited between 1st May 2019 and 31st May 2021.

In total, 94% of women and 80% of men provided metaphors for their own careers. 78% of women and 83% of men provided metaphors for the career of the opposite gender. 

It is noted that all participants in this study identified as either a cisgender man or cisgender woman, therefore the study only considers these two genders. Gender is used here in the context of gender identity (as opposed to biological sex). Most interview and all survey respondents identified as white/Caucasian.