cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yt96x47dc7.1
title: RoseLeafVision: A Comprehensive Multi-Class Image Dataset for Accurate Rose Leaf Disease Detection

description: Description
Rose plant species are among the most widely used flowering plants, establishing themselves in the marketplace due to their aesthetic appeal, fragrance, as well as importance in the economy. They are also vulnerable to several diseases that can easily infect the leaves of the plants, reducing their appeal in the marketplace. In this regard, the RoseLeafVision dataset has been established.

Dataset Content
This dataset was collected from Golap Gram, Savar, Bangladesh, in July to September, 2025, where there are a total of 2,458 raw images of rose leaves collected in natural lighting conditions, although some were collected in a white background for clarity. The dataset has been categorized into five groups:
1. Black Spot: 335
2. Downy Mildew: 316
3. Dry Leaf: 712
4. Healthy Leaf: 668
5. Insect Hole: 427

After Augmentation 
The data was then augmented to a total of 12,991 pictures using rotation, shearing, zoom, flipping, as well as brightness and contrast changes. These augmentation techniques were necessary to ensure equal representation of the five classes, as the models were trained. This was in a bid to counter biased models in the classification strategies. The next step, Augmented
1. Black Spot: 2567
2. Downy Mildew: 2564
3. Dry Leaf: 2641
4. Healthy Leaf: 2634
5. Insect Hole: 2585 

Geographical Location 
All rose leaf images were collected from Golap Gram, Savar, Dhaka, a well-known rose cultivation zone in Bangladesh that naturally provides diverse leaf conditions.
Location: Golap Gram, Savar, Dhaka
Latitude: 23°48'5.808''N
Longitude: 90°19'30.4908''E

Preprocessing Steps 
1. Otsu Thresholding: separates the leaf region from the background.
2. Morphological Operations: This skips the noise and smooths the segmented leaf area.
3. Brightness Adjustment: Enhances the overall look of leaf, making images much more visible to understand.
4. CLAHE: Enhances the local contrast, more visible edge, emphasizing the disease patterns.
5. Normalization: It involves scaling pixel values to be compatible with a selected model.

Augmentation Details 
1. Rotation (±30°): Random rotation helps to make the model robust against different angle of leaf orientations.
2. Shear Transformation (0.15): It ensure slight angular distortion, allowing the model to handle shape variations.
3. Zoom (±20%): Random zoom-in and zoom-out enhance scale invariance.
4. Horizontal Flip: Flips the image left to right to increase variety in the dataset and reduce directional bias.
5. Vertical Flip: top-to-bottom flipping of images in order to enhance model generalization.
6. Fill Mode Nearest: fills no-value pixels appearing due to transformation with the nearest pixel.

Use of the Dataset
1. Perform development and benchmarking of the AI models on automated rose leaf disease detection.
2. IoT-based plant monitoring system to improve smart agriculture solutions.
3. Support research in precision agriculture field.

DOI: 10.17632/sdxfsjjsrw
title: Dataset for : Sustaining Our Public Services: Insights from a Quebec Municipal Outreach Tour on Sustainable Water Services Financing

description: Anonymized data for Sustaining Our Public Services: Insights from a Quebec Municipal Outreach Tour on Sustainable Water Services Financing by Justin Leroux and Nicolas Pinsonneault
DOI: 10.17632/sdxfsjjsrw.1
title: Dataset for : Sustaining Our Public Services: Insights from a Quebec Municipal Outreach Tour on Sustainable Water Services Financing

description: Anonymized data for Sustaining Our Public Services: Insights from a Quebec Municipal Outreach Tour on Sustainable Water Services Financing by Justin Leroux and Nicolas Pinsonneault
DOI: 10.17632/x8x39r6nmt
title: Ethernet Frame Physical-Layer Signal Dataset – 10BaseT

description: This dataset was developed to support research on network traffic classification using raw electrical signals captured at the physical layer. The core hypothesis behind this work is that different types of network traffic exhibit distinguishable patterns in their physical-layer waveforms due to variations in frame structure.

The dataset contains Ethernet frame signals corresponding to six widely used protocol types: DHCP, DNS, HTTP, ICMP, RTSP, and TLS. All data were collected in accordance with the 10Base-T Ethernet standard and are intended for research on signal-level network traffic classification.

To construct the raw signal dataset, protocol-specific packet capture (PCAP) files were collected from various sources. These files were then retransmitted over a 10Base-T Ethernet link, and the corresponding electrical signals were captured from the Ethernet cable using an oscilloscope. In total, 7421 unique signal samples (representing individual Ethernet frames) were extracted.

In addition to the raw signal files (provided in .csv format), the dataset also includes the original PCAP files used during acquisition.

Image Datasets (Visualization-Based):
In addition to the raw signal-level dataset, four separate image datasets were generated using different visualization techniques:
*vertical
*horizontal_zigzag
*spectrogram
*scalogram
Each visualization dataset contains 7,421 images, corresponding one-to-one with the raw signal files. These images were generated to support deep-learning-based classification experiments and are publicly shared together with the signal dataset.

MATLAB Script Packages:
Two MATLAB script packages are also provided to ensure full reproducibility:

1) Signal_Dataset_Generation_MATLAB_Scripts
Contains the scripts used for PCAP preprocessing, deduplication, packet-to-signal matching, automatic signal segmentation, and signal file labeling.

2) Image_Dataset_Generation_MATLAB_Scripts
Contains the scripts used to convert the raw 1-D signals into images using the four visualization techniques listed above.

Both script folders include a detailed script_information.txt file describing the purpose and functionality of each script.

Intended Use:
This dataset can be used for signal-based network traffic classification, physical-layer analysis, and deep learning research. It offers a novel perspective on traffic analysis beyond conventional packet- or flow-level features.
DOI: 10.17632/x8x39r6nmt.1
title: Ethernet Frame Physical-Layer Signal Dataset – 10BaseT

description: This dataset was developed to support research on network traffic classification using raw electrical signals captured at the physical layer. The core hypothesis behind this work is that different types of network traffic exhibit distinguishable patterns in their physical-layer waveforms due to variations in frame structure.

The dataset contains Ethernet frame signals corresponding to six widely used protocol types: DHCP, DNS, HTTP, ICMP, RTSP, and TLS. All data were collected in accordance with the 10Base-T Ethernet standard and are intended for research on signal-level network traffic classification.

To construct the raw signal dataset, protocol-specific packet capture (PCAP) files were collected from various sources. These files were then retransmitted over a 10Base-T Ethernet link, and the corresponding electrical signals were captured from the Ethernet cable using an oscilloscope. In total, 7421 unique signal samples (representing individual Ethernet frames) were extracted.

In addition to the raw signal files (provided in .csv format), the dataset also includes the original PCAP files used during acquisition.

Image Datasets (Visualization-Based):
In addition to the raw signal-level dataset, four separate image datasets were generated using different visualization techniques:
*vertical
*horizontal_zigzag
*spectrogram
*scalogram
Each visualization dataset contains 7,421 images, corresponding one-to-one with the raw signal files. These images were generated to support deep-learning-based classification experiments and are publicly shared together with the signal dataset.

MATLAB Script Packages:
Two MATLAB script packages are also provided to ensure full reproducibility:

1) Signal_Dataset_Generation_MATLAB_Scripts
Contains the scripts used for PCAP preprocessing, deduplication, packet-to-signal matching, automatic signal segmentation, and signal file labeling.

2) Image_Dataset_Generation_MATLAB_Scripts
Contains the scripts used to convert the raw 1-D signals into images using the four visualization techniques listed above.

Both script folders include a detailed script_information.txt file describing the purpose and functionality of each script.

Intended Use:
This dataset can be used for signal-based network traffic classification, physical-layer analysis, and deep learning research. It offers a novel perspective on traffic analysis beyond conventional packet- or flow-level features.
DOI: 10.17632/dsfz9jchmp
title: What Drives Consumers? Influence of Promotional Channels on OTC Medical Device Purchases

description: This dataset contains survey-based research data used in the study titled “What Drives Consumers? Influence of Promotional Channels on OTC Medical Device Purchases.” The research examines consumer trust, awareness, and decision-making patterns across different promotional channels such as doctor/pharmacist recommendations, peer suggestions, online advertisements, social media influencers, and in-store promotions. The data includes anonymized responses from 161 participants and covers demographic information, device usage patterns, and Likert-scale trust ratings. This dataset supports insights into consumer behaviour, healthcare marketing strategy, and OTC medical device adoption in the Indian context.
DOI: 10.17632/dsfz9jchmp.1
title: What Drives Consumers? Influence of Promotional Channels on OTC Medical Device Purchases

description: This dataset contains survey-based research data used in the study titled “What Drives Consumers? Influence of Promotional Channels on OTC Medical Device Purchases.” The research examines consumer trust, awareness, and decision-making patterns across different promotional channels such as doctor/pharmacist recommendations, peer suggestions, online advertisements, social media influencers, and in-store promotions. The data includes anonymized responses from 161 participants and covers demographic information, device usage patterns, and Likert-scale trust ratings. This dataset supports insights into consumer behaviour, healthcare marketing strategy, and OTC medical device adoption in the Indian context.
DOI: 10.17632/ty679nzgfx
title: Supplementary Data for "A novel experimental V-Sc olivine-melt oxybarometer for arc magmas"

description: This supplementary dataset contains all supporting materials associated with the manuscript, including analytical data, figures, quality assessments, and Python code used for data analysis and modeling. It also serves as the data repository for all tables and figures presented in the main manuscript.
The contents are organized as follows:
1. Cacciatore_et_al_GCA_Raw_data_repository.xlsx: This file contains the same data as presented in the tables of the main manuscript.
2. S1_Supplementary_Tables: Excel spreadsheets providing a comprehensive summary of the experimental run conditions and trace element doping information. This section includes complete, processed data from LA-ICP-MS and EPMA analyses, along with quality control metrics for the measurements. It also contains the inputs and results of mass balance calculations, as well as comparisons with QEMSCAN analyses for selected samples. Additionally, this section includes numerical datasets used in statistical modeling (e.g., correlation matrices, fitting statistics), coding inputs, and the full dataset underlying the tables presented in the main manuscript.
3. S2_Supplement_Quality_of_Experimental_Approach: A document providing detailed information on the quality and reproducibility of the experimental procedures used in the study.
4. S3_Supplementary_Figures: Figures illustrating the precision and accuracy of EPMA and LA-ICP-MS measurements, along with graphical outputs of the statistical modeling. This section includes figures referenced in both the main manuscript and supplementary material.
5. S4_Supplement_AI_Generated_Python_Codes: Python scripts (AI-generated) used for data processing and statistical analysis, which produced the regression models presented as Equations 5 and 6 in the main manuscript.
DOI: 10.17632/ty679nzgfx.1
title: Supplementary Data for "A novel experimental V-Sc olivine-melt oxybarometer for arc magmas"

description: This supplementary dataset contains all supporting materials associated with the manuscript, including analytical data, figures, quality assessments, and Python code used for data analysis and modeling. It also serves as the data repository for all tables and figures presented in the main manuscript.
The contents are organized as follows:
1. Cacciatore_et_al_GCA_Raw_data_repository.xlsx: This file contains the same data as presented in the tables of the main manuscript.
2. S1_Supplementary_Tables: Excel spreadsheets providing a comprehensive summary of the experimental run conditions and trace element doping information. This section includes complete, processed data from LA-ICP-MS and EPMA analyses, along with quality control metrics for the measurements. It also contains the inputs and results of mass balance calculations, as well as comparisons with QEMSCAN analyses for selected samples. Additionally, this section includes numerical datasets used in statistical modeling (e.g., correlation matrices, fitting statistics), coding inputs, and the full dataset underlying the tables presented in the main manuscript.
3. S2_Supplement_Quality_of_Experimental_Approach: A document providing detailed information on the quality and reproducibility of the experimental procedures used in the study.
4. S3_Supplementary_Figures: Figures illustrating the precision and accuracy of EPMA and LA-ICP-MS measurements, along with graphical outputs of the statistical modeling. This section includes figures referenced in both the main manuscript and supplementary material.
5. S4_Supplement_AI_Generated_Python_Codes: Python scripts (AI-generated) used for data processing and statistical analysis, which produced the regression models presented as Equations 5 and 6 in the main manuscript.
DOI: 10.17632/ykrfthhzgv
title: AI-Driven Emissions Modeling: A Blueprint for Iraq's Oil and Gas Sector

description: This repository contains the Python source code and training data used to validate the Random Forest emissions model presented in the manuscript "AI-Driven Emissions Modeling: A Blueprint for Iraq's Oil and Gas Sector".
DOI: 10.17632/ykrfthhzgv.2
title: AI-Driven Emissions Modeling: A Blueprint for Iraq's Oil and Gas Sector

description: This repository contains the Python source code and training data used to validate the Random Forest emissions model presented in the manuscript "AI-Driven Emissions Modeling: A Blueprint for Iraq's Oil and Gas Sector".
DOI: 10.17632/wrs5sx4bhb
title: Organize raw data according to the images in the article

description: Gallium nitrate, a non-redox analog of iron (III), suppresses bacterial biofilms and virulence within the framework of bacterial regulation. This study investigates the molecular mechanisms and regulatory pathways through which gallium nitrate modulates bacterial activity and function.
DOI: 10.17632/wrs5sx4bhb.2
title: Organize raw data according to the images in the article

description: Gallium nitrate, a non-redox analog of iron (III), suppresses bacterial biofilms and virulence within the framework of bacterial regulation. This study investigates the molecular mechanisms and regulatory pathways through which gallium nitrate modulates bacterial activity and function.
DOI: 10.17632/khs2rx6f8w
title: Cellulase catalysis on cell surfaces using Caulobacter S-layer display. Davenport et al.

description: All data associated with this manuscript. It can be inputted to the .Rmd file associated with this manuscript (see related link to zenodo) to produce all figures and supplementary figures throughout the paper.
DOI: 10.17632/khs2rx6f8w.1
title: Cellulase catalysis on cell surfaces using Caulobacter S-layer display. Davenport et al.

description: All data associated with this manuscript. It can be inputted to the .Rmd file associated with this manuscript (see related link to zenodo) to produce all figures and supplementary figures throughout the paper.
DOI: 10.15468/dl.2c4fqj
title: Occurrence Download

description: A dataset containing 1447222 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Anser caerulescens"
 ]
}

 The dataset includes 1447222 records from 337 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005642-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pzw9q7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Opuntia pusilla"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005687-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/w7w5ywkjtd
title: Replication_Data_and_Code_for_Career_Preferences_and_Socioeconomic_Background

description: Replication data and code for „Career Preferences and Socio-Economic Background”, Labour Economics, by Paul Schüle. See the README for further instructions.
DOI: 10.17632/w7w5ywkjtd.1
title: Replication_Data_and_Code_for_Career_Preferences_and_Socioeconomic_Background

description: Replication data and code for „Career Preferences and Socio-Economic Background”, Labour Economics, by Paul Schüle. See the README for further instructions.
DOI: 10.15468/dl.bdwvc7
title: Occurrence Download

description: A dataset containing 144429933 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Material sample)",
 "Continent is Europe",
 "CoordinateUncertaintyInMeters 0.0-20100.0",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC-BY 4.0, CC-BY-NC 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta",
 "Year 1980-2025"
 ]
}

 The dataset includes 144429933 records from 6400 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005660-251120083545085/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.25106006
title: 4S+1P, 4S tremor detections

description: Catalog of 15,986 southern Vancouver Island tremor detections (4S) or 4,851 (4S+1P) in hypoDD .event format: YEAR MONTH DAY HOUR MINUTE SECOND LAT LON DEPTH MAGNITUDE 0 0 0 EVENTID The "4S" data were generated using four S-wave windows from stations PGC, SILB, SSIB, and KELB/KLNB, and the "4P" data were generated using an additional station, SNB, for the P-wave. The Olympic test LFE locations are given in olympic_test.lfe. This file includes the LFE templates within 13 km of the JDF-2 profile, with the columns given as: X-coord along profile, Depth, Projection distance, CDP number, Lat, Lon
DOI: 10.25345/c54q7r303
title: MassIVE MSV000097934 - Multiomics of cockroaches infected with Salmonella Typhimurium identifies molecular signatures of vector borne transmission
title: German cockroaches (Blattella germanica) are prevalent indoor pests that have long been associated with the spread of enteric human pathogens. Recent work investigating the relationship between these insects and Salmonella enterica serovar Typhimurium (S. Typhimurium), a model pathogen of global concern, demonstrated that S. Typhimurium colonizes the cockroach gut. S Typhimurium has a broad host range, but mechanistic molecular insight into how ecologically relevant invertebrate hosts interact with this pathogen is lacking. Here, we applied a multiomic (transcriptomic and proteomic) approach to infected cockroaches to examine the molecular variables that govern cockroach-borne S. Typhimurium transmission. Our results reveal enriched monocarboxylic acid transport and metabolism, increased long-chain fatty acid transport, increased triglyceride metabolism, and an increased response to reactive oxygen species and free radicals as host signatures of a metabolic shift in the cockroach gut during infection. Surprisingly, downregulation of the immune deficiency (IMD) pathway transcription factor Relish, and upregulation of xenobiotic detoxification (glutathione-s-transferase) and known allergens (Blag5 and Blag8, myosin, tropomyosin) were also evident in infected guts. To our knowledge, this study is both the first omics study of enteric human pathogen infection in a cockroach vector and the first omics study of S. Typhimurium in an ecologically relevant insect host, representing a seminal contribution to the field of vector-borne infectious disease. This work provides novel fundamental knowledge regarding the response of insect hosts to S. Typhimurium infection and the evolution of vector-pathogen relationships with the potential to inform mitigation of the public health impacts of cockroaches.
DOI: 10.13133/uniroma1/rnhhbz/b7nh4q
title: al-Fajr_1-14_1990_AmnestyInternationalConcernOverTheSituationOfFreedomsInTunisia_OCR.pdf

description: :unav
DOI: 10.5281/zenodo.18348452
title: Supplementary data for the paper under review "Planning-Based User Simulation for Controllable Evaluation of Conversational Search Systems" at SIGIR'26.
DOI: 10.5281/zenodo.18348451
title: Supplementary data for the paper under review "Planning-Based User Simulation for Controllable Evaluation of Conversational Search Systems" at SIGIR'26.
DOI: 10.15468/dl.s6v5j9
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Astomaea seselifolia",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006950-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18347913
title: Example interaction with GPT 5.2

description: Illustrative interaction with GPT 5.2 in Cursor for discussion as part of the astro AI term ath the Institute for Advanced Study.
DOI: 10.5281/zenodo.18347912
title: Example interaction with GPT 5.2

description: Illustrative interaction with GPT 5.2 in Cursor for discussion as part of the astro AI term ath the Institute for Advanced Study.
DOI: 10.3535/e2f-l52-ajj
title: Nassarius boucheti H. H. Kool, 2004

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/gqg-jxk-9gk
title: Reticunassa jeanmartini (H. H. Kool & H. Dekker, 2006)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/9ql-mps-v10
title: Nassarius biendongensis H. H. Kool, 2003

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/07q-x1a-35l
title: Albinaria corrugata corrugata (Bruguière, 1792)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/qys-wy6-jh0
title: Albinaria olivieri olivieri (J. R. Roth, 1839)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/09b-snj-bp6
title: Albinaria corrugata inflata (Olivier, 1801)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/nhy-951-l27
title: Lepidochitona caprearum (Scacchi, 1836)

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.