This dataset was generated using Gemini 2.0 Flash to be used as an extra validation of submissions to the Drawing with LLMs competition

The source is a kaggle notebook created as Capstone project for the Gen AI Intensive Course 2025 Q1

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Files used for self-verification of GEMINI program. https://github.com/mattzett/gemini The version 1.0.0 files were not correct due to an error in old code.

Files used for self-verification of GEMINI program.

https://github.com/gemini3d/gemini

v2.2.1 reflects the v2 sign error correction, including Glow

Title: CAMELS Simulation Suite Galaxy Photometry Dataset

Overview: The dataset consists of synthetic photometric data generated from the CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) project, which utilizes a suite of cosmological hydrodynamic simulations. The dataset includes over 200 million simulated galaxies, providing a rich resource for studying the relationships between cosmological and astrophysical parameters and observable galaxy properties.

Key Components:

Simulations:

The dataset is derived from multiple galaxy formation models, including: Swift-EAGLE Illustris-TNG Simba Astrid Each simulation employs different subgrid models and numerical methods, leading to diverse galaxy properties and behaviors. Photometric Data:

Synthetic photometry is generated across various bands, including: SDSS ugriz (u, g, r, i, z bands) GALEX FUV & NUV (far-ultraviolet and near-ultraviolet) The dataset includes both intrinsic (unattenuated) and dust-attenuated luminosity functions, allowing for the analysis of how dust affects galaxy observations. Galaxy Properties:

The dataset contains information on various galaxy properties, including: Luminosity functions (LFs) at different wavelengths Color distributions (e.g., FUV-NUV, g-r) Mass-to-light ratios (ML ratios) Redshift evolution of galaxy populations Cosmological and Astrophysical Parameters:

The dataset is designed to explore the impact of varying cosmological parameters (e.g., matter density Ωm, power spectrum normalization σ8) and astrophysical parameters related to feedback processes (e.g., supernova feedback parameters ASN1, ASN2). Data Availability:

The synthetic photometric catalogs are publicly available, facilitating further research and model comparison. This accessibility encourages the scientific community to utilize the dataset for various analyses, including parameter inference and model validation. Applications:

The dataset is intended for use in studies that aim to: Constrain cosmological and astrophysical parameters using galaxy photometry. Investigate the relationships between galaxy properties and underlying cosmological models. Enhance understanding of galaxy formation and evolution through simulation-based approaches. Conclusion This dataset represents a significant advancement in the field of cosmology and astrophysics, providing a comprehensive resource for researchers interested in the interplay between theoretical models and observational data. By leveraging the CAMELS simulation suite, the dataset enables detailed investigations into the factors influencing galaxy properties and the broader implications for our understanding of the universe.

Files used for self-verification of GEMINI program.

https://github.com/gemini3d/gemini

v4.1.0 update test2dew_{fang,glow} with permuted Gemini 3D >= 0.10.0 axes order. Previously we didn't swap, but realized for milestones it is necessary.

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Replication Package

This replication package contains the necessary tools, data, and scripts for reproducing the results of our paper: "Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot". Below is a detailed description of the directory structure and the contents of this package.

Contents

The replication package is organized into two main directories:

• assets: This directory contains all .csv files used as input for the script and the outputted .csv file used to perform the manual and automated analyses for RQ1 and RQ2.

• script: This directory contains all scripts for RQ1 and RQ2.

In the following, we describe the content of each directory:

assets

This directory contains the tools and resources required for our study.

dataset: Contains the main datasets used in the study.

• annotationStore.csv: Input dataset for our analyses, originating from the CODESEARCHNET dataset.

• queries.csv: .csv file containing the queries used for the experiments filtered from the CODESEARCHNETdataset. This file contains the following columns:

  • Language: Programming language of the query
  • Query: Query used for the experiment
  • GitHubUrl: GitHub URL related to a snippet that addresses the query
  • Relevance: Relevance of the linked GitHub snippet to the query

data: Contains the datasets and results of all analyses.

• queries.csv: General input queries. This file contains the following columns:

  • Language: Programming language of the query
  • Query: Query used for the snippet generation
  • Prompt: LLM prompt generated for the query as: You are a Senior developer. Then give me a code snippet about:

• queries_filled.csv: Similar to the previous file, but also containing the output produced by the LLM-based assistants. This file contains the following columns:

  • Language: Programming language of the query
  • Query: Query used for the snippet generation
  • Prompt: LLM prompt generated for the query as: You are a Senior developer. Then give me a code snippet about:
  • Notes: General notes that provide additional context or information about the query or prompt.
  • Gemini_Answer(n): The generated code snippets by Gemini.
  • Gemini(n): The external links provided by Gemini.
  • Prompt (repeated)
  • Note: Notes that provide additional context or information about the query or prompt.
  • Copilot_Answer(n): The generated code snippets by Bing-Copilot.
  • Copilot_Bing(n): The external links provided by Bing-Copilot.

copilot || gemini: Contains the data related to the specific LLM. These two subdirectories have the same internal structure.

• queries.csv: The queries_filled.csv file, filtered for the specific LLM.
• queries_noTrivial.csv: Contains only the queries with at least one nontrivial generated snippet.

• external_links.csv: External links extracted from the LLMs output.

• external_links_filled.csv: Snippets extracted from the external links.

  • index: Query ID
  • source: Snippet ID
  • url: Link URL
  • note: Notes that provide additional context or information about the query or prompt
  • code(n): The n-th code snippet extracted from the source

manual_analysis: Manual analysis results.

• manual_analysis.csv:
  • index: Query ID
  • query: Query used for the snippet generation
  • generatedsnippet(n): The n-th code snippet generated by the LLM-based assistant
  • trivial_1: Manual analysis of whether or not the snippet was trivial (validator 1)
  • trivial_2: Manual analysis of whether or not the snippet was trivial (validator 2)
  • trivial_final: Manual analysis of whether or not the snippet was trivial (final classification if there is a disagreement)
  • source: URL to analyze
  • sourcetype1: Type of the source (validator 1)
  • sourcetype2: Type of the source (validator 2)
  • sourcetypefinal: Type of the source (final classification if there is a disagreement)
  • relatedtoquery_1: Relevance of the link to the query (validator 1)
  • relatedtoquery_2: Relevance of the link to the query (validator 2)
  • relatedtoquery_final: Relevance of the link to the query (final classification if there is a disagreement)
  • relatedtosnippets_1: Relevance of the generated snippet to those in the link (validator 1)
  • relatedtosnippets_2: Relevance of the generated snippet to those in the link (validator 2)
  • relatedtosnippets_final: Relevance of the generated snippet to those in the link (final classification if there is a disagreement)
• manual_analysis_noTrivial.csv: As in the previous file, but only the queries with at least one nontrivial generated code snippet.

clone_detector: Output and intermediate files for clone detection with Copilot data.

• copilot_tokens || gemini_tokens: Contains the output the tokenization of the generated code snippets and the code snippets extracted from the external links.
• merged_llm_ext_link.csv: All possible pairs (Cartesian product) (code snippet extracted from the external links, generated code snippet). This file is the input of the clone detection tool.
  • ID_query: Query ID
  • query: Query used for the snippet generation
  • language: Programming language of the query
  • generated_snippet: The generated code snippet by the LLM-based assistant
  • IDgensnippet: The index of the generated code snippet
  • LOCgensnippet: The number of lines of code of the generated code snippet
  • ID_source: Source ID
  • source: Source URL
  • source_snippet: Code snippet extracted from the source
  • IDsourcesnippet: ID of the code snippet extracted from the source
  • LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source
  • note: Notes that provide additional context or information about the query or prompt
• clone_detection_output.csv: Contains the clone detection results.
  • ID_query: The index of the query
  • query: Query used for the snippet generation
  • language: The programming language of the query
  • generated_snippet: The generated code snippet by the LLM-based assistant
  • IDgensnippet: The index of the generated code snippet
  • LOCgensnippet: The number of lines of code of the generated code snippet
  • ID_source: Source ID
  • source: Source URL
  • source_snippet: Code snippet extracted from the source
  • IDsourcesnippet: ID of the code snippet extracted from the source
  • LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source
  • note: Notes that provide additional context or information about the query or prompt
  • clone_detected: bBolean value that indicates whether a clone has been detected (1 = detected, 0 = not detected)
  • cloning_ratio: Ratio of the number of lines of code of the generated code snippet has been detected as a clone in the code snippet extracted from the source
  • cloned_lines: The number of lines of code of the generated code snippet that has been detected as a clone in the code snippet extracted from the source

cosine_sim: Cosine similarity results.

• cosine_sim_output.csv: Contains the cosine similarity results
  • query_id: Query ID
  • snippet_id:ID the generated code snippet
  • source_id: ID of the source
  • sourcesnippetid: ID of the code snippet extracted from the source
    • cosine_similarity: The cosine similarity between the generated code snippet and the code snippet extracted from the source

quant_analysis: Quantitative analysis results.

• topN_links_se.csv: Contains the top-N links extracted from the search engine.
  • id: Query ID
  • query: The query
  • url: Link URL

• merged_clone_cosine.csv: Contains the merged results of the clone detection and cosine similarity.
  • ID_query: Query ID
  • query: The query
  • language: The programming language of the query
  • generated_snippet: The generated code snippet by the LLM-based assistant
  • IDgensnippet: The ID of the generated code

🏥 Medical Reports Simplification Dataset

  📋 Description

Dataset créé avec Gemini 2.5 Pro (Preview) pour entraîner des modèles à simplifier les rapports médicaux complexes en explications compréhensibles pour les patients. 🎯 Objectif : Démocratiser l'accès à l'information médicale en rendant les rapports techniques accessibles au grand public.

  🔧 Génération du Dataset

Génération : Gemini 2.5 Pro (Preview) Validation : Contrôle qualité automatisé… See the full description on the dataset page: https://huggingface.co/datasets/Sadou/medical-reports-simplification-dataset.

廣東話草泥馬

  Dataset Card for Cantonese Alpaca

repository: (https://github.com/hon9kon9ize/yue-alpaca)

  Dataset Description

This dataset contains Cantonese Instruction-Following generated by Gemini Pro using Stanford's Alpaca prompts for fine-tuning LLMs. Attention: This dataset is generated by Gemini Pro and has not undergone rigorous verification. The content may contain errors. Please keep this in mind when using it.

  Licensing Information

The… See the full description on the dataset page: https://huggingface.co/datasets/hon9kon9ize/yue-alpaca.

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This dataset contains manually verified aspect-based sentiment annotations for tourism-related Google Reviews in Central Java, Indonesia. It covers 10 popular tourist destinations and includes multilingual content (Indonesian and English). Each review is segmented and labeled based on the 3A tourism framework: Amenity, Attraction, and Accessibility, with sentiment polarities classified as Positive, Neutral, or Negative.

The annotation process combines rule-based aspect detection with LLM-enhanced verification. Specifically, this dataset was labeled using a hybrid approach that integrates a custom keyword dictionary and syntactic rules, refined through Gemini 2 (Google’s LLM) for aspect and sentiment validation. This approach improves multilingual ABSA accuracy and consistency, especially in noisy user-generated content.

### Dataset Structure:
- `review`: Raw user review text
- `aspect`: One of the 3A categories (Amenity, Attraction, Accessibility)
- `sub_aspect`: Fine-grained subcategory under each aspect
- `sentiment`: Sentiment polarity (Positive, Neutral, Negative)
- `language`: Language of the review (id = Indonesian, en = English)
- `source`: Name of the tourist destination

### Destinations Included:
1. Borobudur
2. Kota Lama Semarang
3. Lawang Sewu
4. Pantai Marina
5. Dusun Semilir
6. Prambanan
7. Owabong
8. Pantai Jatimalang
9. Masjid Agung Demak
10. Sunan Kalijaga

### Applications:
This dataset is part of a doctoral research project on developing a multilingual tourism recommender system using Aspect-Based Sentiment Analysis (ABSA) and Decision Support Systems (DSS). It supports further research in:
- Sentiment analysis
- Recommender systems
- Multilingual NLP
- Tourism informatics
- LLM-based content analysis

### Format:
- CSV files (one per destination, labeled and cleaned)
- UTF-8 encoding
- Compatible with Python (pandas, transformers, HuggingFace Datasets)

### License:
CC-BY 4.0

### Citation:
Please cite this dataset using the DOI provided by Zenodo upon publication.

Two new imaging instruments, ‘Alopeke and Zorro, were designed, built, and commissioned at the Gemini-North and Gemini-South telescopes in 2018 and 2019, respectively. Here we describe them and present the results from over a year of operation. The two identical instruments are based on the legacy of the DSSI (Differential Speckle Survey Instrument) instrument, successfully used for years at the WIYN and the Gemini telescopes in Hawaii and Chile. ‘Alopeke and Zorro are dual-channel imagers having both speckle (6.7″) and “wide-field” (∼1 arcminute) field-of-view options. They were built to primarily perform speckle interferometry providing diffraction-limited imagery at optical wavebands, yielding pixel scale uncertainties of ±0.21 mas, position angle uncertainties of ±0.7◦, and photometric uncertainties of Δm ± 0.02–0.04 magnitudes (for the blue and red channels, respectively) when run through the standard data reduction pipeline. One of their main scientific roles is the validation and characterization of exoplanets and their host stars as discovered by transit surveys such as the NASA Kepler, K2, and TESS missions. The limiting magnitude for speckle observations at Gemini can be quite faint (r ∼18 in good observing conditions) but typically the observed targets are brighter. The instruments can also function as conventional CCD imagers providing a 1 arc-minute field of view and allowing simultaneous two-color, high-speed time-series operation. These resident visitor instruments are remotely operable and are available for use by the community via the peer-reviewed proposal process.

A dataset of 1331 healthy adults used for training and validation of the Gemini-GAN model for joint 3D super-resolution and segmentation of cardiac images.

The multimodal model market is experiencing explosive growth, projected to reach $863 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 52%. This rapid expansion is fueled by several key factors. Firstly, advancements in artificial intelligence (AI) and deep learning are enabling the creation of increasingly sophisticated models capable of processing and integrating information from diverse data sources like text, images, audio, and video. This capability is driving adoption across various sectors. The medical field leverages multimodal models for improved diagnostics and personalized treatment, while finance utilizes them for enhanced fraud detection and risk assessment. E-commerce and retail benefit from improved product recommendations and customer service, and the entertainment industry sees applications in advanced content creation and personalized experiences. Furthermore, the emergence of new multimodal model architectures like transformers and the increasing availability of large, diverse datasets are accelerating innovation and market expansion. Competition is fierce, with established tech giants like OpenAI, Google (with Gemini), and Meta vying for market dominance alongside innovative startups like Twelve Labs and Pika. The market segmentation reveals significant opportunities within specific application areas. Medical applications are poised for substantial growth due to the potential for improving healthcare outcomes. Similarly, the finance sector's increasing reliance on AI for risk management and fraud prevention is driving strong demand. The retail and e-commerce segments are witnessing increasing adoption as businesses seek to enhance customer experiences and operational efficiency. While the current focus is on applications, the underlying technologies—multimodal representation, translation, alignment, fusion, and co-learning—represent distinct areas of ongoing development that will continue to fuel market growth. Geographic distribution shows a strong concentration in North America and Europe initially, but rapid growth is anticipated in the Asia-Pacific region, particularly in China and India, due to increasing technological investment and data availability. However, challenges remain, such as data privacy concerns, the high computational cost of training these models, and the need for robust validation and regulatory frameworks, which will influence the pace of market growth in the long term.

Gemini is a trusted and regulated cryptocurrency exchange and wallet provider known for its strong security and user-friendly features. If you’re experiencing issues accessing your wallet, recovering assets, or navigating the app, professional support is available to assist you. Contacting Gemini’s dedicated support team can help resolve concerns such as login problems, transaction errors, or account verification delays.

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Dataset Description

Dataset Summary This dataset contains question-answer pairs focused on quantitative finance, covering topics such as option pricing, stochastic calculus (Brownian motion, Itô's Lemma), probability theory, and financial modeling assumptions. Each instance includes a question, a detailed ground-truth solution (often resembling textbook explanations or interview answers), a multi-step reasoning trace generated by a Gemini Pro model, and a structured validation of… See the full description on the dataset page: https://huggingface.co/datasets/VedantPadwal/quantitative-finance-reasoning.

Description:

Downsized (256x256) camera trap images used for the analyses in "Can CNN-based species classification generalise across variation in habitat within a camera trap survey?", and the dataset composition for each analysis. Note that images tagged as 'human' have been removed from this dataset. Full-size images for the BorneoCam dataset will be made available at LILA.science. The full SAFE camera trap dataset metadata is available at DOI: 10.5281/zenodo.6627707.

Project: This dataset was collected as part of the following SAFE research project: Machine learning and image recognition to monitor spatio-temporal changes in the behaviour and dynamics of species interactions

Funding: These data were collected as part of research funded by:

• NERC (NERC QMEE CDT Studentship, NE/P012345/1, http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FP012345%2F1&cookieConsent=A)

This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

XML metadata: GEMINI compliant metadata for this dataset is available here

Files: This dataset consists of 3 files: CT_image_data_info2.xlsx, DN_256x256_image_files.zip, DN_generalisability_code.zip

CT_image_data_info2.xlsx

This file contains dataset metadata and 1 data tables:

1. Dataset Images (described in worksheet Dataset_images)

   Description: This worksheet details the composition of each dataset used in the analyses

   Number of fields: 69

   Number of data rows: 270287

   Fields:

   • filename: Root ID (Field type: id)
   • camera_trap_site: Site ID for the camera trap location (Field type: location)
   • taxon: Taxon recorded by camera trap (Field type: taxa)
   • dist_level: Level of disturbance at site (Field type: ordered categorical)
   • baseline: Label as to whether image is included in the baseline training, validation (val) or test set, or not included (NA) (Field type: categorical)
   • increased_cap: Label as to whether image is included in the 'increased cap' training, validation (val) or test set, or not included (NA) (Field type: categorical)
   • dist_individ_event_level: Label as to whether image is included in the 'individual disturbance level datasets split at event level' training, validation (val) or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_1: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 1' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_2: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 2' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 3' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 4' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance level 5' training or test set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_2: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 2 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 3 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 4 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_1_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_2_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 3 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_2_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 4 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_2_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 4 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_pair_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 4 and 5 (pair)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_2_3: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 3 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_2_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 4 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_2_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_1_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_2_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 4 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_2_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 3 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_2_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 2, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_triple_3_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 3, 4 and 5 (triple)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_quad_1_2_3_4: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 4 (quad)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_quad_1_2_3_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 3 and 5 (quad)' training set, or not included (NA) (Field type: categorical)
   • dist_combined_event_level_quad_1_2_4_5: Label as to whether image is included in the 'disturbance level combination analysis split at event level: disturbance levels 1, 2, 4 and 5 (quad)' training set, or not included (NA) (Field type:

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