Dataset used in the article entitled 'Synthetic Datasets Generator for Testing Information Visualization and Machine Learning Techniques and Tools'. These datasets can be used to test several characteristics in machine learning and data processing algorithms.

Sample size calculation per Cochrane review group; random review # generator (used to help pick reviews at random)

Credit Card Generator Market Outlook

The global credit card generator market is projected to experience robust growth with a market size of approximately USD 580 million in 2023, and it is anticipated to reach USD 1.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.5%. The rising need for secure and efficient credit card testing tools, driven by the expansion of e-commerce and digital transactions, forms a significant growth catalyst for this market. As online retail and digital financial services burgeon, the demand for reliable credit card generators continues to escalate, underscoring the importance of this market segment.

One of the pivotal growth drivers for the credit card generator market is the increasing complexity and sophistication of online payment systems. As e-commerce platforms and digital payment solutions proliferate worldwide, there is a growing need for comprehensive testing tools to ensure the reliability and security of these systems. Credit card generators play a crucial role in this context by providing developers and testers with the means to simulate various credit card scenarios, thereby enhancing the robustness of payment processing systems. Additionally, the rise in cyber threats and fraud necessitates stringent testing, further propelling market growth.

Another significant factor contributing to the market's expansion is the growing emphasis on fraud prevention and security. Financial institutions and businesses are increasingly investing in sophisticated tools to combat fraud and secure financial transactions. Credit card generators offer a practical solution for testing the efficacy of anti-fraud measures and ensuring that security protocols are adequately robust. By enabling the simulation of fraudulent activities and various transaction scenarios, these tools help organizations better prepare for and mitigate potential security breaches.

Furthermore, the marketing and promotional applications of credit card generators are also driving market growth. Companies leveraging digital marketing strategies use these tools to create dummy credit card numbers for various promotional activities, such as offering free trials or discounts, without exposing real customer data. This capability not only aids in marketing efforts but also ensures compliance with data privacy regulations, thereby enhancing consumer trust and brand reputation. The versatility of credit card generators in supporting both operational and marketing functions underscores their growing importance in the digital age.

Regionally, North America holds a significant share of the credit card generator market, driven by the high penetration of digital payment systems and advanced cybersecurity measures in the region. The presence of numerous financial institutions and technology companies further bolsters the market in North America. Meanwhile, Asia Pacific is expected to witness the fastest growth, fueled by the rapid digitalization of economies, increasing internet penetration, and burgeoning e-commerce activities. Europe also presents substantial opportunities due to stringent data protection regulations and the widespread adoption of digital transaction systems.

Type Analysis

The credit card generator market can be segmented by type into software and online services. Software-based credit card generators are widely used by developers and testers within organizations to simulate credit card transactions and validate payment processing systems. These tools are typically integrated into the development and testing environments, providing a controlled and secure platform for generating valid credit card numbers. The demand for software-based generators is driven by their ability to offer customizable options and advanced features, such as bulk generation and API integration, which enhance the efficiency of testing processes.

Online services, on the other hand, cater to a broader audience, including individual users, small businesses, and marketers. These services are accessible via web platforms and provide an easy-to-use interface for generating credit card numbers for various purposes, such as testing, fraud prevention, and marketing promotions. The growing popularity of online credit card generators can be attributed to their convenience, accessibility, and the increasing need for temporary and disposable credit card numbers in the digital economy. These services are particularly useful for busin

Comprehensive dataset of 3,449 Electric generator shops in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

Quantum-AI Synthetic Data Generator Market Outlook

According to our latest research, the global Quantum-AI Synthetic Data Generator market size reached USD 1.98 billion in 2024, reflecting robust momentum driven by the convergence of quantum computing and artificial intelligence technologies in data generation. The market is experiencing a significant compound annual growth rate (CAGR) of 32.1% from 2025 to 2033. At this pace, the market is forecasted to reach USD 24.8 billion by 2033. This remarkable growth is propelled by the escalating demand for high-quality synthetic data across industries to enhance AI model training, ensure data privacy, and overcome data scarcity challenges.

One of the primary growth drivers for the Quantum-AI Synthetic Data Generator market is the increasing reliance on advanced machine learning and deep learning models that require vast amounts of diverse, high-fidelity data. Traditional data sources often fall short in volume, variety, and compliance with privacy regulations. Quantum-AI synthetic data generators address these challenges by producing realistic, representative datasets that mimic real-world scenarios without exposing sensitive information. This capability is particularly crucial in regulated sectors such as healthcare and finance, where data privacy and security are paramount. As organizations seek to accelerate AI adoption while minimizing ethical and legal risks, the demand for sophisticated synthetic data solutions continues to rise.

Another significant factor fueling market expansion is the rapid evolution of quantum computing and its integration with AI algorithms. Quantum computing’s superior processing power enables the generation of complex, large-scale datasets at unprecedented speeds and accuracy. This synergy allows enterprises to simulate intricate data patterns and rare events that would be difficult or impossible to capture through conventional means. Additionally, the proliferation of AI-driven applications in sectors like autonomous vehicles, predictive maintenance, and personalized medicine is amplifying the need for synthetic data generators that can support advanced analytics and model validation. The ongoing advancements in quantum hardware, coupled with the growing ecosystem of AI tools, are expected to further catalyze innovation and adoption in this market.

Moreover, the shift toward digital transformation and the growing adoption of cloud-based solutions are reshaping the landscape of the Quantum-AI Synthetic Data Generator market. Enterprises of all sizes are embracing synthetic data generation to streamline data workflows, reduce operational costs, and accelerate time-to-market for AI-powered products and services. Cloud deployment models offer scalability, flexibility, and seamless integration with existing data infrastructure, making synthetic data generation accessible even to resource-constrained organizations. As digital ecosystems evolve and data-driven decision-making becomes a competitive imperative, the strategic importance of synthetic data generation is set to intensify, fostering sustained market growth through 2033.

From a regional perspective, North America currently leads the market, driven by early technology adoption, substantial investments in quantum and AI research, and a vibrant ecosystem of startups and established technology firms. Europe follows closely, benefiting from strong regulatory frameworks and robust funding for AI innovation. The Asia Pacific region is witnessing the fastest growth, fueled by expanding digital economies, government initiatives supporting AI and quantum technology, and increasing awareness of synthetic data’s strategic value. As global enterprises seek to harness the power of quantum-AI synthetic data generators to gain a competitive edge, regional dynamics will continue to shape market trajectories and opportunities.

Component Analysis

The Component segment of the Quantum-AI Synthetic Data Generator

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Synthetic Data Video Generator Market Outlook

According to our latest research, the global Synthetic Data Video Generator market size in 2024 stands at USD 1.46 billion, with robust momentum driven by advances in artificial intelligence and the increasing need for high-quality, privacy-compliant video datasets. The market is witnessing a remarkable compound annual growth rate (CAGR) of 37.2% from 2025 to 2033, propelled by growing adoption across sectors such as autonomous vehicles, healthcare, and surveillance. By 2033, the market is projected to reach USD 18.16 billion, reflecting a seismic shift in how organizations leverage synthetic data to accelerate innovation and mitigate data privacy concerns.

The primary growth factor for the Synthetic Data Video Generator market is the surging demand for data privacy and compliance in machine learning and computer vision applications. As regulatory frameworks like GDPR and CCPA become more stringent, organizations are increasingly wary of using real-world video data that may contain personally identifiable information. Synthetic data video generators provide a scalable and ethical alternative, enabling enterprises to train and validate AI models without risking privacy breaches. This trend is particularly pronounced in sectors such as healthcare and finance, where data sensitivity is paramount. The ability to generate diverse, customizable, and annotation-rich video datasets not only addresses compliance requirements but also accelerates the development and deployment of AI solutions.

Another significant driver is the rapid evolution of deep learning algorithms and simulation technologies, which have dramatically improved the realism and utility of synthetic video data. Innovations in generative adversarial networks (GANs), 3D rendering engines, and advanced simulation platforms have made it possible to create synthetic videos that closely mimic real-world environments and scenarios. This capability is invaluable for industries like autonomous vehicles and robotics, where extensive and varied training data is essential for safe and reliable system behavior. The reduction in time, cost, and logistical complexity associated with collecting and labeling real-world video data further enhances the attractiveness of synthetic data video generators, positioning them as a cornerstone technology for next-generation AI development.

The expanding use cases for synthetic video data across emerging applications also contribute to market growth. Beyond traditional domains such as surveillance and entertainment, synthetic data video generators are finding adoption in areas like augmented reality, smart retail, and advanced robotics. The flexibility to simulate rare, dangerous, or hard-to-capture scenarios offers a strategic advantage for organizations seeking to future-proof their AI initiatives. As synthetic data generation platforms become more accessible and user-friendly, small and medium enterprises are also entering the fray, democratizing access to high-quality training data and fueling a new wave of AI-driven innovation.

From a regional perspective, North America continues to dominate the Synthetic Data Video Generator market, benefiting from a concentration of technology giants, research institutions, and early adopters across key verticals. Europe follows closely, driven by strong regulatory emphasis on data protection and an active ecosystem of AI startups. Meanwhile, the Asia Pacific region is emerging as a high-growth market, buoyed by rapid digital transformation, government AI initiatives, and increasing investments in autonomous systems and smart cities. Latin America and the Middle East & Africa are also showing steady progress, albeit from a smaller base, as awareness and infrastructure for synthetic data generation mature.

Component Analysis

The Synthetic Data Video Generator market, when analyzed by component, is primarily segmented into Software and Services. The software segment currently commands the largest share, driven by the prolif

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Comprehensive dataset of 19 Electric generator shops in Switzerland as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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Artificial Intelligence Text Generator Market Size 2024-2028

The artificial intelligence (AI) text generator market size is forecast to increase by USD 908.2 million at a CAGR of 21.22% between 2023 and 2028.

The market is experiencing significant growth due to several key trends. One of these trends is the increasing popularity of AI generators in various sectors, including education for e-learning applications. Another trend is the growing importance of speech-to-text technology, which is becoming increasingly essential for improving productivity and accessibility. However, data privacy and security concerns remain a challenge for the market, as generators process and store vast amounts of sensitive information. It is crucial for market participants to address these concerns through strong data security measures and transparent data handling practices to ensure customer trust and compliance with regulations. Overall, the AI generator market is poised for continued growth as it offers significant benefits in terms of efficiency, accuracy, and accessibility.

What will be the Size of the Artificial Intelligence (AI) Text Generator Market During the Forecast Period?

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The market is experiencing significant growth as businesses and organizations seek to automate content creation across various industries. Driven by technological advancements in machine learning (ML) and natural language processing, AI generators are increasingly being adopted for downstream applications in sectors such as education, manufacturing, and e-commerce. 
Moreover, these systems enable the creation of personalized content for global audiences in multiple languages, providing a competitive edge for businesses in an interconnected Internet economy. However, responsible AI practices are crucial to mitigate risks associated with biased content, misinformation, misuse, and potential misrepresentation.

How is this Artificial Intelligence (AI) Text Generator Industry segmented and which is the largest segment?

The artificial intelligence (AI) text generator industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solution
  Service


Application

  Text to text
  Speech to text
  Image/video to text


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) text generators have gained significant traction in various industries due to their efficiency and cost-effectiveness in content creation. These solutions utilize machine learning algorithms, such as Deep Neural Networks, to analyze and learn from vast datasets of human-written text. By predicting the most probable word or sequence of words based on patterns and relationships identified In the training data, AIgenerators produce personalized content for multiple languages and global audiences. The application spans across industries, including education, manufacturing, e-commerce, and entertainment & media. In the education industry, AI generators assist in creating personalized learning materials.

Get a glance at the Artificial Intelligence (AI) Text Generator Industry report of share of various segments Request Free Sample

The solution segment was valued at USD 184.50 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 33% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The North American market holds the largest share in the market, driven by the region's technological advancements and increasing adoption of AI in various industries. AI text generators are increasingly utilized for content creation, customer service, virtual assistants, and chatbots, catering to the growing demand for high-quality, personalized content in sectors such as e-commerce and digital marketing. Moreover, the presence of tech giants like Google, Microsoft, and Amazon in North America, who are investing significantly in AI and machine learning, further fuels market growth. AI generators employ Machine Learning algorithms, Deep Neural Networks, and Natural Language Processing to generate content in multiple languages for global audiences.

Market Dynamics

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and c

DESCRIPTION

The TAU Spatial Room Impulse Response Database (TAU-SRIR DB) database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:

Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).

Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.

Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.

Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.

The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.

NOTE: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.

REPORT AND REFERENCE

A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:

Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020), Tokyo, Japan.

available here. A more detailed report specifically focusing on the dataset collection and properties will follow.

AIM

The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:

monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions

monophonic and multichannel polyphonic sound events in multi-room reverberant conditions

single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios

single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios

sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios

SPECIFICATIONS

The SRIRs were captured using an Eigenmike spherical microphone array. A Genelec G Three loudspeaker was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.

The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:

Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.

Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.

Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.

Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.

Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.

Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.

Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.

Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.

Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.

The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a close and a far one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, close and far, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.

Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights.

The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.

More details on the trajectory geometries can be found in the README file and the measinfo.mat file.

RECORDING FORMATS

As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README.

We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).

REFERENCE DOAs

For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.

The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.

SCENE GENERATOR

A set of routines is shared, here termed scene generator, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the TAU-NIGENS Spatial Sound Events 2021 dataset, and has been ported to Python from the original version written in Matlab.

The generator can be found here, along with more details on its use.

The generator at the moment is set to work with the NIGENS sound event sample database, and the FSD50K sound event database, but additional sample banks can be added with small modifications.

The dataset together with the generator has been used by the authors in the following public challenges:

• DCASE 2019 Challenge Task 3, to generate the TAU Spatial Sound Events 2019 dataset (development/evaluation)

• DCASE 2020 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2020 dataset

• DCASE2021 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2021 dataset

• DCASE2022 Challenge Task 3, to generate additional SELD synthetic mixtures for training the task baseline

NOTE: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact us.

DATASET STRUCTURE

The dataset contains a folder of the SRIRs (TAU-SRIR_DB), with all the SRIRs per room in a single MAT file. The file rirdata.mat contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file measinfo.mat contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (TAU-SNoise_DB), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README.

DOWNLOAD

The files TAU-SRIR_DB.z01, ..., TAU-SRIR_DB.zip contain the SRIRs and measurement info files.

The files TAU-SNoise_DB.z01, ..., TAU-SNoise_DB.zip

This files contain the data used for the computational tests in the paper "A Detailed Li-ion Battery Operation Model". The dataset includes: generator data, lines' parameters, load profile and battery sample points.

Optimization of chemical reactions requires a thorough analysis of reaction products and intermediates over a given time course. Chemical reactions are often analyzed by liquid chromatography-mass spectrometry (LC-MS), but generating LC-MS samples and data analysis is time-consuming and produces a significant amount of waste. We sought to remove the sample preparation and data analysis steps by implementing an iChemExplorer/Agilent LC-MS instrument as our reactor and analysis tool, coupled with an automated report generator of reaction progress over time. Herein, we show that our easy-to-use walk-up automated reaction profiling (WARP) system can sample chemical reactions multiple times to produce a data-rich report of reaction progress over time.

Madgraph5 simulation of a simplified model of Zprime with dijets and lepton decays of W/Z. Interference effects are included. Pythia8 was used to shower events. LHAPDF6:NNPDF23_lo_as_0130_qed. The sample was created using the Pythia8 tune Tune:pp = 14 similar to ATLAS.

Higgs production to ZZ* in Pythia6.4. Z-boson decays to e+e- or mu+mu-. This sample uses older Pythia version for detector performance studies and creation of full simulated samples. No slimming. The sample is not recommended for physics studies since uses outdated PDF built-in in Pythai6.4 1k events per file. The full simulation is based on SiD detector to check performance issues.

Comprehensive dataset of 9 Electric generator shops in Japan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 20 Electric generator shops in Muğla, Turkey as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. The data obtained during the performance evaluation of the bell-jar is published in this repository. The files of the following experiments can be found here:

m1

Measurement data online mercury analyser comparison VSL and bell-jar 2023-02-20 Bell_jar_calibration_m1 m2

Measurement data online mercury analyser comparison VSL and bell-jar 2023-02-28 Bell_jar_calibration_m2 m3

Measurement data online mercury analyser comparison VSL and bell-jar 2023-03-09 Bell_jar_calibration_m3 short-term drift

m1

Calibration bell-jar short term drif_m1 Bell_jar_drift_m1 m2

Calibration bell-jar short term drif_m2 Bell_jar_drift_m2 m3

Calibration bell-jar short term drif_m3 Bell_jar_drift_m3 m4

Calibration bell-jar short term drif_m4 Bell_jar_drift_m4 stability

Measurement data online mercury analyser comparison VSL and bell-jar stability

We recently published a high quality validation set for testing conformer generators, consisting of structures from both the PDB and the CSD (Hawkins, P. C. D. et al. J. Chem. Inf. Model. 2010, 50, 572.), and tested the performance of our conformer generator, OMEGA, on these sets. In the present publication, we focus on understanding the suitability of those data sets for validation and identifying and learning from OMEGA’s failures. We compare, for the first time we are aware of, the coverage of the applicable property spaces between the validation data sets we used and the parent compound sets to determine if our data sets adequately sample these property spaces. We also introduce the concept of torsion fingerprinting and compare this method of dissimilation to the more traditional graph-centric diversification methods we used in our previous publication. To improve our ability to programmatically identify cases where the crystallographic conformation is not well reproduced computationally, we introduce a new metric to compare conformations, RMSTanimoto. This new metric is used alongside those from our previous publication to efficiently identify reproduction failures. We find RMSTanimoto to be particularly effective in identifying failures for the smallest molecules in our data sets. Analysis of the nature of these failures, particularly those for the CSD, sheds further light on the issue of strain in crystallographic structures. Some of the residual failure cases not resolved by simple changes in OMEGA’s defaults present significant challenges to conformer generation engines like OMEGA and are a source of new avenues to further improve their performance, while others illustrate the pitfalls of validating against crystallographic ligand conformations, particularly those from the PDB.