We also present the synthesizers that best preserve PPV and TPR accross subgroups. We present the two best synthetic data generator for each task. We selected best synthesizer and runner up based on experiments with privacy-loss budget ϵ = 5.0.

SYNTH

SYNTH is the first open generalist synthetic dataset for training small reasoning model end-to-end, jointly released by Pleias and the AI Alliance. SYNTH includes 79,648,272 individual text samples, comprising over 41 billion words (about 75 billion tokens with Pleias tokenizer). It is based on the amplification of 58,698 articles from Wikipedia and made possible thanks to the Structured Wikipedia dataset from Wikimedia Enterprise. SYNTH differs from existing open synthetic… See the full description on the dataset page: https://huggingface.co/datasets/SYNTH-Initiative/SYNTH.

Offline Synthetic Data (GeoFactX) for: Making, not taking, the Best-of-N

  Content

This data contains completions for the GeoFactX training split prompts from 5 different teacher models and 2 aggregations: Teachers: We sample one completion from each of the following models at temperature T=0.3. For kimik2, qwen3, and deepseek-v3 we use TogetherAI, for gemma3-27b and command-a we use locally hosted images.

gemma3-27b: GEMMA3-27B-IT kimik2: KIMI-K2-INSTRUCT qwen3:… See the full description on the dataset page: https://huggingface.co/datasets/CohereLabs/fusion-synth-data-geofactx.

nalkhou/synth-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Synthetic Credit and SMEs Dataset: Simulated Data for Credit Analysis

Overview: This dataset provides synthetic data for credit analysis, encompassing both individual credit consumers and small to medium-sized enterprises (SMEs). It is designed to simulate various financial and credit-related aspects, enabling analytical and modeling tasks related to credit risk assessment, financial analysis, and business intelligence.

Features for Credit Consumers:

CustomerID: Unique identifier for each credit consumer. Name: Name of the credit consumer. Age: Age of the credit consumer. CreditScore: Credit score of the consumer, ranging from 300 to 850. TransactionAmount: Amount of transactions made by the consumer. LoanAmount: Loan amount associated with the consumer. LatePayments: Number of late payments made by the consumer. Features for SMEs:

BusinessID: Unique identifier for each SME (Small and Medium-sized Enterprise). BusinessName: Name of the SME. Industry: Industry to which the SME belongs (Technology, Finance, Retail, Healthcare). AnnualRevenue: Annual revenue generated by the SME. CreditScore: Credit score of the SME, ranging from 300 to 850. LatePayments: Number of late payments made by the SME. Purpose: This dataset is generated to facilitate research and analysis in the domain of credit evaluation. It provides a diverse set of features for both individual consumers and SMEs, allowing users to explore credit-related patterns, assess risk factors, and develop predictive models.

Intended Usage:

Credit risk assessment for individual consumers and SMEs. Financial analysis and profiling of credit behaviors. Development and testing of credit scoring models. Business intelligence applications in the financial sector. Note: The data is entirely synthetic and does not represent real individuals or businesses. It is suitable for educational purposes, research, and analysis. Users are encouraged to adapt and extend the dataset based on specific use cases and research objectives.

SDNist (v1.3) is a set of benchmark data and metrics for the evaluation of synthetic data generators on structured tabular data. This version (1.3) reproduces the challenge environment from Sprints 2 and 3 of the Temporal Map Challenge. These benchmarks are distributed as a simple open-source python package to allow standardized and reproducible comparison of synthetic generator models on real world data and use cases. These data and metrics were developed for and vetted through the NIST PSCR Differential Privacy Temporal Map Challenge, where the evaluation tools, k-marginal and Higher Order Conjunction, proved effective in distinguishing competing models in the competition environment. SDNist is available via pip install: pip install sdnist==1.2.8 for Python >=3.6 or on the USNIST/Github. The sdnist Python module will download data from NIST as necessary, and users are not required to download data manually.

We compare and rank all synthesizers by their ability to generate quality training data and evaluation data for machine learning pipelines. The comparison presented accounts for synthetic data generated with privacy-loss parameter ϵ = 5.0. In addition to present a performance ranking for Adult, COMPAS data and COMPAS (fair) data, we show a comparison of model AUC measured in TSTR mode—AUC(R), and model AUC measured in TSTS mode—AUC(S).

DNA Data Storage Synthesizer Market Outlook

According to our latest research, the global DNA Data Storage Synthesizer market size reached USD 312 million in 2024, reflecting an impressive momentum in the adoption of next-generation data storage technologies. The market is projected to grow at a robust CAGR of 38.2% from 2025 to 2033, reaching approximately USD 4,965 million by 2033. This exponential growth is primarily driven by the surging demand for ultra-high-density, long-term data storage solutions that can overcome the limitations of conventional electronic storage mediums. As organizations worldwide confront the challenges of data explosion, DNA data storage synthesizers are emerging as a transformative technology, promising unparalleled storage density, longevity, and sustainability.

One of the primary growth factors propelling the DNA Data Storage Synthesizer market is the rapidly increasing volume of digital data generated across industries such as healthcare, finance, media, and research. Traditional storage solutions, including magnetic tapes, hard drives, and SSDs, are struggling to keep pace with the exponential growth of big data, particularly in applications demanding high security and durability. DNA, as a storage medium, offers a theoretical density of up to one exabyte per cubic millimeter and can preserve data for thousands of years under optimal conditions. This unique advantage is driving significant investments in DNA synthesis technologies, with both public and private sectors recognizing the strategic importance of future-proof data archiving.

Technological advancements in DNA synthesis and sequencing have also played a critical role in accelerating the market’s growth trajectory. The cost of DNA synthesis has dropped dramatically over the past decade, making it increasingly feasible for commercial and research applications. Innovations such as enzymatic synthesis and microarray-based high-throughput synthesizers are enabling faster, more accurate, and cost-effective encoding of digital information into DNA strands. Furthermore, the integration of automation and AI-driven optimization in synthesizer platforms is enhancing the scalability and reliability of DNA data storage, attracting interest from cloud providers, research institutes, and enterprise data centers seeking to future-proof their infrastructure.

The regional outlook for the DNA Data Storage Synthesizer market is highly promising, with North America currently leading the global landscape due to its advanced research ecosystem, significant funding, and presence of key market players. Europe follows closely, supported by robust academic collaborations and government initiatives in digital preservation. Meanwhile, the Asia Pacific region is emerging as a high-growth market, driven by rapid digitalization, expanding biotech capabilities, and increasing investments in next-generation storage technologies. As these regions continue to invest in R&D and infrastructure, the global adoption of DNA data storage synthesizers is poised to accelerate, paving the way for new business models and applications across diverse sectors.

Product Type Analysis

The Product Type segment of the DNA Data Storage Synthesizer market is categorized into Benchtop Synthesizers, High-Throughput Synthesizers, and Custom Synthesizers. Benchtop Synthesizers, known for their compact footprint and flexibility, are increasingly favored by academic and research institutes for small-scale, experimental workflows. Their ease of use and relatively lower investment threshold make them ideal for proof-of-concept studies and pilot projects in DNA data storage. These systems are designed to support rapid prototyping and iterative development, enabling researchers to optimize encoding protocols and assess data integrity in a controlled laboratory environment.

High-Throughput Synthesizers, on the other hand, are gaining traction among large enterprises and data centers that require industrial-scale data archiving c

The Google-synth dataset comprises a synthetic Punjabi dataset that has been generated using Google's Cloud Text-to-Speech service. This dataset encompasses approximately 50,000 synthesized utterances, featuring four synthetic speakers (two male and two female), which amounts to approximately 38 hours of audio data. To facilitate training, validation, and testing, the dataset has been pre-divided into three portions: 80% for training, 10% for validation, and 10% for testing. The dataset is meticulously organized, with all speech files stored in the "clips" directory. The corresponding transcript files (train, dev, and test) are situated in the parent directory and follow the TSV (Tab-Separated Values) format. Each line within the transcript files represents a label assigned to a particular speech sample from the clips directory. The first column of each line contains the path and name of the corresponding WAV file, while the second column, separated by a tab, contains the transcript in textual form.

This synthetic dataset includes 16,276 patients admitted for drug overdose from 2016 to 2022, featuring comprehensive patient demographics, comorbidities coded by ICD-10 and SNOMED-CT, and detailed admission data from the index event onward. Information on clinical outcomes, primary diagnoses, psychiatric referrals, and all treatments (e.g., fluids, blood products, procedures) is included.

The dataset was generated using the SDV package's HMA1 synthesizer. The real data was pre-processed, with metadata defining schema, primary/foreign keys, and inter-table relationships, guiding the synthesizer in learning data structure and dependencies. This approach produced synthetic data that mirrors the original’s statistical properties, supporting privacy-preserving analysis and model training.

Geography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

6498 Global import shipment records of Synthesizer with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset contains the predicted prices of the asset Synthesizer Dog over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

To support respiratory research, a synthetic asthma dataset was generated based on a real-world data, originally documenting 381 patients with physician-confirmed asthma who were admitted to secondary care at a single centre in 2019. The dataset is highly detailed, covering demographics, structured physiological data, medication records, and clinical outcomes. The synthetic version extends to 561 patients admitted over a year, offering insights into patient patterns, risk factors, and treatment strategies.

The dataset was created using the Synthetic Data Vault package, specifically employing the GAN synthesizer. Real data was first read and pre-processed, ensuring datetime columns were correctly parsed and identifiers were handled as strings. Metadata was defined to capture the schema, specifying field types and primary keys. This metadata guided the synthesizer in understanding the structure of the data. The GAN synthesizer was then fitted to the real data, learning the distributions and dependencies within. After fitting, the synthesizer generated synthetic data that mirrors the statistical properties and relationships of the original dataset.

Geography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Real world data. Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can provide real-world data upon request.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Dataset for multiple publishable figures in the paper entitled "Leakage Current Pathways in Josephson Arbitrary Waveform Synthesizer Standards"to be submitted to CPEM 2024 Extended Paper (IEEE Transactions on Instrumentation and Measurement). The voltage errors associated with leakage currents in Josephson arbitrary waveform synthesizer (JAWS) systems are significant contributors to the overall system accuracy. This paper describes discrepancies in output voltage between two different circuit halves on a single JAWS chip and shows that this discrepancy is dominated by ac leakage currents through the stray capacitance in the compensation leads.

This dataset contains the predicted prices of the asset Synthesizer Cat over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Access Voice Synthesizer export import data including profitable buyers and suppliers with details like HSN code, Price, Quantity.

Test Blender Synth Data

## Overview

Test Blender Synth Data is a dataset for object detection tasks - it contains Geometric Shape annotations for 329 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Access updated Peptide Synthesizer import data India with HS Code, price, importers list, Indian ports, exporting countries, and verified Peptide Synthesizer buyers in India.

22.5 hours of synthesized audio using the open-source learnfm clone of the DX7 FM synthesizer, based upon 31K presets from Bobby Blue. These represent ``natural'' synthesis sounds---i.e.presets devised by humans.

We generated 4-second samples playing midi note 69 (A440) with a note-on duration of 3 seconds. For each preset, we varied only the velocity, from 1--127, and perceptually normalized the level of each sound. Sounds that were completely identical were removed from the dataset. DX7 FM synthesis is good for this purpose because it doesn't have a noise oscillator. Thus, for a particular preset, there is a timbral variation as the velocity increases. 8K presets had only one unique sound. The median was 51 unique sound per preset, mean 41.9, stddev 27.4.

We used the Surge Python API to generate this dataset.

Applications of this dataset include:

Timbre ranking within a preset

Predict a sound's preset