This dataset is about book subjects. It has 4 rows and is filtered where the books is Changing the game for generation alpha : teaching and raising young children in the 21st century. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F2b87409e296a59d20dab602e6501f340%2Ffile9e063b84e35.gif?generation=1707771596337465&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F9d574862156fdd14299b6bcdf1d7c0e8%2Ffile9e048912e2.gif?generation=1707771713059014&alt=media" alt="">

US Funds Dataset: Unlocking Insights for Informed Investment Decisions

Exchange-Traded Funds (ETFs) have gained significant popularity in recent years as a low-cost alternative to Mutual Funds. This dataset, compiled from Yahoo Finance, offers a comprehensive overview of the US funds market, encompassing 23,783 Mutual Funds and 2,310 ETFs.

Data

The dataset provides a wealth of information on each fund, including:

General fund aspects: total net assets, fund family, inception date, expense ratios, and more. Portfolio indicators: cash allocation, sector weightings, holdings diversification, and other key metrics. Historical returns: year-to-date, 1-year, 3-year, and other performance data for different time periods. Financial ratios: price/earnings ratio, Treynor and Sharpe ratios, alpha, beta, and ESG scores. Applications

This dataset can be leveraged by investors, researchers, and financial professionals for a variety of purposes, including:

Investment analysis: comparing the performance and characteristics of Mutual Funds and ETFs to make informed investment decisions. Portfolio construction: using the data to build diversified portfolios that align with investment goals and risk tolerance. Research and analysis: studying market trends, fund behavior, and other factors to gain insights into the US funds market. Inspiration and Updates

The dataset was inspired by the surge of interest in ETFs in 2017 and the subsequent shift away from Mutual Funds. The data is sourced from Yahoo Finance, a publicly available website, ensuring transparency and accessibility. Updates are planned every 1-2 semesters to keep the data current and relevant.

Conclusion

This comprehensive dataset offers a valuable resource for anyone seeking to gain a deeper understanding of the US funds market. By providing detailed information on a wide range of funds, the dataset empowers investors to make informed decisions and build successful investment portfolios.

Access the dataset and unlock the insights it offers to make informed investment decisions.

Rapidata Video Generation Runway Alpha Human Preference

If you get value from this dataset and would like to see more in the future, please consider liking it.

This dataset was collected in ~1 hour total using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.

  Overview

In this dataset, ~30'000 human annotations were collected to evaluate Runway's Alpha video generation model on our benchmark. The up to date benchmark can… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-runway-alpha.

DEPRECATED. DO NOT USE. See current version at https://res1dxd-o-tdoid-o-torg.vcapture.xyz/10.7799/1812548. See active link below in the resources section. Open sourced data needed to run the basic alpha release version of the dGen model. Includes a pre-generated agent file of 100,000 agents in pickle file format along with the base schema and table data in parquet format that are needed to create a postgreSQL database for the model to interact with.

This dataset is a rich collection of handwritten Sindhi alphabet images, carefully curated to capture a diverse range of writing styles. The dataset includes samples from multiple generations, including Gen X, Millennials, Gen Z, and Gen Alpha, ensuring a broad representation of handwriting variations. Additionally, it encompasses contributions from individuals of different genders and varying levels of handwriting proficiency, making it highly valuable for research in handwriting recognition and computer vision.

This dataset is ideal for training machine learning models on tasks such as:
- Optical Character Recognition (OCR) for Sindhi script
- Handwriting style analysis across generations
- Character classification and dataset augmentation experiments
- Computer vision research involving regional scripts

Dataset Details:

• Total Sindhi Letters: 52
• Images per Letter: 58
• Total Images: 3016
• Format: JPG
• Diversity Factors:
  • Generations Covered: Gen X, Millennials, Gen Z, Gen Alpha
  • Gender Representation: Male and Female contributors
  • Writing Styles: Includes cursive, bold, thin, and uneven strokes

Dataset Structure:

The dataset is structured into 52 folders, each representing a unique Sindhi letter. Each folder contains 31 handwritten samples of that letter, captured from various contributors.

Usage & Applications:

This dataset can be used by researchers, educators, and developers working on:
- Handwriting Recognition Models
- AI-powered OCR for Sindhi Language
- Multi-generational Handwriting Studies
- Sindhi Language Digitization & Preservation

License & Attribution

This dataset is publicly available under the CC BY 4.0 License, meaning you can use it freely with proper attribution.

This dataset was created through the combined efforts of multiple contributors who provided handwritten samples.

• Mudasir Murtaza
• Farhad Ali

This dataset is now open-source and we encourage researchers, developers, and students to use this dataset for AI projects and Sindhi handwriting recognition models!

Ondřejov dataset contains 12936 labelled stellar spectra from Ondřejov CCD700 archive. The spectra were observed with Ondřejov Perek 2m Telescope.

Code used for generation of this dataset is in podondra/ondrejov-dataset GitHub repository.

The dataset was created to support the discovery of emission-line spectra in the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. The main idea was to use Ondřejov dataset to train a machine learning algorithm and (in combination with domain adaption) find interesting objects in the large spectral archive.

The dataset is released as a CSV file containing the following columns for each spectrum:

• id: a unique identifier (FITS file name),
• label: assigned class,
• object: title of observation,
• ra: right ascension,
• dec: declination,
• expval: exposure value in photon counts [Mcounts],
• gratang: diffraction grating angle,
• detector: name of the detector,
• chipid: name of CCD chip,
• specfilt: spectral filter,
• date-obs: UTC date start of the observation,
• dichmir: dichroic mirror number,
• fluxes: 140 columns of fluxes sampled uniformly between 6519 and 6732 Ångströms.

Spectra are divided into 3 classes according to the profile of the H-alpha spectral line:

• absorption: 6102 spectra (47.17%),
• emission: 5301 spectra (40.98%),
• double-peak: 1533 spectra (11.85%),

where double-peak is a special type of emission with typical disk geometry common in Be stars.

Spectra from Ondřejov CCD700 archive are in air wavelengths but LAMOST spectra use vacuum wavelengths. Therefore, conversion of Ondřejov spectra was made according to formulas provided on Vienna Atomic Line Database Wiki.

LAMOST spectrograph spectral resolving power is between 500 and 1800 which is much smaller than spectral resolving power 13000 in H-alpha of Ondřejov spectrograph. To overcome this difference spectra from the dataset were blurred with Gaussian filter with a standard deviation of value 7.

Machine learning algorithms require their inputs to be a set of features. In order to have the same features for all spectra, they need to be resampled to get the measurement in the same wavelength across all spectra. Then it is easy to create a design matrix where each row is a spectrum and columns contain fluxes in specified wavelengths between 6519 and 6732 Ångströms.

Open sourced data needed to run the basic alpha release version of the dGen model. Includes a pre-generated agent file of 100,000 agents in pickle file format along with the base schema and table data in parquet format that are needed to create a postgreSQL database for the model to interact with.

Star-forming galaxies with strong nebular and collisional emission lines are privileged target galaxies in forthcoming cosmological large galaxy redshift surveys. We use the COSMOS2015 photometric catalogue to model galaxy spectral energy distributions and emission-line fluxes. We adopt an empirical but physically motivated model that uses information from the best-fitting spectral energy distribution of stellar continuum to each galaxy. The emission-line flux model is calibrated and validated against direct flux measurements in subsets of galaxies that have 3D-HST or zCOSMOS-Bright spectra. We take a particular care in modelling dust attenuation such that our model can explain both H{alpha} and [OII] observed fluxes at different redshifts. We find that a simple solution to this is to introduce a redshift evolution in the dust attenuation fraction parameter, f=E_star_(B-V)/E_gas_(B-V), as f(z)=0.44+0.2z. From this catalogue, we derive the H{alpha} and [OII] luminosity functions up to redshifts of about 2.5 after carefully accounting for emission line flux and redshift errors. This allows us to make predictions for H{alpha} and [OII] galaxy number counts in next-generation cosmological redshift surveys. Our modelled emission lines and spectra in the COSMOS2015 catalogue shall be useful to study the target selection for planned next-generation galaxy redshift surveys and we make them publicly available as 'EL-COSMOS' on the ASPIC data base. Cone search capability for table J/MNRAS/494/199/tablec1 (EL-COSMOS catalogue)