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.

Year: The year the song was released.

Artist: The artist or group who performed the song.

Song: The title of the song.

Genre: The genre of the song.

Lyrics: Excerpts from the lyrics of the song.

Danceability: A measure of how suitable a track is for dancing, based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity.

Energy: A measure of intensity and activity in the song, typically including dynamic range, perceived loudness, timbre, onset rate, and general entropy.

Liveness: A measure of the presence of an audience in the recording. Higher values represent an increased probability that the track was performed live.

Speechiness: A measure of the presence of spoken words in the track. Values above 0.66 describe tracks that are probably entirely spoken words.

Valence: A measure of the musical positiveness of a track, ranging from 0 to 1, with higher values indicating more positive tracks (e.g., happy, cheerful, euphoric), and lower values indicating more negative tracks (e.g., sad, depressed, angry).

This dataset seems to capture a variety of popular songs from the mid-20th century, providing insights into their lyrical content and musical characteristics.

AlephAlphaGermanWeb

Aleph-Alpha-GermanWeb is a new German-language dataset that combines heuristic and model-based filtering techniques with synthetic data generation to achieve SOTA performance in German-language benchmarks. The dataset draws from three sources: (1) Common Crawl web data, (2) FineWeb2, and (3) synthetically-generated data conditioned on actual, organic web data. In our accompanying paper, we evaluated our dataset by training both a 1B Llama-style model and an 8B… See the full description on the dataset page: https://huggingface.co/datasets/Aleph-Alpha/Aleph-Alpha-GermanWeb.

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.

The Global Pattern Search for alpha-level Optimisation (aGPS) is a global optimisation approach explicitly designed for efficient and user-friendly alpha-level optimisations. It can be used to improve the efficiency of fuzzy structural analyses. The efficiency of aGPS stems from its deterministic sample generation, which allows a reuse of many samples within the various alpha-level optimisations. Moreover, information gained within an alpha-level optimisation is used for all subsequent optimisations. It outperforms state-of-the-art algorithms. This means that it requires less model evaluations, and therefore, it has lower computing times. Here, the entire source code for its implementation in MATLAB is given. For further information, it is referred to "Huebler, C., & Hofmeister, B. (2021). Efficient and user-friendly alpha-level optimisation for application-orientated fuzzy structural analyses. Submitted to Engineering Structures."

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… See the full description on the dataset page: https://huggingface.co/datasets/maddy2104/Sindhi-Handwritten-Alphabets.