The data used in this paper is from the 16th issue of SDSS. SDSS-DR16 contains a total of 930,268 photometric images, with 1.2 billion observation sources and tens of millions of spectra. The data obtained in this paper is downloaded from the official website of SDSS. Specifically, the data is obtained through the SkyServerAPI structure by using SQL query statements in the subwebsite CasJobs. As the current SDSS photometric table PhotoObj can only classify all observed sources as point sources and surface sources, the target sources can be better classified as galaxies, stars and quasars through spectra. Therefore, we obtain calibrated sources in CasJobs by crossing SpecPhoto with the PhotoObj star list, and obtain target position information (right ascension and declination). Calibrated sources can tell them apart precisely and quickly. Each calibrated source is labeled with the parameter "Class" as "galaxy", "star", or "quasar". In this paper, observation day area 3462, 3478, 3530 and other 4 areas in SDSS-DR16 are selected as experimental data, because a large number of sources can be obtained in these areas to provide rich sample data for the experiment. For example, there are 9891 sources in the 3462-day area, including 2790 galactic sources, 2378 stellar sources and 4723 quasar sources. There are 3862 sources in the 3478 day area, including 1759 galactic sources, 577 stellar sources and 1526 quasar sources. FITS files are a commonly used data format in the astronomical community. By cross-matching the star list and FITS files in the local celestial region, we obtained images of 5 bands of u, g, r, i and z of 12499 galaxy sources, 16914 quasar sources and 16908 star sources as training and testing data.1.1 Image SynthesisSDSS photometric data includes photometric images of five bands u, g, r, i and z, and these photometric image data are respectively packaged in single-band format in FITS files. Images of different bands contain different information. Since the three bands g, r and i contain more feature information and less noise, Astronomical researchers typically use the g, r, and i bands corresponding to the R, G, and B channels of the image to synthesize photometric images. Generally, different bands cannot be directly synthesized. If three bands are directly synthesized, the image of different bands may not be aligned. Therefore, this paper adopts the RGB multi-band image synthesis software written by He Zhendong et al. to synthesize images in g, r and i bands. This method effectively avoids the problem that images in different bands cannot be aligned. The pixel of each photometry image in this paper is 2048×1489.1.2 Data tailoringThis paper first clipped the target image, image clipping can use image segmentation tools to solve this problem, this paper uses Python to achieve this process. In the process of clipping, we convert the right ascension and declination of the source in the star list into pixel coordinates on the photometric image through the coordinate conversion formula, and determine the specific position of the source through the pixel coordinates. The coordinates are regarded as the center point and clipping is carried out in the form of a rectangular box. We found that the input image size affects the experimental results. Therefore, according to the target size of the source, we selected three different cutting sizes, 40×40, 60×60 and 80×80 respectively. Through experiment and analysis, we find that convolutional neural network has better learning ability and higher accuracy for data with small image size. In the end, we chose to divide the surface source galaxies, point source quasars, and stars into 40×40 sizes.1.3 Division of training and test dataIn order to make the algorithm have more accurate recognition performance, we need enough image samples. The selection of training set, verification set and test set is an important factor affecting the final recognition accuracy. In this paper, the training set, verification set and test set are set according to the ratio of 8:1:1. The purpose of verification set is used to revise the algorithm, and the purpose of test set is used to evaluate the generalization ability of the final algorithm. Table 1 shows the specific data partitioning information. The total sample size is 34,000 source images, including 11543 galaxy sources, 11967 star sources, and 10490 quasar sources.1.4 Data preprocessingIn this experiment, the training set and test set can be used as the training and test input of the algorithm after data preprocessing. The data quantity and quality largely determine the recognition performance of the algorithm. The pre-processing of the training set and the test set are different. In the training set, we first perform vertical flip, horizontal flip and scale on the cropped image to enrich the data samples and enhance the generalization ability of the algorithm. Since the features in the celestial object source have the flip invariability, the labels of galaxies, stars and quasars will not change after rotation. In the test set, our preprocessing process is relatively simple compared with the training set. We carry out simple scaling processing on the input image and test input the obtained image.

rStar-Coder Dataset

Project GitHub | Paper

  Dataset Description

rStar-Coder is a large-scale competitive code problem dataset containing 418K programming problems, 580K long-reasoning solutions, and rich test cases of varying difficulty levels. This dataset aims to enhance code reasoning capabilities in large language models, particularly in handling competitive code problems. Experiments on Qwen models (1.5B-14B) across various code reasoning benchmarks demonstrate… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/rStar-Coder.

Dataset Info:

This is a dataset consisting of several features of stars.

Some of them are: - Absolute Temperature (in K) - Relative Luminosity (L/Lo) - Relative Radius (R/Ro) - Absolute Magnitude (Mv) - Star Color (white,Red,Blue,Yellow,yellow-orange etc) - Spectral Class (O,B,A,F,G,K,,M) - Star Type **(Red Dwarf, Brown Dwarf, White Dwarf, Main Sequence , SuperGiants, HyperGiants)**

Lo = 3.828 x 10^26 Watts (Avg Luminosity of Sun) Ro = 6.9551 x 10^8 m (Avg Radius of Sun)

Purpose:

The purpose of making the dataset is to prove that the stars follows a certain graph in the celestial Space , specifically called Hertzsprung-Russell Diagram or simply HR-Diagram so that we can classify stars by plotting its features based on that graph.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2F14338bbebf77d18e1faef582bccdbdd6%2Fhr.jpg?generation=1597349509841965&alt=media" alt="hr-1"> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2F9fc57334a9b9fafbc71aacdd6e5cd69c%2F310px-Hertzsprung-Russel_StarData.png?generation=1597349661801284&alt=media" alt="hr-2">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2Ffe9436bf4e2d23b5b18fb3db1f1fcbcb%2FHRDiagram.png?generation=1597348809674507&alt=media" alt="hr-3">

Data Collection and Preparation techniques:

The dataset is created based on several equations in astrophysics. They are given below:

1. Stefan-Boltzmann's law of Black body radiation (To find the luminosity of a star)
2. Wienn's Displacement law (for finding surface temperature of a star using wavelength)
3. Absolute magnitude relation
4. Radius of a star using parallax .

The dataset took 3 weeks to collect for 240 stars which are mostly collected from web. The missing data were manually calculated using those equations of astrophysics given above.

Acknowledgements:

• Wikipedia
• Stars and Galaxies by SEEDS | Backman

Repo Link:

AstroML

This dataset tracks annual average expenditure per student from 1995 to 2021 for Union Star R-II School District

Analysis of ‘Star dataset to predict star types’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/deepu1109/star-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Dataset Info:

This is a dataset consisting of several features of stars.

Some of them are: - Absolute Temperature (in K) - Relative Luminosity (L/Lo) - Relative Radius (R/Ro) - Absolute Magnitude (Mv) - Star Color (white,Red,Blue,Yellow,yellow-orange etc) - Spectral Class (O,B,A,F,G,K,,M) - Star Type **(Red Dwarf, Brown Dwarf, White Dwarf, Main Sequence , SuperGiants, HyperGiants)**

Lo = 3.828 x 10^26 Watts (Avg Luminosity of Sun) Ro = 6.9551 x 10^8 m (Avg Radius of Sun)

Purpose:

The purpose of making the dataset is to prove that the stars follows a certain graph in the celestial Space , specifically called Hertzsprung-Russell Diagram or simply HR-Diagram so that we can classify stars by plotting its features based on that graph.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2F14338bbebf77d18e1faef582bccdbdd6%2Fhr.jpg?generation=1597349509841965&alt=media" alt="hr-1"> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2F9fc57334a9b9fafbc71aacdd6e5cd69c%2F310px-Hertzsprung-Russel_StarData.png?generation=1597349661801284&alt=media" alt="hr-2">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2Ffe9436bf4e2d23b5b18fb3db1f1fcbcb%2FHRDiagram.png?generation=1597348809674507&alt=media" alt="hr-3">

Data Collection and Preparation techniques:

The dataset is created based on several equations in astrophysics. They are given below:

1. Stefan-Boltzmann's law of Black body radiation (To find the luminosity of a star)
2. Wienn's Displacement law (for finding surface temperature of a star using wavelength)
3. Absolute magnitude relation
4. Radius of a star using parallax .

The dataset took 3 weeks to collect for 240 stars which are mostly collected from web. The missing data were manually calculated using those equations of astrophysics given above.

Acknowledgements:

• Wikipedia
• Stars and Galaxies by SEEDS | Backman

Repo Link:

AstroML

--- Original source retains full ownership of the source dataset ---

USACO 2025 Open Contest Dataset

  Dataset Description

The USA Computing Olympiad (USACO) is a prestigious algorithmic programming competition for high school students in the United States, consisting of four difficulty levels: Bronze, Silver, Gold, and Platinum. Each level contains a set of challenging problems that test algorithmic thinking and implementation skills, making USACO a valuable benchmark for evaluating the reasoning and problem-solving capabilities of large… See the full description on the dataset page: https://huggingface.co/datasets/rStar-Reasoning/usaco_2025.

we propose to investigate the newly discovered 7.15 micron oxygenrich (or) circumstellar emission feature by observing its profile and distribution among approximately 50 oxygenrich stars distributed throughout the sky. we expect this study to place constraints on the abundances, stellar evolution and chemistry of dust formation in these stars, and the importance of the emitting material as a possible inter stellar dust component. the iras autoclass ii study of cheeseman et al. 1989 provides a guide from which we select a set of stars representing the entire population of or stars. one star is selected from each of the 32 or subclasses expected to have the feature present. based upon the work of goebel et al.(1994 ap.j. july 15,1994), these objects have a high probability of displaying the 7.15 um feature. stars from each r^ant subclass need to be observed with the sws throughout their entire spectral range so that all spectral features may be accurately profiled, defined, and correlated with the 7.15 um feature. high reso lution scans are required to determine the influence of molecular bands on the features at 7.15, 10, 11.3, 13.1, 18, and 19.7 um. shape and relative strength of the features should enable identification of the responsible dust component. once high resolution scans of a rep resentative sample of or stars have been obtained, the low resolution spectra of survey investigators can be used to supplement our statis tics, but not to determine the confusing influences of molecular bands, nor meet the goals of this proposal. we anticipate reciprocal sharing of the proposed aot sws013 high resolution data with other groups that have aot sws011 low resolution data of or objects, notably price, tsuji, de jong, heske, waters, and barlow. the 7.15 um feature is in a spectral region blocked completely by the earths atmospheric water vapor. iso is the only platform with sufficient sensitivity, sky cover truncat [truncated!, Please see actual data for full text]

This dataset tracks annual graduation rate from 2012 to 2022 for Union Star High School vs. Missouri and Union Star R-II School District

Data containing the species, log gf, and abundances for BPS CS 29529-0089, TYC 9219-2422-1 and HD 122563 in aa53295-24.

R Star Company Limited Company Export Import Records. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

chiruan/rStar-Coder-seed-test dataset hosted on Hugging Face and contributed by the HF Datasets community

The accompanying files provide some IDL code and "data behind the figures" for the paper titled "Brown Dwarfs are Violet" (by S. R. Cranmer), which has been submitted to Research Notes of the AAS.

This paper presents a collection of objective (CIE x,y coordinate) and subjective (RGB triple) colors for main-sequence stars and brown dwarfs, as they may be perceived by human eyes without the reddening effects of the Earth's atmosphere. However, the algorithm described in the paper for computing RGB triples ought to be considered as only a preliminary first step; i.e., it needs to be tested by comparing the results to other more established ways of converting astronomical spectra to perceived colors.

This dataset tracks annual total revenue from 1990 to 2021 for Union Star R-II School District

This dataset tracks annual distribution of students across grade levels in Union Star R-II School District and average distribution per school district in Missouri

The type of Energy Star HVAC-R equipment with most shipments in 2023 were for refrigeration. HVAC-R stands for heating, ventilation, air-conditioning, and refrigeration. These figures do not refer to all shipments of HVAC equipments or devices, but just to those certified by Energy Star. California was the U.S. state with plumbing and HVAC contractor establishment.

This repository contains the data released in the paper 'Transfer learning for galaxy feature detection: Finding Giant Star-forming Clumps in low redshift galaxies using Faster R-CNN' (DOI: 10.1093/rasti/rzae013).

We release a detailed catalogue of Giant Star-forming Clumps (GSFCs), detected for the full set of Galaxy Zoo: Clump Scout galaxies observed by SDSS using the Faster R-CNN architecture with the Zoobot classification-CNN as a feature extraction backbone.

The final models and code are made publicly available via Github: https://github.com/ou-astrophysics/Faster-R-CNN-for-Galaxy-Zoo-Clump-Scout.

We will release updates if needed via Zenodo versioning. We recommend using the latest version of this repository. You can check the version you are currently viewing on the right-hand sidebar.

Please cite the paper (DOI: 10.1093/rasti/rzae013) when using the data in this repository.

The csv-file FRCNN_Zoobot_SDSS_GZCS_detections.csv has the following columns. Alternatively, the file FRCNN_Zoobot_SDSS_GZCS_detections.gzip contains the same data but stored as a parquet-file.

This model is trained and used to detect star-forming clumps in nearby galaxies observed by CLAUDS and HSC SSP. The model is based on the Faster R-CNN architecture and uses the Zoobot galaxy morphology classification model (https://doi.org/10.21105/joss.05312) as its feature extraction backbone. The model differs also from terrestrial object detection models as it takes 6-channel imaging data as input, U-band from CLAUDS, GRIZY from HSC SSP.

The compressed archive contains example code for training the model and running detections on sample galaxy images. Included is also the data used to train the model and the final model weights.

The accompanying files include a Python Jupyter notebook (and associated data files read in by the Python code) that carry out the calculations described by Cranmer (2023), talk 246.05 presented at the 241st Meeting of the American Astronomical Society (AAS) in Seattle, Washington. The abstract of the talk is provided here:

There has always been interest in the perceived colors of the stars. They were key to the development of the H-R diagram, and they are also used widely in educational and public-outreach imagery. Thus, it is useful to develop software tools to compute these colors, as accurately as possible, from spectral energy distributions. This presentation follows up on an RNAAS paper (Cranmer 2021) that presented a collection of objective (CIE coordinate) and subjective (RGB triple) colors for main-sequence stars and brown dwarfs. A new empirical method of converting from CIE to RGB values is described, and results for various stellar spectra are presented. Although brown dwarfs over a wide range of effective temperatures (400 to 2000 K) emit most of their flux in the infrared, their visible spectra often exhibit a local maximum around a strong dip in the Na I cross section at 0.4-0.5 microns. Thus, they may appear purple to human eyes. Also, the hottest (O-type) main-sequence stars may appear even "bluer than the blue sky" because of Paschen continuum absorption. This presentation will update earlier stellar and brown-dwarf color estimates using more recently published synthetic spectra, and it will also investigate the effects of atmospheric absorption, over a range of air-mass values, on these perceived colors. Python Jupyter notebooks that carry out these calculations will be uploaded to the Zenodo repository for open-access distribution.

NOTE 1: The algorithms described here, for computing RGB triples, ought to be considered as preliminary results in ongoing research; i.e., they need additional testing and validation by comparing to the results of other more established ways of converting astronomical spectra to perceived colors.

NOTE 2: These files follow on from those provided in another Zenodo upload associated with the 2021 RNAAS paper: https://doi.org/10.5281/zenodo.5293307

object name: Small Sagittarius Star Cloud object type: star cloud coordinates: 18 16 48.0, -18 33 00 (2000) constellation: Sagittarius alternate names: Messier 24, IC 4715 observation filters: B, G, R file formats: FITS, PNG observation dates: 2020 June-September telescope: Mittelman Observatories 0.5m at New Mexico Skies

we propose to observe thermal emission from circumstellar dust grains around r crb type stars (rcbs) at their minimums using the sws in order to study the nucleation and grain formation around stars. our proposal consists of detecting the continuum emission due to carbon grains and searching for carbon cluster fullerenes (c60, c70, ...) around rcbs. rcbs are known to be hydrogendeficient, carbonrich stars and to undergo irregular decrease in light due to carbon grain formation. they provide a good opportunity to study the nucleation and the grain formation phenomena around stars because the condensible atoms are limited compared with that around normal carbon stars. furthermore, there is high possibility of detecting fullerenes around rcbs since the condition for the formation of carbon particles is similar to that for fullerene formation. although ubiquity of fullerenes is widely recognized on earth, they have not yet been discovered in space. this may be because fullerenes are not likely to be formed in the hydrogenrich atmosphere which is the case in normal carbon stars. rcbs are the best candidate at which fullerenes should be searched for. if one of our candidate stars shows decrease in light and will be observed with iso at that time, we also propose to take spectrum of this star again after the star recovers its light, if time allows. this spectrum together with the one taken at minimum can be used to determine the infrared properties of dust grains. whether fullerenes will be discovered or not, the spectroscopic data can be used to identify dust grains and to understand the nucleation and the grain formation in carbonrich environments. truncated!, Please see actual data for full text [truncated!, Please see actual data for full text]