Datasets:

Smith42
/

ascl-code

ascl_id stringclasses 752 values	repo_url stringclasses 737 values	file_path stringlengths 4 194	content large_stringlengths 1 1M	language stringclasses 29 values	license_type stringclasses 24 values	size int64 1 1M
[ascl:2306.019]	https://github.com/realfastvla/realfast	.travis.yml	language: python python: - 2.7 - 3.6 branches: only: - main - development install: - sudo apt-get update -y - sudo apt-get install -y libfftw3-dev # set up conda - wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh - bash miniconda.sh -b -p $HOME/minicon...	YAML	BSD	1,017
[ascl:2306.019]	https://github.com/realfastvla/realfast	README.md	# realfast Realfast is the name of a project and software package related to radio interferometric data analysis at the Very Large Array. For more information, see [realfast.io](http://realfast.io) or visit the [VLA](https://public.nrao.edu/telescopes/vla/) web site. This repo includes a Python 3 application that int...	Markdown	BSD	2,874
[ascl:2306.019]	https://github.com/realfastvla/realfast	setup.py	from setuptools import setup, find_packages import glob setup( name='realfast', description='Real-time data analysis at the VLA', author='Casey Law and the realfast team', author_email='caseyjlaw@gmail.com', version='3.6.12', url='http://realfast.io', include_package_data=True, packages...	Python	BSD	848
[ascl:2306.019]	https://github.com/realfastvla/realfast	scripts/rf_get_cand.py	#! /usr/bin/env python import sys import os import json import astropy.coordinates import astropy.units as u import sdmpy from elasticsearch import Elasticsearch # This script takes a realfast portal candidate ID, and # assembles the SDM, BDF and PNG files into the current # directory. It then modifies the SDM Annot...	Python	BSD	2,779
[ascl:2306.019]	https://github.com/realfastvla/realfast	scripts/rfarchive.sh	#!/usr/bin/env bash MODE=$1 SDMNAME=$2 if [ $MODE == "create" ]; then ssh rfr "conda activate development; realfast buildsdm --indexprefix final --copybdf --sdmname $SDMNAME; rsync -rL --remove-source-files ${SDMNAME} claw@nmpost-master:~/fasttransients/realfast/tmp ; find ${SDMNAME} -type d -empty -delete" elif [...	Shell	BSD	562
[ascl:2306.019]	https://github.com/realfastvla/realfast	scripts/rfarchive.py	import os.path from subprocess import Popen, PIPE import shutil import pickle from realfast import elastic from time import sleep from astropy.time import Time # get all Ids Ids = elastic.get_ids('finalcands', caseyjlaw_tags="astrophysical,archive") # any project code filters can be added here # Ids = [Id for Id in I...	Python	BSD	2,516
[ascl:2306.019]	https://github.com/realfastvla/realfast	scripts/archive.sh	#!/usr/bin/env bash SDMNAME=$1 PROFILE=$2 # can be dsoc-test or dsoc-prod ssh rfr "conda activate deployment; cd lustre_workdir; realfast buildsdm --indexprefix final --sdmname "${SDMNAME}"; ~/soft/sdmpy/scripts/realfast_sdm_fix.py "${SDMNAME}"; rsync -aL --bwlimit=20m --remove-source-files "${SDMNAME}".fix claw@nmp...	Shell	BSD	572
[ascl:2306.019]	https://github.com/realfastvla/realfast	conf/realfast.yml	rfpipe: default: nthread: 2 # not taking all in case multiple workers going dtarr: [1] # integer to integrate in time for independent searches maxdm: 100 flagantsol: True timesub: 'mean' searchtype: 'image' # sigma_image1: 6.4 # sigma_kalman: 0. npix_max: 2048 badspwpol: 2. ...	YAML	BSD	7,498
[ascl:2306.019]	https://github.com/realfastvla/realfast	conf/realfastdev.sh	#!/bin/bash . ~/anaconda/etc/profile.d/conda.sh conda activate development36 cd /lustre/evla/test/realfast exec realfast run --mode development	Shell	BSD	144
[ascl:2306.019]	https://github.com/realfastvla/realfast	conf/env_realfast.yml	name: development36 channels: - anaconda - conda-forge - pkgw-forge - defaults dependencies: - _tflow_select=2.1.0=gpu - absl-py=0.7.1=py36_0 - astor=0.7.1=py36_0 - atk=2.25.90=hf2eb9ee_1001 - blas=2.10=openblas - boost=1.68.0=py36h8619c78_1001 - boost-cpp=1.68.0=h11c811c_1000 - bzip2=1.0.6=h14c...	YAML	BSD	6,574
[ascl:2306.019]	https://github.com/realfastvla/realfast	conf/realfast.sh	#!/bin/bash . ~/anaconda/etc/profile.d/conda.sh conda activate deployment3 cd /lustre/evla/test/realfast exec realfast run --mode deployment	Shell	BSD	141

End of preview. Expand in Data Studio

ASCL Astronomy Source Code

The Astrophysics Source Code Library (ASCL) is a curated registry of source code used in astronomy and astrophysics research. This dataset contains source files extracted from ASCL-listed repositories, paired with catalog metadata.

Dataset Structure

Manifest (`manifest.parquet`)

One row per ASCL catalog entry with the following fields:

Field	Description
`ascl_id`	ASCL identifier (e.g., `[ascl:2306.019]`)
`title`	Software title
`authors`	Author list
`description`	Abstract / description from ASCL
`detail_url`	ASCL detail page URL
`repo_url`	GitHub/GitLab/Bitbucket URL (if found)
`code_site`	Project homepage URL
`ads_url`	ADS bibcode URL
`license_type`	Detected license (e.g., MIT, GPL-3.0)
`license_file`	Path to license file in repo

Source Code (`code/*.parquet`)

Stack-style source files extracted from cloned repositories (one row per file):

Field	Description
`ascl_id`	ASCL identifier
`repo_url`	Source repository URL
`file_path`	Relative path within repo
`content`	File text content
`language`	Detected programming language (from file extension)
`license_type`	License detected from the repository
`size`	File size in bytes

Data Collection Methodology

Phase 1: Catalog Scrape

The ASCL catalog is scraped to extract metadata for each entry: title, authors, description, repository URLs, and ADS bibcode links. Only entries with a repository URL on GitHub, GitLab, or Bitbucket proceed to Phase 2.

Phase 2: Code Extraction

Each repository is shallow-cloned (--depth 1), its license file is detected and classified via regex pattern matching, and all recognised source files are extracted into Parquet batches. Language detection uses file extension mapping (Python, C, C++, Fortran, Julia, R, MATLAB/Octave, IDL, Java, Rust, Go, JavaScript, Shell, and others).

Limitations

Repository coverage: only repos hosted on GitHub, GitLab, or Bitbucket are included; code distributed via tarballs, personal websites, or other non-git hosting is skipped.
Shallow clones only: only the latest commit is captured — no version history.
Language detection is extension-based: file extensions are mapped to languages; there is no content-based language classification.
License detection is regex-based: licenses are identified by pattern matching against common license file names and text; unusual or custom licenses may be misclassified or reported as Unknown.
No deduplication: if multiple ASCL entries point to the same repository, its files may appear more than once.

Licensing

This is a multi-license dataset. Each row carries a license_type field indicating the license detected for that repository. Individual source files retain their original licenses as set by their authors. Catalog metadata originates from ASCL.

Usage

from datasets import load_dataset

# Load catalog metadata
ds_manifest = load_dataset("Smith42/ascl-code", data_files="manifest.parquet")

# Load source code files
ds_code = load_dataset("Smith42/ascl-code", data_files="code/*.parquet")

# Filter to a specific license
mit_code = ds_code["train"].filter(lambda x: x["license_type"] == "MIT")

# Filter to Python files
python_code = ds_code["train"].filter(lambda x: x["language"] == "Python")