Entries added over the weekend, including eight recent submissions by software authors, pushed the number of records in the ASCL over the 3000 mark. Thanks for all the codes!
3K! Yay!
ASCL poster on software citation at AAS 241
All posters at the 241st meeting of the American Astronomical Society were iPosters: displayed on a screen instead of printed on paper or fabric. The ASCL’s iPoster is available for viewing in the iPoster Gallery; the image below is a static screenshot.
Why others might not be citing your astronomy software
Screenshot of ASCL iPoster at AAS 241
Your codes can themselves be cited, and you can choose your preferred citation method! So why aren’t people citing your code? Come find out, and also learn what five steps you can take to improve citation of the software you write.
In the past decade, software citation has accelerated in astrophysics, resulting in the field now having multiple ways to cite computational methods. Adding software metadata files, such as a CITATION.cff or a codemeta.json file, to the root directory of a GitHub repo (or other code storage site) lets others know how they should cite that software. Yet most software authors do not specify how they would like their code to be cited, while others specify a citation method that is not easily tracked (or tracked at all) by most indexers. In 2020, the Astrophysics Source Code Library (ASCL, ascl.net) sent authors of 135 codes software metadata files (CITATION.cff and codemeta.json), tailored to each computational method, and suggested that one of these files be edited as needed and included on their code site. In early 2021, we examined the code sites for these 135 entries and found that only 41% of these sites had citation information in any form available. In mid-2021, GitHub announced the integration of CITATION.cff into its service, making it easier to add this metadata file to one’s repo. Even so, as of January, 2023, 54% of the codes registered in the ASCL do not specify how to cite use of the software. The lack of citation information creates an obstacle for article authors to provide credit to software creators, thus hindering citation of and recognition for computational contributions to research and for the scientists who develop and maintain software.
#AAS241
December 2022 additions to the ASCL
Twenty-six codes were added to the ASCL in December, 2022:
AbundanceMatching: Subhalo abundance matching with scatter
BANZAI-NRES: BANZAI data reduction pipeline for NRES
Burning Arrow: Black hole massive particles orbit degradation
CONTROL: Colorado Ultraviolet Transit Experiment data reduction pipeline
Elysium: Observing black hole accretion disks
Eventdisplay: Analysis and reconstruction package for ground-based Gamma-ray astronomy
FastDF: Integrating neutrino geodesics in linear theory
GPry: Bayesian inference of expensive likelihoods with Gaussian processes
GWFAST: Fisher information matrix python package for gravitational-wave detectors
Hazma: Compute indirect detection constraints on sub-GeV dark matter
Infinity: Calculate accretion disk radiation forces onto moving particles
m2mcluster: Star clusters made-to-measure modeling
MGCosmoPop: Modified gravity and cosmology with binary black holes population models
MTNeedlet: Spherical maps filtering
Omega: Photon equations of motion
PACMAN: Planetary Atmosphere, Crust, and MANtle geochemical evolution
panco2: Pressure profile measurements of galaxy clusters
powspec: Power and cross spectral density of 2D arrays
PyMCCF: Python Modernized Cross Correlation Function for reverberation mapping studies
pyTANSPEC: Python tool for extracting 1D TANSPEC spectra from 2D images
sf_deconvolve: PSF deconvolution and analysis
SImMER: Stellar Image Maturation via Efficient Reduction
SourceXtractor++: Extracts sources from astronomical images
Spender: Neural spectrum encoder and decoder
Tranquillity: Creating black hole spin divergence plots
xwavecal: Wavelength calibrating echelle spectrographs
November 2022 additions to the ASCL
Twenty codes were added to the ASCL in November, 2022:
2DFFTUtils: 2DFFT Utilities implementation
AMBER: Abundance Matching Box for the Epoch of Reionization
APERO: A PipelinE to Reduce Observations
baobab: Training data generator for hierarchically modeling strong lenses with Bayesian neural networks
BiGONLight: Bi-local Geodesic Operators framework for Numerical Light propagation
BlackJAX: Library of samplers for JAX
EXCEED-DM: EXtended Calculation of Electronic Excitations for Direct detection of Dark Matter
fastSHT: Fast Spherical Harmonic Transforms
gsf: Grism SED Fitting package
H-FISTA: Phase retrieval for pulsar spectroscopy
KC: Analytical propagator with collision detection for Keplerian systems
Korg: 1D local thermodynamic equilibrium stellar spectral synthesis
mgcnn: Standard and modified gravity (MG) cosmological models classifier
ODNet: Asteroid occultation detection convolutional neural network
ovejero: Bayesian neural network inference of strong gravitational lenses
PAHDecomp: Decomposing the mid-IR spectra of extremely obscured galaxies
PDFchem: Average abundance of species from Av-PDFs
pmclib: Population Monte Carlo library
PTAfast: PTA correlations from stochastic gravitational wave background
unTimely_Catalog_explorer: A search and visualization tool for the unTimely Catalog
October 2022 additions to the ASCL
Thirty codes were added to the ASCL in October, 2022:
Blacklight: GR ray tracing code for post-processing Athena++ simulations
BornRaytrace: Weak gravitational lensing effects simulator
CK: Cloud modeling and removal
COMET: Emulated predictions of large-scale structure observables
cuvarbase: fast period finding utilities for GPUs
ExoRad2: Generic point source radiometric model
Faiss: Similarity search and clustering of dense vectors library
Finder_charts: Create finder charts from image data of various sky surveys
gbdes: DECam instrumental signature fitting and processing programs
iharm3d: Hybrid MPI/OpenMP 3D HARM with vectorization
ixpeobssim: Imaging X-ray Polarimetry Explorer simulator and analyzer
LavAtmos: Gas-melt equilibrium calculations for a given temperature and melt composition
LensingETC: Lensing Exposure Time Calculator
MCCD: Multi-CCD Point Spread Function Modelling
NEMESIS: Non-linear optimal estimator for multivariate spectral analysis
NIRDust: Near Infrared Dust finder for Type2 AGN K-band spectra
paltas: Simulation-based inference on strong gravitational lensing systems
PETSc: Portable, Extensible Toolkit for Scientific Computation
PGOPHER: Rotational, vibrational, and electronic spectra simulator
pixmappy: Python interface to gbdes astrometry solutions
POSYDON: Single and binary star population synthesis code
PSFr: Point Spread Function reconstruction
PSS: Pulsar Survey Scraper
PySME: Spectroscopy Made Easy reimplemented with Python
RADTRAN: General purpose planetary radiative transfer model
SHEEP: Machine Learning pipeline for astronomy classification
Solar-MACH: Multi-spacecraft longitudinal configuration plotter
SPINspiral: Parameter estimation for analyzing gravitational-wave signals
TSRecon: Time series reconstruction method of massive astronomical catalogs
tvguide: Observability by TESS
ASCL poster on software citation at ADASS XXXII
Are others using software you’ve written in their research and citing it as you want it to be cited? If not, this poster will help! Software can be cited in different ways, some good, and some not good at all for tracking and counting citations in indexers such as ADS and Google Scholar. Generally, indexers need to match citations to resources, such as journal articles, they ingest. There are several reasons why your code might not be cited well (in a trackable/countable way). One common reason is the lack of clear and explicit instructions on a code’s download site. Most astro code sites don’t list a preferred citation method! Make it easy for people to cite your software by listing a (good! trackable!) preferred citation method where others can easily find it. Creating a standard software metadata file, such as a CITATION.cff or codemeta.json, and adding it to the root of your code repo is easy to do with the ASCL’s metadata file creation overlay (see handout below), and will help out anyone wanting to give you credit for your computational method, whether it’s a huge carefully-written and tested package, or a short quick-and-dirty-but-oh-so-useful code.
#ADASSXXXI
Using the Astrophysics Source Code Library: Find, cite, download, parse, study, and submit
This morning, I gave a tutorial on the ASCL at ADASS XXXII, which is being held virtually from the University of Toronto and the University of Victoria. I’ll write more extensively about ADASS later this week; it is, as always, a fabulous conference with a lot of great work, talks, software, data, discussion, posters, chats, demos, tutorials… well, a lot! It’s my favorite astro conference.
But for now, slides from the tutorial and a link to the recording are below. Thanks to ADASS for accepting the proposal and to the participants for attending and for all the interesting (and occasionally scary!) comments and questions!
September 2022 additions to the ASCL
Twenty codes were added to the ASCL in September, 2022:
A-SLOTH: Semi-analytical model to connect first stars and galaxies to observables
AMBER: Fast pipeline for detecting single-pulse radio transients
Cluster Toolkit: Tools for analyzing galaxy clusters
DeepMass: Cosmological map inference with deep learning
FastQSL: Quasi-separatrix Layers computation method
GaLight: 2D modeling of galaxy images
GRUMPY: Galaxy formation with RegUlator Model in Python
Herculens: Differentiable gravitational lensing
HyPhy: Hydrodynamical Physics via Deep Generative Painting
KaRMMa: Curved-sky mass map reconstruction
libTheSky: Compute positions of celestial bodies and events
PINION: Accelerating radiative transfer simulations for cosmic reionization
RAPOC: Rosseland and Planck mean opacities calculator
SCORE: Shape COnstraint REstoration
SolTrack: Compute the position of the Sun in topocentric coordinates
SpectraPy: Extract and reduce astronomical spectral data
SynPopIO: Synthetic Population of Interstellar Objects
TauREx3: Tau Retrieval for Exoplanets
URILIGHT: Time-dependent Monte-Carlo radiative-transfer
wsynphot: Synthetic photometry package using quantities
August 2022 additions to the ASCL
Twenty-five codes were added to the ASCL in August 2022:
Asymmetric Uncertainty: Handling nonstandard numerical uncertainties
BlaST: Synchrotron peak estimator for blazars
CRPropa3: Simulation framework for propagating extraterrestrial ultra-high energy particles
CubeFit: Regularized 3D fitting for spectro-imaging data
DELIGHT: Identify host galaxies of transient candidates
EstrellaNueva: Expected rates of supernova neutrinos calculator
FFD: Flare Frequency Distribution
GSSP: Grid Search in Stellar Parameters
GStokes: Magnetic field structure and line profiles calculator
HOCHUNK3D: Dust radiative transfer in 3D
J-comb: Combine high-resolution and low-resolution data
LeXInt: Leja Exponential Integrators
POIS: Python Optical Interferometry Simulation
PyNAPLE: Automated pipeline for detecting changes on the lunar surface
qrpca: QR-based Principal Components Analysis
RadioLensfit: Radio weak lensing shear measurement in the visibility domain
RJ-plots: Automated objective classification of 2D structures
Scatfit: Scattering fits of time domain radio signals (Fast Radio Bursts or pulsars)
SPAMMS: Spectroscopic PAtch Model for Massive Stars
ThermoEngine: Thermodynamic properties estimator and phase equilibrium calculator
toise: Performance estimator for high-energy neutrino detectors
TOM Toolkit: Target and Observation Manager Toolkit
uvcombine: Combine images with different resolutions
VapoRock: Modeling magma ocean atmospheres and stellar nebula
Yonder: Data denoising and reconstruction
Downloading and exploring ASCL information using our API and ADS
Did you know you can download all the ASCL’s entries? You can! If you want all accepted entries: https://ascl.net/code/json
If you want particular fields, use an API query. This query brings up ASCL ID, title, bibcode, and when the entry was last updated.
https://ascl.net/api/search/?q=abstract:%22%22&fl=ascl_id,title,bibcode,time_updated
Want only entries that list a preferred citation? This will do it: https://ascl.net/api/search/?q=citation_method:%22.%22&fl=ascl_id,title,bibcode,citation_method
Find more info on our API here: https://github.com/teuben/ascl-tools/tree/master/API
If you’d like to know more about our metadata schema, please see this page: https://ascl.net/wordpress/about-ascl/metadata-schema/ 
You can also search ADS (https://ui.adsabs.harvard.edu) for ASCL entries by searching for bibstem:ascl.soft
https://ui.adsabs.harvard.edu/search/q=bibstem%3Aascl.soft&sort=date%20desc%2C%20bibcode%20desc&p_=0
Click the ADS Explore button on screen right and you can play with all kinds of info about ASCL entries, including citation metrics and concept clouds. Is there something else you’d like to see, another way you’d like ASCL entry data? Please let us know!