ApJ overtakes MNRAS in number of ASCL citations

In looking at the ASCL dashboard yesterday, I realized that the Astrophysical Journal (ApJ) now has the most citations to ASCL entries, having overtaken (by a hair) Monthly Notices of the Royal Astronomical Society (MNRAS), which has held the top spot since at least 2015. MNRAS‘s early (and enduring) lead in software citations was initially the result of one editor, Dr. Keith T. Smith (now a senior editor at Science), who strongly encouraged article authors to cite the software they had used to generate their results. (Though I obviously saw the effect of his work in MNRAS, I had no idea one person was responsible for it until I saw a post in the Astronomers Facebook group and later queried Keith, who was unknown to me at the time.)

AAS Journals, which publishes ApJ, has three data editors, Greg Schwarz, August Muench, and Katie Merrell, who, though primarily consumed with data work, also encourage software citation, obviously to good effect. And so it grows!

April 2024 additions to the ASCL

Thirty codes were added to the ASCL in April, 2024:

astroNN: Deep learning for astronomers with Tensorflow
BayeSN: NumPyro implementation of BayeSN
binary_precursor: Light curve model of supernova precursors powered by compact object companions
cbeam: Coupled-mode propagator for slowly-varying waveguides
cudisc: CUDA-accelerated 2D code for protoplanetary disc evolution simulations

EBWeyl: Compute the electric and magnetic parts of the Weyl tensor
EffectiveHalos: Matter power spectrum and cluster counts covariance modeler
ExoPlex: Thermodynamically self-consistent mass-radius-composition calculator
GalMOSS: GPU-accelerated Galaxy Profile Fitting
GPUniverse: Quantum fields in finite dimensional Hilbert spaces modeler

jetsimpy: Hydrodynamic model of gamma-ray burst jet and afterglow
KCWIKit: KCWI Post-Processing and Improvements
LensIt: CMB lensing delensing tools
LEO-vetter: Automated vetting for TESS planet candidates
Mean_offset: Photometric image alignment with row and column means

mhealpy: Object-oriented healpy wrapper with support for multi-resolution maps
MLTPC: Machine Learning Telescope Pointing Correction
NbodyIMRI: N-body solver for intermediate-mass ratio inspirals of black holes and dark matter spikes
pAGN: AGN disk model equations solver
Panphasia: Create cosmological and resimulation initial conditions

PIPE: Extracting PSF photometry from CHEOPS data
PolyBin3D: Binned polyspectrum estimation for 3D large-scale structure
pyilc: Needlet ILC in Python
PySSED: Python Stellar Spectral Energy Distributions
RhoPop: Small-planet populations identifier

s2fft: Differentiable and accelerated spherical transforms
stringgen: Scattering based cosmic string emulation
superABC: Cosmological constraints from SN light curves using Approximate Bayesian Computation
TAT: Timing Analysis Toolkit for high-energy pulsar astrophysics
WignerFamilies: Compute families of wigner symbols with recurrence relations

Hand-crafted spam

Nearly every month, ASCL editors notify software authors that their code has been registered in the ASCL. Each editor sends out notifications for the code entries she worked on. I say “nearly every month” because I sometimes get behind in the prep work for the notifications, and that delays all editors who send out these notifications. Editors create their own processes for handling this correspondence.

I refer to my process as “hand-crafted spam.” I send out two types of notifications, one to authors who submitted their software to the ASCL, and another to authors for code entries I created. I have standard text into which I add, one by one, the necessary details for each email, by cutting and pasting the info from an Excel spreadsheet I create just for this purpose. I also check the emails against the ASCL entries to make sure I’ve got the right code and author. This sounds laborious, but it actually doesn’t take much time. This afternoon, I sent out 34 emails covering 39 code entries (some authors had two codes added to the ASCL) for code entries processed in February, March, and April, and it took me, working at a steady but unhurried pace, exactly one hour from the first missive to the last.

I’m putting this here to remind future me that this task goes pretty quickly, so, future me, do this more promptly!

March 2024 additions to the ASCL

Sixteen codes were added to the ASCL in March, 2024:

BTSbot: Automated identification of supernovae with multi-modal deep learning
CLASS-PT: Nonlinear perturbation theory extension of the Boltzmann code CLASS
DensityFieldTools: Manipulating density fields and measuring power spectra and bispectra
DistClassiPy: Distance-based light curve classification

FitCov: Fitted Covariance generation
fkpt: Compute LCDM and modified gravity perturbation theory using fk-kernels
kinematic_scaleheight: Infer the vertical distribution of clouds in the solar neighborhood
LtU-ILI: Robust machine learning in astro

MINDS: Hybrid pipeline for the reduction of JWST/MIRI-MRS data
OneLoopBispectrum: Computation of the one-loop bispectrum of galaxies in redshift space
Poke: Polarization ray tracing and Gaussian beamlet module for Python
pycorr: Two-point correlation function estimation

Pylians3: Libraries to analyze numerical simulations in Python 3
Pynkowski: Minkowski functionals and other higher order statistics
s4cmb: Systematics For Cosmic Microwave Background
URecon: Reconstruct initial conditions of N-Body simulations

February 2024 additions to the ASCL

Ten codes were added to the ASCL in February, 2024:

2cosmos: Monte Python modification for two independent instances of CLASS
CCBH-Numerics: Cosmologically-coupled-black-holes formation mass numerics
ECLIPSR: Automatically find individual eclipses in light curves, determine ephemerides, and more
MGPT: Modified Gravity Perturbation Theory code
NMMA: Nuclear Multi Messenger Astronomy framework

polarizationtools: Polarization analysis and simulation tools in python
Rfits: FITS file manipulation in R
Rwcs: World coordinate system transforms in R
SkyLine: Generate mock line-intensity maps
star_shadow: Analyze eclipsing binary light curves, find eccentricity, and more

January 2024 additions to the ASCL

Twenty codes were added to the ASCL in January, 2024:

baryon-sweep: Outlier rejection algorithm for JWST/NIRSpec IFS data
CosmosCanvas: Useful color maps for different astrophysical properties
CRR: Convex Ridge Regularizer
DARC: Dirac Atomic R-matrix Codes
deal.II: Finite element library

escatter: Electron scattering in Python
Harmonic: Learnt harmonic mean estimator
LoRD: Locate Reconnection Distribution
LoSoTo: LOFAR solutions tool
LUNA: Forward model luna simulator

maskfill: Fill in masked values in an image
ostrich: Surrogate modeling using PCA and Gaussian process interpolation
pyPETaL: A Pipeline for Estimating AGN Time Lags
QuantifAI: Radio interferometric imaging reconstruction with scalable Bayesian uncertainty quantification
Rayleigh: Pseudo-spectral MHD

SolarKAT: Solar imaging pipeline for MeerKAT
StructureFunction: Bayesian estimation of the AGN structure function for Poisson data
SYSNet: Neural Network modeling of imaging systematics in galaxy surveys
tidalspin: Constrain black hole spins using relativistic tidal forces properties
tomso: TOols for Models of Stars and their Oscillations

The ASCL at AAS 243: A Special Session and posters!

The ASCL is at the 243rd meeting of the American Astronomical Society (AAS), which is taking place in the major food destination New Orleans. In addition to tracking down beignets and bread pudding, ASCL team members have shared the stage with others in a Special Session and have presented iPosters. The Special Session was held on Monday afternoon; iPosters were presented on Monday and Tuesday evenings.

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Special Session: Into the Future: Building on 25 Years of Community Organization in Astro Software Development

Abstract: Over the past 25 years, astronomy has seen many changes involving research software development. These include improved transparency, improved software availability, and changes in journal policies. Formal recognition of community-based software development has increased through targeted funding, the establishment of new journals specifically focused on software, and code citation.

Changes in astro software development include the rise of open social coding sites such as GitHub and GitLab, the establishment and growth of conferences devoted to or including research software (such as ADASS, FORCE11, and the Research Data Alliance), and community-based training in software development (for example, The Carpentries and SciCoder) and exploration (for example, .dotastro and hack days) events.

This Special Session will look back at the community-driven work that has enabled some of these changes and look forward to future horizons for the software community in astronomy. Leaders of some of these community efforts will serve on an expert panel and will share their perspectives, after which the floor will be open for discussion with participants.

Speakers
Peter Teuben, University of Maryland, College Park: Introduction and Overview
Demitri Muna, Chief Science Data Office, NASA HQ: Software Training for Research Scientists: SciCoder and Other Efforts
Aarya Patil, Max Planck Institute for Astronomy: Building the AstroPy Community
Robert Nemiroff, Michigan Technological University: How and Why the Astrophysics Source Code Library Was Formed
Kimberly DuPrie, Space Telescope Science Institute: Lessons from Industry

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iPoster: Using the Astrophysics Source Code Library in the classroom
Alice Allen, Astrophysics Source Code Library; Kimberly DuPrie, Space Telescope Science Institute; Peter Teuben, University of Maryland, College Park; Robert Nemiroff, Michigan Technological University

Abstract: The Astrophysics Source Code Library (ASCL, ascl.net) is an online registry of source codes used in refereed astrophysics research. It currently lists over 3,300 codes and covers all aspects of computational astrophysics, and all of its public metadata about software can be downloaded. This presentation covers possible ways the ASCL can be used by educators and their graduate students. The ASCL serves as a discovery tool for codes that can be used for one’s own research. Graduate students can also investigate existing codes to see how common astronomical problems are approached numerically in practice, and use these codes as benchmarks for their own solutions to these problems. Further, they can deepen their knowledge of software practices and techniques through examination of others’ codes, and can use the ASCL’s data set for research on computational methods in astrophysics.

Screenshot of Using the Astrophysics Source Code Library in the Classroom iPoster

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iPoster: ASCL, ADS, and EMAC: Improving the visibility and citability of exoplanet research software
Alice Allen, Astrophysics Source Code Library; Alberto Accomazzi, Center for Astrophysics | Harvard & Smithsonian; Joe Renaud, UMD College Park / NASA Goddard.

Abstract: The Astrophysics Source Code Library (ASCL, ascl.net) is a free online registry for source codes of interest to astronomers, astrophysicists, and planetary scientists. It lists, and in some cases houses, software used in research that has appeared in, or been submitted to, peer-reviewed publications. It now has over 3300 software entries and is indexed by ADS and Clarivate’s Web of Science. In 2020, NASA created the Exoplanet Modeling and Analysis Center (EMAC, emac.gsfc.nasa.gov). Housed at the Goddard Space Flight Center, EMAC serves, in part, as a catalog and repository for exoplanet research resources. EMAC currently has 223 entries, 77% of which are for downloadable software. This presentation will cover the collaborative work the ASCL is doing with EMAC and with NASA’s Astrophysics Data System (ADS) to increase the discoverability and citability of EMAC’s software entries and to strengthen the ASCL’s and ADS’s ability to serve the planetary science community.

Screenshot of ASCL, ADS and EMAC: Improving the visibility and citability of exoplanet research software iPoster

December 2023 additions to the ASCL

Thirty-six codes were added to the ASCL in December, 2023:

21cmEMU: 21cmFAST summaries emulator
AM³: Astrophysical Multi-Messenger Modeling
BUQO: Bayesian Uncertainty Quantification by Optimization
C2-Ray: Time-dependent photo-ionization calculations
C2-Ray3Dm: 3D version of C2-Ray for multiple sources, hydrogen only
C2-Ray3Dm1D_Helium: Hydrogen + helium version of C2-Ray

CloudFlex: Small-scale structure observational signatures modeling
CompressedFisher: Library for testing Fisher forecasts
CosmoLED: Cosmo code for Large Extra Dimension (LED) black holes
DENSe: Bayesian density estimation for Poisson data
FORECAST: Realistic astronomical image and galaxy survey generator
gaia_tools: Tools for working with Gaia and related data sets

galclaim: GALaxy Chance of Local Alignment algorIthM
GravSphere: Jeans modeling code
GRFolres: Extension to GRChombo for modified gravity simulations
LimberJack.jl: Auto-differentiable methods for cosmology
LyaCoLoRe: Generate simulated Lyman alpha forest spectra
matvis: Fast matrix-based visibility simulator

PhotochemPy: 1-D photochemical model of rocky planet atmospheres
ProPane: Image warping and stacking utilities
PROSPECT: Profile likelihood for frequentist cosmological inference
PulsarX: Pulsar searching
pyC2Ray: Python interface to C2Ray with GPU acceleration
pycheops: Light curve analysis for ESA CHEOPS data

PyMsOfa: Python package for the Standards of Fundamental Astronomy (SOFA) service
PyRaTE: Non-LTE spectral lines simulations
RADIS: Fast line-by-line code for high-resolution infrared molecular spectra
Rainbow: Simultaneous multi-band light curve fitting
RRLFE: Metallicity calibrations for RR Lyrae variable stars
SAGE: Stellar Activity Grid for Exoplanets

smops: A sub-band model FITS image interpolator
SolarAxionFlux: Solar axion flux calculator for different solar models and opacity codes
SubGen: Fast subhalo sampler
SubGen2: Subhalo population generator
SUNBIRD: Neural-network-based models for galaxy clustering
The Farmer: Photometry routines for deep multi-wavelength galaxy surveys

ASCL at ADASS XXXIII: Broken bones and improving the visibility and citability of exoplanet research software

ADASS is my favorite conference; it’s so favored that… well, you’ll see …

ADASS was in Tucson this year. I decided to drive from Maryland to Tucson, figuring that was likely my best way to avoid getting/bringing COVID to the conference. It’s too long a drive to do in one day, so I stayed at a campsite outside Claremore, Oklahoma one night. It was lovely! Almost deserted, lots of birds, view of the lake… perfect! But alas, when making my coffee the next morning, I tripped and fell, breaking my right arm and nose and bashing up a few other body parts. I got patched up at a local hospital (excellent care!) and had surgery two days later (more excellent care!). I couldn’t drive, of course, so what to do, what to do? A friend offered to fly to Tulsa and drive me wherever I wanted to go, suggesting “back home” would be best. Where did I want to go? Tucson, of course! I did not want to miss ADASS!! So off to Tucson we went. Once I was deposited in a hotel there, she flew back to her home. (Now that’s a friend!! I’m forever grateful!!)

I gave an oral presentation at ADASS on “Improving the visibility and citability of exoplanet research software,” this about the work the ASCL is doing with NASA’s Exoplanet Modeling and Analysis Center (EMAC) and ADS. Links to my slides and proceedings pre-print are below.

ADASS did not disappoint! I’m so glad I attended it, and also attended the IVOA meeting immediately after. Everyone was helpful and kind, as I knew they would be. I stayed in Tucson until cleared to drive, and then drove myself the 2300 miles home. Not the trip (hahahaha) I was expecting, but it was the trip that was.

Slides
Proceedings paper