August 2014 additions to the ASCL

Twenty-three codes were added to the ASCL in August:

APS: Active Parameter Searching
bamr: Bayesian analysis of mass and radius observations
CosmoPhotoz: Photometric redshift estimation using generalized linear models
GALAPAGOS-C: Galaxy Analysis over Large Areas
GALIC: Galaxy initial conditions construction

HEAsoft: Unified Release of FTOOLS and XANADU
IIPImage: Large-image visualization
Imfit: A Fast, Flexible Program for Astronomical Image Fitting
LIA: LWS Interactive Analysis
LightcurveMC: An extensible lightcurve simulation program

NumCosmo: Numerical Cosmology
O2scl: Object-oriented scientific computing library
PhotoRApToR: PHOTOmetric Research APplication TO Redshifts
PIA: ISOPHOT Interactive Analysis
pieflag: CASA task to efficiently flag bad data

POET: Planetary Orbital Evolution due to Tides
RDGEN: Routines for data handling, display, and adjusting
Skycorr: Sky emission subtraction for observations without plain sky information
SPAM: Source Peeling and Atmospheric Modeling
VisiOmatic: Celestial image viewer

VPFIT: Voigt profile fitting program
vpguess: Fitting multiple Voigt profiles to spectroscopic data
WSClean: Widefield interferometric imager

July 2014 additions to the ASCL

Twenty codes were added to the ASCL in July:

AstroML: Machine learning and data mining in astronomy
ASTRORAY: General relativistic polarized radiative transfer code
BayesFlare: Bayesian method for detecting stellar flares
Brut: Automatic bubble classifier
CLE: Coronal line synthesis

e-MERLIN data reduction pipeline
Exopop: Exoplanet population inference
EZ_Ages: Stellar population age calculator
Halogen: Multimass spherical structure models for N-body simulations
kungifu: Calibration and reduction of fiber-fed IFU astronomical spectroscopy

MATLAB package for astronomy and astrophysics
MCMAC: Monte Carlo Merger Analysis Code
Period04: Statistical analysis of large astronomical time series
PINGSoft2: Integral Field Spectroscopy Software
SAMI: Sydney-AAO Multi-object Integral field spectrograph pipeline

SPECDRE: Spectroscopy Data Reduction
The Starfish Diagram: Statistical visualization tool
TWODSPEC: Long-slit and optical fiber array spectra extensions for FIGARO
VIDE: The Void IDentification and Examination toolkit
VStar: Variable star data visualization and analysis tool

A new site for the ASCL!

On Thursday, July 10, the ASCL’s new site, designed and developed by Judy Schmidt, was moved into production. Code entries are in a new, more flexible database; as a result, browsing is much more flexible, and back-end processing is greatly improved. We have retained WordPress for related content management and this blog, and the phpbb — the discussion forum — for announcements and discussion of individual codes.

I’ve embedded a presentation that highlights the major changes to the ASCL, but hope you will explore the site and click through it rather than click through the slides! Regardless of which you do, I hope you will click Leave a reply below to post your feedback and questions; please let us know what you think!

Thanks!

June 2014 additions to the ASCL

Twenty codes were added to the ASCL in June:

ASTROM: Basic astrometry program
ASURV: Astronomical SURVival Statistics
Autoastrom: Autoastrometry for Mosaics
CGS4DR: Automated reduction of data from CGS4
COCO: Conversion of Celestial Coordinates

CoREAS: CORSIKA-based Radio Emission from Air Showers simulator
FROG: Time-series analysis
GAUSSCLUMPS: Gaussian-shaped clumping from a spectral map
IRAS90: IRAS Data Processing
IRCAMDR: IRCAM3 Data Reduction Software

IUEDR: IUE Data Reduction package
JCMTDR: Applications for reducing JCMT continuum data in GSD format
MATCH: A program for matching star lists
PAMELA: Optimal extraction code for long-slit CCD spectroscopy
PERIOD: Time-series analysis package

POLMAP: Interactive data analysis package for linear spectropolarimetry
RV: Radial Components of Observer’s Velocity
STARMAN: Stellar photometry and image/table handling
TSP: Time-Series/Polarimetry Package
VADER: Viscous Accretion Disk Evolution Resource

May 2014 additions to the ASCL

Eighteen codes were added to the ASCL in May:

ATV: Image display tool
CURSA: Catalog and Table Manipulation Applications
DATACUBE: A datacube manipulation package
Defringeflat: Fringe pattern removal
DIPSO: Spectrum analysis code

ECHOMOP: Echelle data reduction package
ESP: Extended Surface Photometry
FLUXES: Position and flux density of planets
FORWARD: Forward modeling of coronal observables
HIIPHOT: Automated Photometry of H II Regions

LBLRTM: Line-By-Line Radiative Transfer Model
PHOTOM: Photometry of digitized images
PISA: Position Intensity and Shape Analysis
POLPACK: Imaging polarimetry reduction package
PROPER: Optical propagation routines

TelFit: Fitting the telluric absorption spectrum
The Hammer: An IDL Spectral Typing Suite
TRIPP: Time Resolved Imaging Photometry Package

March and April 2014 code additions

Twenty-six codes were added to the ASCL in March:

ASTERIX: X-ray Data Processing System
BAOlab: Image processing program
CCDPACK: CCD Data Reduction Package
CHIMERA: Core-collapse supernovae simulation code
computePk: Power spectrum computation

disc2vel: Tangential and radial velocity components derivation
GAIA: Graphical Astronomy and Image Analysis Tool
GPU-D: Generating cosmological microlensing magnification maps
GRay: Massive parallel ODE integrator
Inverse Beta: Inverse cumulative density function (CDF) of a Beta distribution

ISAP: ISO Spectral Analysis Package
JAM: Jeans Anisotropic MGE modeling method
KAPPA: Kernel Applications Package
KINEMETRY: Analysis of 2D maps of kinematic moments of LOSVD
Lightcone: Light-cone generating script

MGE_FIT_SECTORS: Multi-Gaussian Expansion fits to galaxy images
MLZ: Machine Learning for photo-Z
pyExtinction: Atmospheric extinction
RMHB: Hierarchical Reverberation Mapping
SLALIB: A Positional Astronomy Library

SOFA: Standards of Fundamental Astronomy
SURF: Submm User Reduction Facility
T(dust) as a function of sSFR
Unified EOS for neutron stars
Viewpoints: Fast interactive linked plotting of large multivariate data sets

YNOGKM: Time-like geodesics in the Kerr-Newmann Spacetime calculations

And seventeen codes were added to the ASCL in April:

AMBIG: Automated Ambiguity-Resolution Code
AST: World Coordinate Systems in Astronomy
CAP_LOESS_1D & CAP_LOESS_2D: Recover mean trends from noisy data
carma_pack: MCMC sampler for Bayesian inference
Comet: Multifunction VOEvent broker

LTS_LINEFIT & LTS_PLANEFIT: LTS fit of lines or planes
macula: Model of rotational modulations of a spotted star
RegPT: Regularized cosmological power spectrum
SAS: Science Analysis System for XMM-Newton observatory
SER: Subpixel Event Repositioning Algorithms

SpecPro: Astronomical spectra viewer and analyzer
Spextool: Spectral EXtraction tool
TORUS: Radiation transport and hydrodynamics code
TTVFast: Transit timing inversion
VictoriaReginaModels: Stellar evolutionary tracks

WFC3UV_GC: WFC3 UVIS geometric-distortion correction
ZDCF: Z-Transformed Discrete Correlation Function

Changes to the ASCL

Improvements are coming to the ASCL; we don’t have a firm timeline yet but expect to have the majority of changes made well before the end of the year. The presentation below shows screenshots of the changes; we hope you like what you see.

The biggest changes are that code entries will move from the APOD discussion forum and will be housed in a new database. We have been running the new database in parallel with the existing ASCL and are getting closer to putting the new database into production. We are integrating our current technologies — this WordPress site for our general information and blog and the phpbb for announcements and discussion for individual codes — into our new infrastructure as well.

Current URLs for code entries will continue to work after implementation of the new system. We will likely be making changes in several phases, and will announce them before and after here and on our social media sites.

Please let us know what you think; thanks!

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Creating and evaluating data management plans

I’m delighted to offer the following guest post by Jonathan Petters, Data Management Consultant, Johns Hopkins Data Management Services, and thank him very much for it!

Funding agencies have long encouraged and expected that data and code used in the course of funded research be made available to those in the research discipline.In a recent discussion on preservation and sharing of research data, a few participants expressed their concern (paraphrased here) that “My research community doesn’t know how to create a quality data management plan” and “We don’t know how to evaluate data management plans.” The astronomy community explicitly requested a little guidance. We in Johns Hopkins University Data Management Services have developed a few resources, described below, of use in both developing and evaluating data management plans within all research disciplines, including astronomy.

Funding agencies have long encouraged and expected that data and code used in the course of funded research be made available to those in the research discipline. NSF is an important funder of astronomical research that has such expectations (and the agency I will focus on here). A few years ago NSF began requiring data management plans as part of research proposal, in part to aid in the dissemination and sharing of research data and code. Following a February 2013 Office of Science and Technology Policy memo other US funding agencies are expected to follow suit with similar data management plan requirements, including the Department of Energy’s Office of Science.

What does NSF say about writing and evaluating quality data management plans? A good overview of NSF data policies relevant for the AST community can be found in these slides from Daniel Katz, NSF). In general the National Science Foundation (NSF) states that data management will be defined by “the communities of interest.” The NSF AST-specific policy further states “MPS Divisions will rely heavily on the merit review process in this initial phase to determine those types of plan that best serve each community and update the information accordingly.” Neither statement is especially prescriptive and can leave researchers unclear as to what they should do.

Creating a plan
While effective research data management certainly has community- and discipline-specific attributes, there ARE aspects of effective data management that are generalizable across research disciplines. It is around these general aspects that we in Johns Hopkins University Data Management Services (JHUDMS) devised our Data Management Planning Questionnaire. We work through this questionnaire with researchers at Johns Hopkins to help them create effective data management plans.

The Questionnaire is designed to comprehensively hit upon the important aspects of effective research data management (e.g. data inputs/outputs in the research, ethical/legal compliance, standards and formats used, intended sharing and preservation, PI restrictions on the use of the data).  By answering the applicable questions in the document, removing the questions/front matter and connecting the answers in each section into paragraphs, a researcher would be well on their way to a quality, well thought-out data management plan.

Two relevant side-notes:
1.)   For the Questionnaire we consider code and software tools as one ‘kind’ of research data; thus analysis or simulation codes used in the course of your proposed research should be included as a Data Product. While research code and research data generated or processed by code are clearly NOT the same, there are many similarities in managing the two. In both cases effective management should include consideration of documentation, licensing, formats, associated metadata, and upon what platform(s) the data or code could be shared.

2.)   Astronomy, as in other disciplines, conducts a substantial amount of research through large collaborations (e.g. surrounding HST or SDSS data). In these cases it is typical for investments in research data infrastructure to be made, and data policies/practices to be defined for those working with the data. Citing those policies and practices in a data management plan would be appropriate.

Screenshot of Reviewer Guide and Worksheet for Data Management Plans

Screenshot of Reviewer Guide and Worksheet for Data Management Plans

Evaluating a plan
To help researchers evaluate data management plans for their quality, my colleagues developed the Reviewer Guide and Worksheet for Data Management Plans (dotx). This Guide and Worksheet is a complement to our Questionnaire; it is a handy checklist by which a grant reviewer can determine whether a researcher thoroughly considered the important aspects of research data management.

For those who researchers saying to themselves, “The Questionnaire and Reviewer Guide are nice, but PLEASE just tell me what to do!!!”, I found two tweets from the code sharing session at the latest (223rd) AAS meeting in January to be quite relevant (h/t August Muench and Lucianne Walkowicz):

Who enforces software/data sharing in astronomy? YOU DO! WE DO! PEER REVIEW DOES! not snf/nasa #aas223 #astroCodeShare It's UP TO YOU to include good data management plan as part of panel reviews. The community must enforce importance. #aas223 #astroCodeShare

I wholeheartedly agree with both tweets. It is up to the research community members to police and enforce the data management and sharing practices they would like to see in their community. That’s how peer review works! So the next time you review astronomical research proposals, look over the data management plans carefully and bring up relevant thoughts and concerns to the review panel.

Summing up
I hope the Data Management Planning Questionnaire and Reviewer Guide and Worksheet for Data Management Plans help you and other researchers in the astronomy community more fully develop expectations for data management and sharing practices. It’s likely your institution also has research data management personnel (like the JHUDMS at Hopkins) who are more than happy to help!

Code citation news, info, and commentary

Mozilla Science Lab, GitHub and Figshare team up to fix the citation of code in academia
The Mozilla Science Lab, GitHub and Figshare – a repository where academics can upload, share and cite their research materials – is starting to tackle the problem. The trio have developed a system so researchers can easily sync their GitHub releases with a Figshare account. It creates a Digital Object Identifier (DOI) automatically, which can then be referenced and checked by other people.

Discussion of the above article on YCombinator
…it always make me cringe when privately held companies want to define an “open standard” for scientific citations that (surprise!) relies completely on their proprietary infrastructure. I still remember the case of Mendeley, which promised to build an open repository for research documents, and which is now a subsidiary of Elsevier, an organization that does not really embrace “open science”, to put it mildly.

Tool developed at CERN makes software citation easier
Researchers working at CERN have developed a tool that allows source code from the popular software development site GitHub to be preserved and cited through the CERN-hosted online repository Zenodo….
Now, people working on software in GitHub will be able to ensure that their code is not only preserved through Zenodo, but is also provided with a unique digital object identifier (DOI), just like an academic paper.

Webcite
WebCite is an on-demand archiving system for webreferences (cited webpages and websites, or other kinds of Internet-accessible digital objects), which can be used by authors, editors, and publishers of scholarly papers and books, to ensure that cited webmaterial will remain available to readers in the future.

DOIs unambiguously and persistently identify published, trustworthy, citable online scholarly literature. Right?
So DOIs unambiguously and persistently identify published, trustworthy, citable online scholarly literature. Right? Wrong.
The examples above are useful because they help elucidate some misconceptions about the DOI itself, the nature of the DOI registration agencies and, in particular issues being raised by new RAs and new DOI allocation models.

February 2014 code additions

Thirty-five codes were added to the ASCL in February:

Aladin Lite: Lightweight sky atlas for browsers
ANAigm: Analytic model for attenuation by the intergalactic medium
ARTIST: Adaptable Radiative Transfer Innovations for Submillimeter Telescopes
astroplotlib: Astronomical library of plots
athena: Tree code for second-order correlation functions

BAOlab: Baryon Acoustic Oscillations software
BF_dist: Busy Function fitting
CASSIS: Interactive spectrum analyzer
Commander 2: Bayesian CMB component separation and analysis
CPL: Common Pipeline Library

Darth Fader: Galaxy catalog cleaning method for redshift estimation
DexM: Semi-numerical simulations for very large scales
FAMA: Fast Automatic MOOG Analysis
GalSim: Modular galaxy image simulation toolkit
Glue: Linked data visualizations across multiple files

gyrfalcON: N-body code
HALOFIT: Nonlinear distribution of cosmological mass and galaxies
HydraLens: Gravitational lens model generator
KROME: Chemistry package for astrophysical simulations
libsharp: Library for spherical harmonic transforms

MGHalofit: Modified Gravity extension of Halofit
Munipack: General astronomical image processing software
P2SAD: Particle Phase Space Average Density
PyGFit: Python Galaxy Fitter
PyVO: Python access to the Virtual Observatory

PyWiFeS: Wide Field Spectrograph data reduction pipeline
QUICKCV: Cosmic variance calculator
QuickReduce: Data reduction pipeline for the WIYN One Degree Imager
SPLAT-VO: Spectral Analysis Tool for the Virtual Observatory
SPLAT: Spectral Analysis Tool

TARDIS: Temperature And Radiative Diffusion In Supernovae
UVMULTIFIT: Fitting astronomical radio interferometric data
Vissage: ALMA VO Desktop Viewer
wssa_utils: WSSA 12 micron dust map utilities
XNS: Axisymmetric equilibrium configuration of neutron stars