duecredit is being conceived to address the problem of inadequate citation of scientific software and methods, and limited visibility of donation requests for open-source software.
It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations.
You can already start "registering" citations using duecredit in your Python modules and even registering citations (we call this approach "injections") for modules which do not (yet) use duecredit. duecredit will remain an optional dependency, i.e. your software will work correctly even without duecredit installed.
For using duecredit in your software
-
copy
duecredit/stub.pyto your codebase, e.g.wget -q -O /path/tomodule/yourmodule/due.py \ https://raw.githubusercontent.com/duecredit/duecredit/master/duecredit/stub.pyNote that it might be better to avoid naming it duecredit.py to avoid shadowing installed duecredit.
-
Then use
duecreditimport due and necessary entries in your code asfrom .due import due, Doi, BibTeXto provide reference for the entire module just use e.g.
due.cite(Doi("1.2.3/x.y.z"), description="Solves all your problems", path="magicpy")To provide a reference for a function or a method, use dcite decorator
@due.dcite(Doi("1.2.3/x.y.z"), description="Resolves constipation issue") def pushit(): ...
References can also be entered as BibTeX entries
due.cite(BibTeX("""
@article{mynicearticle,
title={A very cool paper},
author={Happy, Author and Lucky, Author},
journal={The Journal of Serendipitous Discoveries}
}
"""),
description="Solves all your problems", path="magicpy")
We hope that eventually this somewhat cruel approach will not be necessary. But until other packages support duecredit "natively" we have provided a way to "inject" citations for modules and/or functions and methods via injections: citations will be added to the corresponding functionality upon those modules import.
All injections are collected under
duecredit/injections.
See any file there with mod_ prefix for a complete example. But
overall it is just a regular Python module defining a function
inject(injector) which will then add new entries to the injector,
which will in turn add those entries to the duecredit whenever the
corresponding module gets imported.
By default duecredit does exactly nothing -- all decorators do not
decorate, all cite functions just return, so there should be no fear
that it would break anything. Then whenever anyone runs their analysis
which uses your code and sets DUECREDIT_ENABLE=yes environment
variable or uses python -m duecredit, and invokes any of the cited
function/methods, at the end of the run all collected bibliography
will be presented to the screen and pickled into .duecredit.p file
in current directory:
$> python -m duecredit examples/example_scipy.py
I: Simulating 4 blobs
I: Done clustering 4 blobs
DueCredit Report:
- Scientific tools library / numpy (v 1.10.4) [1]
- Scientific tools library / scipy (v 0.14) [2]
- Single linkage hierarchical clustering / scipy.cluster.hierarchy:linkage (v 0.14) [3]
2 packages cited
0 modules cited
1 function cited
References
----------
[1] Van Der Walt, S., Colbert, S.C. & Varoquaux, G., 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), pp.22–30.
[2] Jones, E. et al., 2001. SciPy: Open source scientific tools for Python.
[3] Sibson, R., 1973. SLINK: an optimally efficient algorithm for the single-link cluster method. The Computer Journal, 16(1), pp.30–34.
Incremental runs of various software would keep enriching that file.
Then you can use duecredit summary command to show that information
again (stored in .duecredit.p file) or export it as a BibTeX file
ready for reuse, e.g.:
$> duecredit summary --format=bibtex
@article{van2011numpy,
title={The NumPy array: a structure for efficient numerical computation},
author={Van Der Walt, Stefan and Colbert, S Chris and Varoquaux, Gael},
journal={Computing in Science \& Engineering},
volume={13},
number={2},
pages={22--30},
year={2011},
publisher={AIP Publishing}
}
@Misc{JOP+01,
author = {Eric Jones and Travis Oliphant and Pearu Peterson and others},
title = {{SciPy}: Open source scientific tools for {Python}},
year = {2001--},
url = "http://www.scipy.org/",
note = {[Online; accessed 2015-07-13]}
}
@article{sibson1973slink,
title={SLINK: an optimally efficient algorithm for the single-link cluster method},
author={Sibson, Robin},
journal={The Computer Journal},
volume={16},
number={1},
pages={30--34},
year={1973},
publisher={Br Computer Soc}
}
and if by default only references for "implementation" are listed, we can enable listing of references for other tags as well (e.g. "edu" depicting instructional materials -- textbooks etc on the topic):
$> DUECREDIT_REPORT_TAGS=* duecredit summary
DueCredit Report:
- Scientific tools library / numpy (v 1.10.4) [1]
- Scientific tools library / scipy (v 0.14) [2]
- Hierarchical clustering / scipy.cluster.hierarchy (v 0.14) [3, 4, 5, 6, 7, 8, 9]
- Single linkage hierarchical clustering / scipy.cluster.hierarchy:linkage (v 0.14) [10, 11]
2 packages cited
1 module cited
1 function cited
References
----------
[1] Van Der Walt, S., Colbert, S.C. & Varoquaux, G., 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), pp.22–30.
[2] Jones, E. et al., 2001. SciPy: Open source scientific tools for Python.
[3] Sneath, P.H. & Sokal, R.R., 1962. Numerical taxonomy. Nature, 193(4818), pp.855–860.
[4] Batagelj, V. & Bren, M., 1995. Comparing resemblance measures. Journal of classification, 12(1), pp.73–90.
[5] Sokal, R.R., Michener, C.D. & University of Kansas, 1958. A Statistical Method for Evaluating Systematic Relationships, University of Kansas.
[6] Jain, A.K. & Dubes, R.C., 1988. Algorithms for clustering data, Prentice-Hall, Inc..
[7] Johnson, S.C., 1967. Hierarchical clustering schemes. Psychometrika, 32(3), pp.241–254.
[8] Edelbrock, C., 1979. Mixture model tests of hierarchical clustering algorithms: the problem of classifying everybody. Multivariate Behavioral Research, 14(3), pp.367–384.
[9] Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2), pp.179–188.
[10] Gower, J.C. & Ross, G., 1969. Minimum spanning trees and single linkage cluster analysis. Applied statistics, pp.54–64.
[11] Sibson, R., 1973. SLINK: an optimally efficient algorithm for the single-link cluster method. The Computer Journal, 16(1), pp.30–34.
The DUECREDIT_REPORT_ALL flag allows one to output all the references
for the modules that lack objects or functions with citations.
Compared to the previous example, the following output additionally
shows a reference for scikit-learn since example_scipy.py uses
an uncited function from that package.
$> DUECREDIT_REPORT_TAGS=* DUECREDIT_REPORT_ALL=1 duecredit summary
DueCredit Report:
- Scientific tools library / numpy (v 1.10.4) [1]
- Scientific tools library / scipy (v 0.14) [2]
- Hierarchical clustering / scipy.cluster.hierarchy (v 0.14) [3, 4, 5, 6, 7, 8, 9]
- Single linkage hierarchical clustering / scipy.cluster.hierarchy:linkage (v 0.14) [10, 11]
- Machine Learning library / sklearn (v 0.15.2) [12]
3 packages cited
1 module cited
1 function cited
References
----------
[1] Van Der Walt, S., Colbert, S.C. & Varoquaux, G., 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13(2), pp.22–30.
[2] Jones, E. et al., 2001. SciPy: Open source scientific tools for Python.
[3] Sneath, P.H. & Sokal, R.R., 1962. Numerical taxonomy. Nature, 193(4818), pp.855–860.
...
Problem: Scientific software is often developed to gain citations for original publication through the use of the software implementing it. Unfortunately such established procedure discourages contributions to existing projects and fosters new projects to be developed from scratch.
Solution: With easy ways to provide all-and-only relevant references for used functionality within a large(r) framework, scientific developers will prefer to contribute to already existing projects.
Benefits: As a result, scientific developers will immediately benefit from adhering to proper development procedures (codebase structuring, testing, etc) and already established delivery and deployment channels existing projects already have. This will increase efficiency and standardization of scientific software development, thus addressing many (if not all) core problems with scientific software development everyone likes to bash about (reproducibility, longevity, etc.).
Problem: Scientific software often, if not always, uses 3rd party libraries (e.g., NumPy, SciPy, atlas) which might not even be visible at the user level. Therefore they are rarely referenced in the publications despite providing the fundamental core for solving a scientific problem at hands.
Solution: With automated bibliography compilation for all used libraries, such projects and their authors would get a chance to receive adequate citability.
Benefits: Adequate appreciation of the scientific software developments. Coupled with a solution for "prima ballerina" problem, more contributions will flow into the core/foundational projects making new methodological developments readily available to even wider audiences without proliferation of the low quality scientific software.
sempervirens -- an experimental prototype for gathering anonymous, opt-in usage data for open scientific software. Eventually in duecredit we aim either to provide similar functionality (since we are collecting such information as well) or just interface/report to sempervirens.