Awesome open science for software and computational science

awesome-open-science-software lists resources about open science and software:

• software as main experimental tool (aka computational methods)
• software as main study object (software engineering, programming language, systems software, ...).
• software support for open science (communication, data sharing, etc)

RSS feed: https://github.com/INRIA/awesome-open-science-software/commits/master.atom

Community

• Eclipse Science: open source and collaboration for computational science.
• Mozilla Science is a community of researchers, developers, and librarians making research open and accessible.
• Research Data Alliance (RDA) is a community-driven organization supported by the European Commission, the United States Government's National Science Foundation and National Institute of Standards and Technology, and the Australian Government’s Department of Innovation with the goal of building the social and technical infrastructure to enable open sharing of data.
• The Software Sustainability Institute's mission is to cultivate better, more sustainable, research software to enable world-class research.
• Openscience.org supports the development of open scientific software, in particular for cheminformatics.
• NumFOCUS Open-code, better science. Non-profit organization that promotes sustainable high-level programming languages, open code development, and reproducible scientific research.

Software as Experimental Tool

Reference documents (edit to add one):

• Result and Artifact Review and Badging (ACM)

Events (edit to add one):

• IEEE Workshop on The Future of Research Curation and Research Reproducibility (Nov 2016)
• Dagstuhl Workshop: Rethinking Experimental Methods in Computing (March 2016) (organized by D. Delling, C. Demetrescu, D. Johnson, J. Vitek)
• Dagstuhl Workshop: Artifact Evaluation for Publications (Nov 2015) (organized by B. Childers, G. Fursin, S. Krishnamurthi, A. Zeller).
• Dagstuhl Workshop: Engineering Academic Software Dagstuhl Workshop (June 2016) (organized by C. Goble, J. Howison, C. Kirchner, O. Nierstrasz).

Artifact review and evaluations (edit to add one):

• Artifact evaluation (artifact-eval.org) (maintained by Shriram Krishnamurthi) discusses availability of experimental data and reproducibility of experiments.
• Artifact Evaluation at ISSTA'16.
• The Reproducible Science Project (reproduciblescience.org) is a joint-project by New York University, the University of California, Berkeley and the University of Washington to promote open-science.
• The ReScience journal is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research is reproducible.

Papers (edit to add one):

• Enhancing reproducibility for computational methods (Science, Dec 2016, Vol. 354)
• Journal of Open Source Software - JOSS The Journal of Open Source Software (JOSS) is a journal for research software packages. JOSS papers enable citation of research software packages with a DOI.
• Streamlining the Inclusion of Computer Experiments In a Research Paper, IEEE Computer, vol 55. no. 11, November 2018.

Tools (edit to add one):

• Depsy helps build the software-intensive science of the future by promoting credit for software as a fundamental building block of science.
• CodeMeta The CodeMeta project proposes a way to have structured metadata for research software.
• code-ini Code meta data via .ini files

Software as Research Object

Software data curation and preservation (edit to add one):

• Software Heritage project collects and preserves software. The project motivation is that "software embodies our technical and scientific knowledge and humanity cannot afford the risk of losing it." (project lead by Roberto Di Cosmo).
• tera-PROMISE - Research dataset repository specializing in software engineering research datasets.
• Zenodo - operated by CERN, contains several collections about software data:
  • Software Engineering Artifacts Can Really Assist Future Tasks
  • Empirical Software Engineering
  • Mining Software Repositories
• Data Archiving and Networked Services - DANS (The Netherlands)

Others:

• Awesome-msr: curated repository of software engineering data sets
• The Evaluate Collaboratory (managed by Matthias Hauswirth) is a hub for everybody interested in understanding and improving the state of practice in experimental evaluation in software science.
• The Github group "opensciences" (initiated by Tim Menzies) contains the source of http://openscience.us/, which is an "open science for software engineering portal".
• SoftwareEngineeringToolDemos This Github group gathers software prototypes associated with software-engineering publications (maintained by Emerson Murphy-Hill and his students).

Software Support for Open Science

(edit to add one)

• IEEE DataPort is an open repository of datasets hosted by IEEE
• Dataverse A data repository framework to share and publish research data
• Code as a Research Object is a prototype integration between Github and figshare.
• dokieli is a client-side editor for decentralised article publishing, annotations and social interactions for science. Its source code is open source under the Apache License, Version 2.0. Maintained by Sarven Capadisli (Github) et al.
• Open Science Framework (OSF.io) provides project management support for researchers across the entire research lifecycle. Developed by the Center for Open Science.
• Anonymous Github is a system to anonymize open-science Github repositories before referring to them in a double-blind paper submission.
• recipy records provenance for Python programs
• codeocean Code Ocean is a cloud-based computational reproducibility platform to run scientific code
• LabPal is a Java library that allows you to design, control, process and package experiments that are run on a computer, and to streamline the integration of results within a research paper.

Courses

• Swedish PhD course "Tools for reproducible research". It gives very good pointers.
• Coursera MOOC on Reproducible Research