Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-open-science
:books: :microscope: :unlock: :mag: A curated list of resources, data, tools, scholarship related to Open Access, Data and Open Science :books: :microscope: :unlock: :mag:
https://github.com/ZoranPandovski/awesome-open-science
Last synced: 5 days ago
JSON representation
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Research Data
- E-learning platform
- Finnish Social Science Data Archive
- BASE - Biefeld academic search engine
- Academia - Academia.edu is a platform for academics to share research papers
- Research Gate - ResearchGate is the professional network for scientists and researchers
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Research Articles and Reports
- Research Objects: Towards Exchange and Reuse of Digital Knowledge - Bechhofer et al., 2010
- The Enduring Value of Social Science Research: The Use and Reuse of Primary Research Data - Pienta et al., 2010
- The data paper: a mechanism to incentivize data publishing in biodiversity science - Chavan and Penev, 2011
- The Dataverse Network: An Open-Source Application for Sharing, Discovering and Preserving Data - Crosas, 2011
- Data sharing in neuroimaging research - Poline et al., 2012
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data sharing count: a publication-based solution
- EUDAT: A New Cross-Disciplinary Data Infrastructure for Science - Lecarpentier et al., 2013
- Data reuse and the open data citation advantage - Piwowar and Vision, 2013
- Nine simple ways to make it easier to (re)use your data - White et al., 2013
- The data sharing advantage in astrophysics - Dorch et al., 2015
- What Drives Academic Data Sharing? - Fecher et al., 2015
- From Peer-Reviewed to Peer-Reproduced in Scholarly Publishing: The Complementary Roles of Data Models and Workflows in Bioinformatics - Gonzalez-Beltran et al., 2015
- Making data count - Kratz and Strasser, 2015
- The center for expanded data annotation and retrieval - Musen et al., 2015
- Public Data Archiving in Ecology and Evolution: How Well Are We Doing? - Roche et al., 2015
- Achieving human and machine accessibility of cited data in scholarly publications - Starr et al., 2015
- The State of Open Data Report - Treadway et al., 2016
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- Towards coordinated international support of core data resources for the life sciences - Anderson et al., 2017
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud - Mons et al., 2017
- Code of practice for research data usage metrics release 1 - Fenner et al., 2018
- Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship - Atenas, Havemann, Priego, 2015
- Reproducibility, Virtual Appliances, and Cloud Computing
- The Ironic Effect of Significant Results on the Credibility of Multiple-Study Articles
- Power failure: why small sample size undermines the reliability of neuroscience
- Git can facilitate greater reproducibility and increased transparency in science
- Ten simple rules for reproducible computational research
- Investigating Variation in Replicability: A "Many Labs" Replication Project
- An introduction to Docker for reproducible research
- Opinion: Reproducible research can still be wrong: Adopting a prevention approach
- Replicability vs. reproducibility -- or is it the other way around?
- The GRIM test: A simple technique detects numerous anomalies in the reporting of results in psychology
- What does research reproducibility mean?
- Tools and techniques for computational reproducibility
- Transparency, Reproducibility, and the Credibility of Economics Research
- A trust approach for sharing research reagents
- Estimating the Reproducibility of Psychological Science
- Digital Open Science -- Teaching digital tools for reproducible and transparent research
- Terminologies for reproducible research
- The practice of reproducible research: case studies and lessons from the data-intensive sciences
- bookdown: Authoring Books and Technical Documents with R Markdown
- Our path to better science in less time using open data science tools
- Haves and Have nots must find a better way: The case for Open Scientific Hardware
- Computational Reproducibility via Containers in Social Psychology - Sherin, 2018
- Developer Survey Results 2019
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- Power failure: why small sample size undermines the reliability of neuroscience
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Toward interoperable bioscience data - Sansone et al., 2012
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data sharing count: a publication-based solution
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- Our path to better science in less time using open data science tools
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Investigating Variation in Replicability: A "Many Labs" Replication Project
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Making data count - Kratz and Strasser, 2015
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Toward interoperable bioscience data - Sansone et al., 2012
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Making data count - Kratz and Strasser, 2015
- Toward interoperable bioscience data - Sansone et al., 2012
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Power failure: why small sample size undermines the reliability of neuroscience
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
- Toward interoperable bioscience data - Sansone et al., 2012
- Making data count - Kratz and Strasser, 2015
- The FAIR Guiding Principles for scientific data management and stewardship - Wilkinson et al., 2016
- Towards coordinated international support of core data resources for the life sciences - Anderson et al., 2017
- A reputation economy: how individual reward considerations trump systemic arguments for open access to data - Fecher et al., 2017
- Our path to better science in less time using open data science tools
-
Tools
- Sci-Hub - Research Paper File Sharing Platform
- EOSC
- Journal of Open Research Software
- InsideDNA
- Galaxy - Reproducible Research environments
- Amazon Web Services - (AWS), Cloud-based software environments
- Open Science, Open Data, Open Source - Fernandes and Vos, 2017
- Choose an open source license
- Reproducibility Project: Cancer Biology
- Reproducibility Project: Psychological Science
- Registered Reports
- Reproducible Research, Workshop - CC-BY, April Clyburne-Sherin & Courtney Soderberg
- Reproducibility Workshop - Best practices and easy steps to save time for yourself and other researchers, [Code Ocean](https://codeocean.com/)
- ReproZip
- Software Carpentry
- Docker
- Vagrant
- nteract.io
- Binder Documentation - for creating custom computing environments that can be shared and used by multiple remote users
- Statcheck
- Scienceroot - the first blockchain-based scientific ecosystem
- Lando - A local development and DevOps tool for all your projects
- Podman - Podman is a daemonless container engine for developing, managing, and running OCI Containers on your Linux System
- PLOS open source toolkit channel
- Open Neuroscience
- Open Plant Science
- DocuBricks
- Hackaday.io
- Bio-protocol - a peer reviewed protocol journal
- BMJ Open Science - a new journal that aims to improve the transparency, integrity and reproducibility of biomedical research
- Evernote
- Labguru
- AsPredicted
- The Sci-Gaia Open Science Platform
- Improving your statistical inferences - Daniel Lakens
- Open Stats Lab - Kevin McIntyre
- R tutorial: Introduction to cleaning data with R - DataCamp
- Nextflow - open source tool than enables reproducible and portable computational workflows across cloud and clusters
- Reproducibility in Science: A guide to enhancing reproducibility in scientific results and writing
- PLOS open source toolkit channel
-
Organizations
- OpenScienceMOOC - Community organisation for the development of an Massive Open Online Community for Open Science
- Eclipse Science - Open source and collaboration for computational science.
- Mozilla Science - A community of researchers, developers, and librarians making research open and accessible.
- The Software Sustainability Institute - Cultivate better, more sustainable, research software to enable world-class research.
- Openscience.org - Supports the development of open scientific software, in particular for cheminformatics.
- 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.
-
Courses
-
Books
- Opening Science - The Evolving Guide on How the Web is Changing Research, Collaboration and Scholarly
- Top 10 Must-Read Books for Computer Science Majors
-
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