https://github.com/Daniel-Mietchen/datascience

Keeping track of activities around research data
https://github.com/Daniel-Mietchen/datascience

data-science data-sharing open-data open-science research research-data research-data-management research-funding science-policy

Last synced: about 1 month ago
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Keeping track of activities around research data

• Host: GitHub
• URL: https://github.com/Daniel-Mietchen/datascience
• Owner: Daniel-Mietchen
• License: other
• Created: 2015-03-02T14:29:47.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2024-05-10T15:41:37.000Z (4 months ago)
• Last Synced: 2024-07-18T10:02:45.046Z (2 months ago)
• Topics: data-science, data-sharing, open-data, open-science, research, research-data, research-data-management, research-funding, science-policy
• Homepage:
• Size: 2.74 MB
• Stars: 31
• Watchers: 8
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Data science

This repo contains thoughts and activities around [research data](http://datascience.nih.gov/), with a focus on

* Analysis of different kinds of open models for use in research funding.

  * [How can research funding be opened up to the benefit of research?](https://github.com/Daniel-Mietchen/datascience/blob/master/open-research-funding.md)

  * [How can peer review be made more efficient?](https://github.com/Daniel-Mietchen/datascience/blob/master/peer-review.md)

* Interfacing of the Commons with activities across institutional, disciplinary and international boundaries.

  * [How to conceptualize and implement a Commons infrastructure for research?](https://github.com/Daniel-Mietchen/datascience/blob/master/commons.md)

  * [What policies govern open access to research outputs?](https://github.com/Daniel-Mietchen/datascience/blob/master/public-access-policies.md)

  * [How does free and open-source software fit into the picture?](https://github.com/Daniel-Mietchen/datascience/blob/master/open-source.md)

  * [How can data best be described in order to lay the groundwork for interoperability across data sources?](https://github.com/Daniel-Mietchen/datascience/blob/master/common-data-elements.md)

* Quantifying the reuse of various research objects, so as to gather information on what needs to be sustained.

  * [How are research objects being used and reused, and how can reuse be encouraged and quantified?](https://github.com/Daniel-Mietchen/datascience/blob/master/reuse.md)

  * [How best to cite data and software?](https://github.com/Daniel-Mietchen/datascience/blob/master/data-citation.md)

  * [How to make data accessible for people with disabilities?](https://github.com/Daniel-Mietchen/datascience/blob/master/web-accessibility.md)

  * [How to sustain infrastructure for data sharing?](https://github.com/Daniel-Mietchen/datascience/blob/master/sustainability.md)

* Engaging communities with data, both experts and in the framework of citizen science projects.

  * [How best to integrate crowdsourcing components into data management?](https://github.com/Daniel-Mietchen/datascience/blob/master/crowdsourcing.md)

  * [How to make data management plans machine readable?](https://github.com/Daniel-Mietchen/datascience/blob/master/data-management-plans.md)

  * [How can quality data journalism be supported?](https://github.com/Daniel-Mietchen/datascience/blob/master/data-driven-journalism.md)

as well as a collection of further relevant resources in the [reading room](https://github.com/Daniel-Mietchen/datascience/blob/master/reading-room.md).

For a quick overview of some of the key elements of data science, see [this infographic](http://web.archive.org/web/20181127210055/https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Infographics/8-easy-steps-to-become-a-data-scientist.jpg).