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"Open Science is the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of society, amateur or professional..." (Wikipedia)

This Awesome List is compiled in the effort to help new researchers find and learn about Open Science Tools.

[Awesome]([https://github.com/sindresorhus/awesome]) lists were started on GitHub by Sindre Sorhus. (Searchable Index)

Contents

Open Science FAQ

What is Open Science?

Wikipedia definition

UNESCO Recommendatio non Open Science

Open Science Manifesto

Six Pillars of Open Science

Open methodology

Open Source Software

Open Data

Open Access

Open Peer Review

Open Educational Resources

How do you define reproducible science?
Reproducibility and Replicability in Science by The National Academies 2019

National Academies Report 2019

Reproducibility means computational reproducibility—obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis.

Replicability means obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data.

Reproducibility vs Replicability by Plesser (2018)

In Reproducibility vs. Replicability, Hans Plesser gives the following useful definitions:

Repeatability (Same team, same experimental setup): The measurement can be obtained with stated precision by the same team using the same measurement procedure, the same measuring system, under the same operating conditions, in the same location on multiple trials. For computational experiments, this means that a researcher can reliably repeat her own computation.

Replicability (Different team, same experimental setup): The measurement can be obtained with stated precision by a different team using the same measurement procedure, the same measuring system, under the same operating conditions, in the same or a different location on multiple trials. For computational experiments, this means that an independent group can obtain the same result using the author’s own artifacts.

Reproducibility (Different team, different experimental setup): The measurement can be obtained with stated precision by a different team, a different measuring system, in a different location on multiple trials. For computational experiments, this means that an independent group can obtain the same result using artifacts which they develop completely independently.

The paper goes on to further simplify:

Methods reproducibility: provide sufficient detail about procedures and data so that the same procedures could be exactly repeated.

Results reproducibility: obtain the same results from an independent study with procedures as closely matched to the original study as possible.

Inferential reproducibility: draw the same conclusions from either an independent replication of a study or a reanalysis of the original study.

How many pillars of Open Science Are There?

Depending on what you find, the number generally ranges from 4 to 8

Commonly Identified Pillars

Data

FAIR Principles

CARE Principles

Code

Publications

Reviews

Education

What is Cloud Native Science?

Abernathey et al. (2021) propose three pillars of cloud native science

Three Pillars of Cloud Native Science

Analysis-Ready Data (ARD)

also Analyisis Ready Cloud Optimized (ARCO) formats, e.g., Cloud Optimized GeoTiff

Data-proximate Computing

also called server-side computing, allowing computations to happen remotely

Scalable Distributed Computing

the ability to modify the volume and number of resources used in a computational process.

Diagrams

flowchart LR

id1([open science]) --> id3([open publishing]) & id4([open data]) & id5([open tools])

id3([open publishing]) --> id41([open access]) & id42([open reviews])

id5([open tools]) --> id8([open repositories]) & id10([open services]) & id11([open workflows])

id8([open repositories]) --> id12([version control]) & id13([container registries])

id12([version control]) --> id101([compute])

id13([container registries]) --> id101([compute])

id14([public data registry]) --> id101([compute])

id10([open services]) --> id101([compute]) 

id11([open workflows]) --> id101([compute]) 

id4([open data]) --> id14([public data registry])

id101([compute]) <--> id15([on-prem]) & id16([commercial cloud]) & id17([public cloud])

id15([On-Prem]) <--> id20([open resources])

id16([Commercial Cloud]) <--> id20([open resources]) 

id17([Public Clouds]) <--> id20([open resources]) 

Figure: Hypothetical flow of open science tools


Last update: 2024-02-27