Open and reproducible neuroimaging: From study inception to publication
- File: data/review/fulltext/oa-id-W4295290221.pdf
- DOI: https://doi.org/10.1016/j.neuroimage.2022.119623
- OpenAlex ID: https://openalex.org/W4295290221
Other notes
- This paper is not much about open collaboration but more about which tools to use for open and reproducible research from start to finish of a project.
- Study inception, planning, and ethics: OSF pre-registration, clinicaltrials.gov, aspredicted
- Data processing and analysis: git, github, gitlab, etc
- Scientific research can now become more transparent, inclusive and collaborative throughout the research cycle
- Publishing Code of Conduct for collaborative projects is one practice that helps ensure a more welcoming and inclusive space for everyone regardless of background or identity
Ten simple rules for helping newcomers become contributors to open projects
- File: data/review/fulltext/oa-id-W2972809368.pdf
- DOI: https://doi.org/10.1371/journal.pcbi.1007296
- OpenAlex ID: https://openalex.org/W2972809368
General themes
- community building
- social rules
- organization around the collaboration
- new comers
Other notes
- Focus on building community: 1: be welcoming, 3. make governance explicit, 6. Develop forms of legitimate peripheral participation, 7. Make it easy for newcomers to get started.
Collaborative open science as a way to reproducibility and new insights in primate cognition research
- File: data/review/fulltext/oa-id-W4251805646.pdf
- DOI: https://doi.org/10.31234/osf.io/8w7zd
- OpenAlex ID: https://openalex.org/W4251805646
General themes
- collaboration structure
- tools
- organization
Other notes
- Focus on large-scale collaboration in psychology, because of a main challenge of small samples sizes.
- Project started in connection with a conference. First paper was a pilot. Main aim of the project is to provide infrastructure for large-scale collaboration. Used mailing list to share information, then slack for discussions. Use Google docs for writing and documentation and github for code and data. Data analysis plans pre-registered at OSF.
- Have a website - good for newcomers and sharing info. Topic for research selected through voting. After organizing the data, they had a modelling challenge. Also discuss challenges. Main one was how to make decisions.
- Leadership team preferable. Also have author guidelines.
UKRN Open Research Training Resources and Priorities Working Paper
- File: data/review/fulltext/oa-id-W4372403418.pdf
- DOI: https://doi.org/10.31219/osf.io/s2f6k
- OpenAlex ID: https://openalex.org/W4372403418
Other notes
- aim of the survey is to inform training priorities for open science
- research cycle: planning, conducting, analysing, disseminating
- Planning: team science guides, research co-production
- Conducting: open research testing platforms
- Most resources focus on the planning and analysing stage
- Many resources were only for internal use
- Very few resources for open collaboration, none were open and they were valued poorly compared to other practices
Eleven Strategies for Making Reproducible Research and Open Science Training the Norm at Research Institutions
- File: data/review/fulltext/oa-id-W4378611187.pdf
- DOI: https://doi.org/10.31219/osf.io/kcvra
- OpenAlex ID: https://openalex.org/W4378611187
General themes
- training
- recommendations
- organization
Other notes
- Strategy 5 - Perform replication or meta-research studies as course projects: As the class collaborates on one project, participants also build skills for collaborative team science and gain experience leading small teams.
- Tips for Strategy 5 - Perform replication or meta-research studies as course projects: Carefully define the scope of the project, Ensure that you have adequate support
- build communities: Foster accessible discussions (Consider running “beginner” and “advanced” community meetings), Build communities: Organize regular meetings
Collaboration and Open Science Initiatives in Primate Research
- File: data/review/fulltext/oa-id-W3183365357.pdf
- DOI: https://doi.org/10.31219/osf.io/7c93a
- OpenAlex ID: https://openalex.org/W3183365357
General themes
- large-scale collaboration
- organization
- tools
Other notes
- for them, making the large-scale collaboration was about overcoming challenges related to their research
- Large-scale collaborations can help to set standards in the field by following good scientific practice of replications, data reporting, and pre-registration
- Low entry barriers and the various ways in which a scientist can be involved in a large-scale collaboration (design, data collection, analysis, manuscript writing) offer a multitude of possibilities for researchers in different stages of their careers
- These types of projects also present valuable opportunities to make new contacts, initiate new collaborations, and connect with a bigger community on a regular basis
- Large-scale collaborations represent a wholesale shift in scientific practice, from how research questions are chosen and approached, to how research is logistically coordinated and how credit for research efforts is allocated
- large-scale collaborative projects may reduce the diversity of topics by covering them at a scale that would be unfeasible by independent research groups
- Agreeing on project selection, study design, stimuli, analysis plan, and findings’ interpretation is not easy in big groups. (…) research questions are selected democratically - proposals for projects are submitted and members vote to select which projects will be carried out.
- Large-scale projects also have to develop authorship guidelines, which specify the minimal conditions that one person has to fulfill to qualify for authorship
- none of the large-scale consortia in psychology have a steady source of financing
- setup the project to be long-lasting and build an infrastructure that they piloted
- task forces within the project prepared different materials they then uploaded to github and pre-register
Open and reproducible practices in developmental psychology research: The workflow of the WomCogDev lab as an example
- File: data/review/fulltext/oa-id-W4229452124.pdf
- DOI: https://doi.org/10.31234/osf.io/73bwu
- OpenAlex ID: https://openalex.org/W4229452124
Other notes
- Nice figure with workflow
- Resources: lab meetings, slack, trello, OSF repository
- They setup a clear structure for how to conduct the research - how a project is supposed to work
- Open Science is never static, as each of us continues to incorporate more and more practices into our repertoires over time
Accelerating addiction research via Open Science and Team Science
- File: data/review/fulltext/oa-id-W4383376256.pdf
- DOI: https://doi.org/10.31234/osf.io/pbkrx
- OpenAlex ID: https://openalex.org/W4383376256
Other notes
- mentions registered reports that are peer reviewer twice
- recommend sharing on OSF
- synthpop package in R creates a synthetic dataset with same statistical properties
- large-scale collaboration is recommended to improve generalizability and power.
- they provide an example of an individual-participant meta-analysis they conducted
- As part of this process, we made several decisions as a group (via an anonymous poll), such as which smallest effect size of interest to preregister and to which journal to submit the paper
- Barriers: within our current scientific culture, there are few incentives to work in large teams, all large-scale collaborations need leadership
Ten strategies to foster open science in psychology and beyond
- File: data/review/fulltext/oa-id-W4281886503.pdf
- DOI: https://doi.org/10.31234/osf.io/c38a2
- OpenAlex ID: https://openalex.org/W4281886503
General themes
- tools
- organization
- large-scale collaboration
Other notes
- Strategy 6: Collaborate with Others Using Open Tools
- can get started by writing in markdown/quarto, using github
- Strategy 7: Develop Networks of Open Collaboration
- Big Team Science has advantages for research, as it allows investigators to access more resources, work with greater sample sizes, take advantage of the expertise of a larger team of researchers in areas such as data analysis, and distribute work more efficiently
- Participating in these large-scale projects is not easy and calls on researchers to develop specific standards and guidelines that ensure effective communication among collaborators and enable projects to develop coherently and cohesively
- there is still a long way to go before open science becomes the by-design and by-default model for scientific research
UKRN ORCC Primer on Working in Open Research
- File: data/review/fulltext/oa-id-W4386723394.pdf
- DOI: https://doi.org/10.31219/osf.io/346hr
- OpenAlex ID: https://openalex.org/W4386723394
General themes
- not relevant. Is about working in open research not performing it
Lessons Learned: A Neuroimaging Research Center’s Transition to Open and Reproducible Science
- File: data/review/fulltext/oa-id-W4283836446.pdf
- DOI: https://doi.org/10.31219/osf.io/fe74t
- OpenAlex ID: https://openalex.org/W4283836446
Other notes
- also nice figure of workflow including open science practices in the workflow
- use OSF for registering protocols
- focus on open software like R and Python, git and github
- this is a practical guide to make a transition, very little mentioned specifically about collaboration or team work