Understanding Open Science 

Read the FABBS Statement on Open Science

What is Open Science?

Open science involves a number of practices that increase the credibility, replicability, and availability of research and data. Ideally, scholars should strive to make their work Findable, Accessible, Interoperable (easily shared, used, and understood by other systems and researchers), and Reusable (FAIR) during and after conducting research.  

Open access involves making research publications and pre-prints publicly available rather than keeping them behind publisher paywalls or siloed within their institution’s department. Open access can also involve making a study’s materials, measures, and data (that is stripped of personally identifiable information) publicly available through uploading them to storage databases known as repositories.  

• View NASEM’s Open Science by the Numbers Infographic for statistics highlighting Open Science successes. 
• Read Barriers and Facilitators to Qualitative Data Sharing in the United States: A Survey of Qualitative Researchers from PLOS ONE which surveys researchers on the obstacles of qualitative data sharing. 

• Read the Promises and Pitfalls of Data Sharing in Qualitative Research for a description of the critical concerns around qualitative data sharing as well as possible solutions. 
• Read Data Sharing and Community-Engaged Research: Data Sharing Must be Accompanied by Responsibility Sharing for rationales, goals, and concerns of data sharing and community-engaged research. 
• Read Top Cogn Sci’s What Should be the Data Sharing Policy of Cognitive Science? which describes the responsibilities, benefits, and concerns of open science practices for scientists. It also provides suggestions to establish a data sharing policy within the cognitive science community.

Data Management

Data management involves handling, organizing, storing, and maintaining data generated or used during a research project. Done well, it can streamline the research process and can also help to ensure that research data is credible, accessible, and reproducible.

Effective data management promotes efficient resource utilization, data sharing and accessibility, and reproducibility and replicability:

Efficient resource utilization: Well-organized data management practices save time and resources by streamlining the research process, minimizing the risk of data loss, and enabling efficient data retrieval and analysis. 

Data sharing and accessibility: Proper data management facilitates the sharing of research data with other researchers and the wider scientific community. This promotes transparency, collaboration, and knowledge advancement by allowing others to access, verify, and build upon existing research findings. 

Reproducibility and replicability: Data management practices, including thorough documentation and metadata, help ensure that data can be accurately interpreted and analyzed by others.  

Reproducibility, Replication, and Preregistration

Reproducibility refers to the ability of a researcher to obtain the same results as the original study by following the exact same methodology and using the same data. This concept emphasizes the importance of providing a transparent and detailed description of the research process, including the data collection, analysis, and interpretation methods. Reproducibility helps to ensure that the findings are not due to errors or biases in the research process, but rather a result of the procedures and data used. 

Replicability, on the other hand, refers to the ability of a researcher to obtain similar results as the original study by conducting an independent study with a different sample and, potentially, slightly different methodologies. This concept highlights the importance of the generalizability of research findings. Replicability helps to confirm that the original findings are not due to chance or specific to a particular sample, but rather can be generalized to a broader population or context. 

• Read the report Reproducibility and Replication in Science by the National Academies of Science, Engineering, and Medicine (NASEM) for factors preventing reproducibility and replication. The report also provides recommendations to improve rigor within research. 

Preregistration is the process of submitting a detailed research plan, including hypotheses, methods, and analysis strategies, to a public registry before conducting a study. This plan is time-stamped and cannot be modified once submitted, which encourages researchers to think critically about their study design, hypotheses, and analysis strategies before conducting the study. This promotes more rigorous and well-planned research, which contributes to the overall quality and robustness of scientific findings. 

Preregistration also reduces publication bias, p-hacking, and HARKing: 

Publication bias: By documenting research plans in advance, preregistration encourages the publication of all results, regardless of whether they are statistically significant or in line with prior expectations. This reduces publication bias and provides a more accurate representation of the true state of scientific knowledge. 

P-hacking and HARKing: Preregistration helps to prevent questionable research practices such as p-hacking (selectively reporting analyses that yield significant results) and HARKing (hypothesizing after the results are known). By specifying the planned analyses beforehand, researchers are held accountable for conducting and reporting their study as planned, which strengthens the credibility of research findings. 

• Read An Excess of Positive Results: Comparing the Standard Psychology Literature with Registered Reports for an explanation of the rates of “supported” hypotheses between standard published and pre-registered reports. 

Repositories

It is important to select a data repository that is suitable for your research data and that helps ensure the reliability, security, and accessibility of data for the broader research community.   

In general, a good data repository is:  

1. Domain-specific: Repositories for specific research fields or disciplines may have features or tools tailored to the needs of researchers in that domain.  
2. Reputable: Ensure the repository is recognized by relevant organizations or funding agencies.  
3. Sustainable: Repositories need a clear plan for long-term data preservation and should be backed by a sustainable funding model.  
4. Discoverable: Repositories should make data easily accessible and discoverable, allowing other researchers to find and reuse the data.  
5. Secure: Ensure the repository has robust data security measures in place to protect sensitive or confidential data and complies with relevant data protection regulations.  
6. Narrative: Choose a repository that supports data versioning and that tracks changes and updates from the beginning to the end of a research project.   
7. Supportive: Opt for a repository that provides comprehensive metadata and documentation support, facilitating the proper understanding and interpretation of the data.  
8. Tailorable: Verify that the repository has clear policies for data licensing and use, allowing you to retain control over how and when your data is used and shared.  
9. Interoperable: Select a repository that supports a wide range of data formats and is compatible with relevant data standards in your research field, ensuring interoperability with other systems.  
10. Accessible: Choose a repository that offers adequate user support and engages with the research community through activities such as workshops, webinars, or forums.  
11. Dependable: Opt for a repository that enables proper citation and attribution of datasets, allowing researchers to receive credit for their data contributions. 

See FABBS’ Repositories page for guidance on picking a repository and for a directory of repositories.

Ontologies

Ontologies are used to standardized the concepts and vocabulary within a subject area, making it easier for humans and machines to communicate, interpret, and use information consistently (interoperably).

For resources on ontologies, go to FABBS’ section on Ontologies on our Partnering with NASEM Page.

How to Start Implementing Open Science Practices?

For guidance on implementing open practices in each step of the research process, go to FABBS’ Open Science in the Research Process Page.

• Watch WeShareData’s videos that describe how to follow journal data policies, how to become interoperable, and how to navigate public access and ease of sharing research data.  
• Read Data Sharing in Education Science, a guide from SAGE that debunks common concerns with data sharing and provides a step-by-step guide to share your data. 
• Use the Human Metrics Initiative for workshops, presentations, research, and guides to better match scholarly practice to researchers’ values. 

Additional Resources

• FABBS Open Science Statement
• Read Ready, Set, Share! from Science.org for information on data sharing rates among researchers, suggestions and expectations to develop open data sharing management, and a Q&A of some major concerns regarding open science implementation. 
• Read APA’s What is Open Science, and Why do We Need it? for an additional list of Open Science terms and definitions and an introduction to pre-registration logistics.   
• Read APA’s Open Science in Health Psychology and Behavioral Medicine: A Statement from the Behavioral Medicine Research Council for a more in-depth overview of Open Science benefits and hurdles.
• Look through Arizona State University’s Project Open Database with a compilation of research articles, perspectives, case studies, news stories, and other materials that demonstrate the myriad ways in which open science benefits researchers and society alike. 
• Use the Open Research Funders Group Reading List which includes experiences and advice from researchers who have made their work openly available. It also provides articles featuring successes in implementing open science.