Resources

Did you know that several Haas Professors are leaders in the Open Science movement? Haas Professors integrate best practices for open science into research-focused courses, including:

• PHDBA 297B 001 – LEC 001
• PHDBA 297T 003 – LEC 003

Expecting to start a research project or work on one? Start with the UC Berkeley CITI training on protection of human subjects.

Not sure where to start with open science? Start with understanding your goals and the best practices you need to incorporate into your research workflow. For example, if you want to publish in a journal, understand evolving journal requirements and preferences – including study registration, pre-analysis plans, availability of code and data – and build into your workflow at the right times.

• Search Open Science Practices across TOP journals – https://www.topfactor.org/

Learn more about how some disciplines review and track evolving journal practices, such as this summary from development economics.

Looking for examples of published reproducible research led by Haas faculty and co-authors? Review published Data Replication files for recent Haas faculty publications, such as:

• Schroeder, J., & Fishbach, A. (2024). Feeling known predicts relationship satisfaction. Journal of Experimental Social Psychology, 110, 104559. https://doi.org/10.1016/j.jesp.2023.104559
• Kim G, Adams I, Diaw M, Celly M, Nelson LD, Jung MH (2022) Prosocial spending encourages happiness: A replication of the only experiment reported in Dunn, Aknin, and Norton (2008). PLoS ONE 17(9): e0272434. https://doi.org/10.1371/journal.pone.0272434
• Bachas, P., Gertler, P., Higgins, S. and Seira, E. (2021), How Debit Cards Enable the Poor to Save More. The Journal of Finance, 76: 1913-1957. https://doi.org/10.1111/jofi.13021
• Christensen G, Dafoe A, Miguel E, Moore DA, Rose AK (2019) A study of the impact of data sharing on article citations using journal policies as a natural experiment. PLoS ONE 14(12): e0225883. https://doi.org/10.1371/journal.pone.0225883

Unfamiliar with why research study registration matters? Review this brief explainer and determine which registry is right for you and your study.

Available registries include:

• American Economic Association’s (AEA) registry for randomized controlled trials – https://www.socialscienceregistry.org/
• Open Science Framework (OSF) registry – https://osf.io/
• AsPredicted – https://aspredicted.org/
• Registry for International Development Impact Evaluations – https://ridie.3ieimpact.org/index.php

At the study registration stage? Consider – what are your forecasted or predicted results? How will you know how your actual results compare to these forecasts? If you’re curious about work in this area, check out publications by Haas faculty, including:

• DellaVigna, Stefano, and Devin Pope. 22018. “Predicting Experimental Results: Who Knows What?” Journal of Political Economy 126 (6): 2410–2456. doi:10.1086/699976.
• DellaVigna, Stefano, Devin Pope, and Eva Vivalt. 2019. “Predict Science to Improve Science.” Science 366 (6464): 428–429. doi:10.1126/science.aaz1704.

The Social Science Prediction Platform (SSPP) is a free platform that enables researchers to collect predictions by other academics about the results of their research study. By collecting forecasts, you can compare your findings to other researcher’s priors. Collecting forecasts can frame why your research findings, including null results, are important for changing or confirming the current state of knowledge.

Working toward computationally reproducible research starts during study design and ends with availability of final analysis and underlying data and code. But transparency must be balanced with responsible management of data – and not all data can be made public. Brush up on Berkeley data classification and protection standards and handy visuals or check-in with your IT department!

Understand responsible data management throughout your research life cycle:

• IBSI Data Management Guidance – Under development
• (Many!) Berkeley Data Services – See summary from the Berkeley Data Services Librarian.
• Data collaboration – Working with a team? Review Berkeley’s data collaboration tools.
• Independent computational reproducibility assessments – Interested in what support Berkeley can offer you for pre-publication reproducibility checks? Haas Xlab is ready to support!

Using Generative AI in your research? UC Berkeley has an AI policy agreement with certain providers to protect UC data and prevent it from being used as a training set. See the campus list of agreements with generative AI companies and corresponding levels of data security, along with the UC Berkeley policy for generative AI.

Are you ready to share your research findings and the underlying data and code? As discussed above, make sure you choose the right site based on the P-level of your data – P1 is public, but P3-P4 cannot be shared as public data. Brush up on Berkeley standards and handy visuals or check-in with your IT department!

P1/Public data sites include:

• UC Berkeley Library Dataverse – https://datasets.lib.berkeley.edu/
• Dryad – https://datadryad.org/
• Research Box – https://researchbox.org/
• Open Science Framework – https://osf.io/

Restricted data sites include:

• Inter-University Consortium for Political and Social Research (ICPSR) – (Log-in with CalNet) – https://www-icpsr-umich-edu.libproxy.berkeley.edu/sites/icpsr/home

• UC Berkeley Open Science library guide – At Berkeley, there are various resources to start you on your path towards open, transparent and reproducible research. We rely on many proprietary products in our day-to-day work, but there is no one-size-fits-all way of practicing open science. Be flexible, adaptable and curious in your approach!

• Berkeley Initiative for Transparency in the Social Sciences (BITSS) Resources – The BITSS Resource Library contains tools for learning, teaching, and practicing research transparency and reproducibility, including curricula, slide decks, books, guidelines, templates, software, and other tools. All resources are categorized by i) topic, ii) type, and iii) discipline. Filter results by applying criteria along these parameters, such as resources specific to the stage of the research process that you are engaged in, or use the search bar to find what you’re looking for. Follow their BITSS blog – https://www.bitss.org/blog/ – or contact them directly.

• Open Science Framework (OSF) – The OSF provides free online resources for storing your research documents, collecting and analyzing your data, and publishing your work through preprints. The platform makes it easier to centralize research materials and make them public and is integrated with other software, including GitHub and Google Drive. The OSF assigns Global Unique Identifiers (GUIDs) to all files and components of your project, enabling citations. Log in with your Calnet ID to access paid features available through UC Berkeley’s license.

• Framework for Open and Reproducible Research Training (FORRT) – FORRT has a pedagogical library with materials for professors to teach research transparency, a replication hub, and publications on the state of research transparency and open science. FORRT’s replication hub allows researchers to upload their own replications, to view and find previously completed replications, and to access replication templates.

• Bay Area Open Science Group – The Bay Area Open Science Group is a growing community for Bay Area academics and researchers interested in incorporating open science into their research, teaching, and learning. Targeting students, faculty, and staff at UCSF, Berkeley, and Stanford, the goal of the community is to increase awareness of and engagement with all things open science, including open access articles, open research data, open source software, and open educational resources. Through this work the group hopes to connect researchers with tools they can use to make the products and process of science more equitable and reproducible.