R3 Frequently Asked Questions (FAQs)

NIH Research Rigor and Reproducibility: The NIH Research Rigor Web resource outlines their efforts to enhance rigor and reproducibility in scientific research

Columbia University has great Reproducibility Resources and Guidelines organized by Topic 

Duke University has devoted considerable resources in Supporting a Culture of Rigor, Reproducibility, and Responsible Conduct of Research 

Johns Hopkins Bloomberg School of Public Health has developed The R³ Center for Innovation in Science Education (R³ISE) 

University of Pennsylvania - Responsible Conduct of Research and Scientific Rigor and Reproducibility

The UK Reproducibility Network (UKRN)

HMS does not currently offer or endorse a single platform for utilizing ELNs, although there are a tremendous about of piloting efforts happening across Harvard Campuses. We will continue to add updates regarding these efforts as available in this section.

Additionally, the HMS Data Management Working Group (DMWG) has been leading an initiative evaluating ELN platforms and surveying our research faculty and staff on the possible utility of such platforms. You can take a look at the incredible detail specific to many of the vendors-offered platforms directly through the DMWG ELN page to evaluate what may work best for your needs. Additionally, you may not be the only person in your department with these questions (and interest); a great start may also be a conversation with your local laboratory/center/department to discuss how different teams track and manage daily experimental output.

A great starting place for this conversation may be the Countway Library as the Harvard DASH (Digital Access to Scholarship at Harvard) system is in place to help you fulfill these needs. HMS and Harvard University also provide guidance regarding open access requirements for NIH funded researchers. The excellent Research, Reference and Educational staff at the Countway Library can help guide you through the process if you have questions. Additionally, regardless if you are funded through the NIH or not, you may be interested in additional options for open access data management and sharing. Take a look at Harvard's Dataverse and the OSF   (Open Science Framework) as two additional (not exhaustive) options that are out there for you to consider. As a reminder, have a conversation locally in your lab (either with your supervisor if you are a trainee/faculty in a laboratory or with your team if you are a PI) to consider elements of data management and sharing such as access and security while you consider these options. 

As a community in general, there is no "one size fits all" approach to generating figures that represent the culmination of your scientific efforts. Many journals provide direct guidance as to appropriate steps for creating and processing images. For example, the Journal of Cell Biology (JCB) provides some excellent general guidelines regarding Image Data Integrity (see the section on "Data integrity and plagiarism"). Nature Publishing provides guidance in their Image Integrity policy for all image/figure data submitted as part of a manuscript. Additionally, the Columbia ReaDI program has a great guide called the "Data to Figure" template to assist you in thinking about the overall management of your image data during this critical juncture. These resources provide a solid foundation for incorporating appropriate image management strategies available in the scientific community.

In addition to general concepts you may want to visit OMERO, HMS' image data repository platform. Some recent advancements in the platform have generated new workflows which provide "soup to nuts" management of your image data. The platform contains features including maintenance of raw image data (from capture) and versioning for the utility of these images in downstream applications (like creating figures for papers, while maintaining source data intact). This is a resource that may have some restrictions to access depending on whether you are a HMS quad based researcher, or whether you are a member of our affiliate institutions, so make sure to explore all of your options with the OMERO team.  

A data management plan (DMP) is as strategy you (and your team) will want consider in managing the totality of the data you generate as a member of the Harvard Medical School community. Whether you are a recently recruited trainee or a tenured faculty member, having a consistent system for how you name, organize, collect, store, access, share and archive data are all essential elements of a successful research program.

There currently is no single HMS required or preferred format for DMP development. However, your local laboratory and/or Departments and Centers  may have these requirements and preferred systems in place, so it is important to have a discussion with local leadership regarding expectations of data management. If you have or are working on a funded grant project, your granting agency likely has requirements regarding data management, and specifically data sharing (e.g., NIH Data Implementation and Guidance) 

The HMS Data Management Working Group (DMWG) has also put together some great resources regarding DMP development and implementation. This includes sample DMPs and links to the web-based DMP Tool.  There are also frameworks available from the National Information Standards Organization (NISO); check out the NISO Primer on Research Data Management. Additional helpful resources that may guide successful DMP discussions and development can be found in the DMWG checklists for joining a new lab and starting or joining a new project at HMS.

National Academy of Sciences (NAS) Report on Reproducibility and Replicability in Science (2019)

The important conversation of this committee is also happening nationwide, and worldwide. The National Academy of Sciences (NAS) Report on Reproducibility and Replicability in Science (2019) is an evidence-based consensus assessment developed by the NAS Committee on Reproducibility and Replicability in Science. In particular, the discussions of Sections 4, 6, and 7 review the prevalence of non-reproducibility, non-transparency, non-replicability, and related challenges in the sciences. These are common hurdles. The report sections noted also include some thoughtful discourse on tools and resources that might help to address these challenges.

Research Culture: Framework for advancing rigorous research (Koroshetz et al. 2020)

A recent article from the National Institute of Neurological Disorders and Stroke (NINDS) published in eLife (Koroshetz et al. 2020) regarding the need for a shift in the culture of scientific practice, including concrete frameworks for consideration from a broad group of stakeholders (and colleagues) in various institutional settings. For additional information on this work, please visit the NINDS site on developing Rigor Champions and Resources.