Our Editor in Chief has started a dialogue on the well documented reproducibility gap in science. As Dr. Pritsker has mentioned, if journals devote more resources to publishing in-depth methods, we may be able to increase the reproducibility of results. However, this isn't the only challenge faced by the scientific community, and there are other questions to be asked such as: "Are my colleagues drawing the correct conclusions from their experiments?" or "Do my results compare to those of similar experiments?"
As stated in BioMed Central's blog, there may be a solution to these questions. Services from the National Center for Biotechnology Information (NCBI) and GigaDB.com offer scientists the opportunity to publish their results in a citable, open access format. NCBI is a long established program funded by the National Institutes of Health that stores genome sequencing data. Giga DB is a new database and journal built to increase sharing biological data. This format allows scientists to publish data from specific studies to be referenced in an article through a typical citation. In the case of the GigaDB database, researchers can generate independent citations to specific data, thus giving credit to the scientists who generated the data. This is beneficial as scientists may provide more data if they can ensure they will be credited for it when other scientists utilize their work. The key is providing the data a Direct Object Identifier (DOI for short) to allow a permanent reference point for the content.
This provides a valuable resource to scientists looking to publish their work, and further motivation for scientists to publish their raw data or null results, per the Mayo Clinic's recent suggestions. However, these resources may prove a double edged sword, as scientists may not want their name permanently associated with raw data they cannot defend. Raw data can often be open to different methods of interpretation, which can damage the work of a scientist if interpreted improperly. Despite these concerns, citable database resources provide a meaningful step in increasing experimental transparency.