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Objectives The ultimate goal of biomedical research is the development of new treatment options for patients. Animal models are used if questions cannot be addressed otherwise. Currently, it is widely believed that a large fraction of performed studies are never published, but there are no data that directly address this question. Methods We have tracked a selection of animal study protocols approved in the University Medical Center Utrecht in the Netherlands, to assess whether these have led to a publication with a follow-up period of 7 years. Results We found that 60% of all animal study protocols led to at least one publication (full text or abstract). A total of 5590 animals were used in these studies, of which 26% was reported in the resulting publications. Conclusions The data presented here underline the need for preclinical preregistration, in view of the risk of reporting and publication bias in preclinical research. We plea that all animal study protocols should be prospectively registered on an online, accessible platform to increase transparency and data sharing. To facilitate this, we have developed a platform dedicated to animal study protocol registration: www.preclinicaltrials.eu.

There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.

The international consortium TACTICS (Transnational Alliance for Regenerative Therapies in Cardiovascular Syndromes) has recently addressed key priorities in the field of cell-based therapy for cardiac repair, identifying the efficacy of translational research as one of the main challenges to ultimately improve the quality of life of patients with ischemic disease. Much of the controversy and confusion surrounding cardiac regenerative therapy stems from insufficient rigor in the conduct of preclinical studies, and there is an increasing recognition of a number of problems that undermine its quality that may contribute to translational failure. Here, we introduce well defined stages for preclinical research, and put forth proposals that should promote more rigorous preclinical work, in an effort to improve its quality and translatability. To augment the utility of preclinical research and its translation, it is necessary to (1) improve the quality of preclinical research, (2) promote collaborative efforts, and (3) enhance the sharing of knowledge and protocols. In particular, confirmatory (stage III) preclinical studies should be considered as a preamble to clinical studies and therefore must adhere to their standards of quality (including internal validity, standardization of protocols, and multicenter design). To increase transparency and minimize bias, these studies should be prospectively registered in an independent, open database. Ultimately, these recommendations should be implemented in the daily routine of investigators and in the policies of institutions, journals, and funding agencies.