Indeed, the reality of scientific publication shows that the quality of both the “Materials and Methods” section and the “Results” section ranges from very poor to reasonably useful. As the experimental results should serve as a valid basis for the acceptance of hypotheses, or for the creation of new hypotheses that need to be accepted, again, both the materials and the methods applied, and the data generated, must be reported accurately

in ways that do not allow misinterpretation. Even more, enzymology data should be reported in standardized way to link protein (structure) to enzyme function datasets and to make them machine-readable for the creation of protein-function databases. Apweiler et al., 2005 and Apweiler et al., 2010 pointed out the ABT-199 CP-868596 importance of standards when protein-function data are reported in journals (see also Tipton et al., 2014). A framework of criteria that determines a minimum

of data reported will help to ensure that data generated can be located by researchers and computers alike, an important pre-requisite for successful in silico analysis and representation of metabolic systems. In recent years scientists from diverse fields in computational and experimental biology have been developing minimum information standards for improving the data quality in publications and databases. The Minimum Information for Biological and Biomedical Investigations (MIBBI) project has devoted great efforts to coordinating the development of data standards and to avoiding redundancy and incompatibility. MIBBI is intended to be a one-stop-shop for minimum-information new checklists;

it currently provides links to 39 registered checklists in the portal section and assistance for the creation of new, non-redundant guidelines in the foundry section ( Taylor et al., 2008). In the best case, authors can access MIBBI to find the most appropriate set of minimum information guidelines when writing their papers. Examination of the publication guidelines of the major biochemistry journals confirms the emerging interest of their editors in high-quality data reporting, as a growing number of these journals have adopted community-based guidelines for data standards. However, the checklist groups need to take into account the constant changes in technology and methodology, as well as modifications of laboratory standard practices that lead to the need for continual revision and periodic updating of their lists. The advantages of data reporting standards appear to be obvious; potential problems with the standardization of enzyme data in terms of good publication practice are so far unknown. This is a typical question when rules and recommendations are proposed, on account of suspicions that it may restrict scientific freedom and potentially put researchers in a straitjacket, as previously mentioned.