Transparency in the reporting of data and reproducibility
of results are important for proper peer-reviewing and
interpretation of background data, at least to the extent
that this is possible with regards to sensitive industry
inventory data. A good example of the way data can be
published without compromising the focus of the article
was given by Grönroos et al. (2006) and Pelletier et al. (2009),
who both published supporting documents describing
inventories (although with different coverage of environmental
data; see above), core processes, assumptions, and
calculations. Another solution to fitting large inventories
to the often restricted format of scientific journals is to
report which processes derived from a background database
(e.g., ecoinvent) were included in the study without actually
including the data of that process. Such processes could
simply be reported using the process ID numbers, rather
than the full process names. This kind of more open
reporting of data is critical for developing specific LCA
data sets for aquaculture-related processes, as much primary
data currently are lost by aggregating results and by only
presenting impacts, rather than inventories. It should,
however, be pointed out that the data sourcing and
reporting issues discussed here are not unique to aquaculture
LCAs, but rather apply to the majority of LCA studies
published, whether peer-reviewed or not.