Biomedical researchers often have to work on massive, detailed, and heterogeneous datasets that raise new challenges of information management. This study reports an investigation into the nature of the problems faced by the researchers in two bioscience test laboratories when dealing with their data management applications. Data were collected using ethnographic observations, questionnaires, and semi-structured interviews. The major problems identified in working with these systems were related to data organization, publications, and collaboration. The interoperability standards were analyzed using a C(4)I framework at the level of connection, communication, consolidation, and collaboration. Such an analysis was found to be useful in judging the capabilities of data management systems at different levels of technological competency. While collaboration and system interoperability are the "must have" attributes of these biomedical scientific laboratory information management applications, usability and human interoperability are the other design concerns that must also be addressed for easy use and implementation.