Scientific data audit—A key management tool

Abstract

Approximately 10–20% of all research and development (R&D) funds are estimated to be spent on questionable studies, which are characterized by misrepresentation of data, inaccurate reporting, and fabrication of experimental results. Routine data audits are capable of decreasing the frequency of questionable R&D studies by half, resulting in a savings of at least $5 to $10 for every audit dollar spent. The primary value of data audit to research is to verify the validity of experimental procedures that lead to positive results, whereas the primary value of data audit to development is to identify problem areas in experimental protocols and procedures. Data auditing is also useful in aiding managers to evaluate and select R&D projects. A regularly scheduled data auditing program increases the reliability of project planning and helps to improve organizational performance in the completion of projects meeting functional expectations on time and within budget. The benefits of data audit to organizations conducting R&D range from the highly focused‐reduction of errors and irregularities, to the big picture‐enhanced effectiveness in strategic planning and implementation of the plan.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 34 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.