How robust are your data?
Scientists at PAASP have developed the unique evaluation protocol, PAASPort®, to identify potential bias in research and to create a comprehensive picture about the quality of research data. Click here for more information…
-
Investors
Your investment might be at risk due to biased research. Use PAASPort® as a valuable supplement to your DD process.
-
Funders
Research quality assessment is a filter to focus your funding on high quality research with higher chances to be translatable.
-
Pharma
Buying research data of low quality results in tremendous waste of resources which can be avoided with a clear idea about their bias.
-
CROs
The PAASPort label identifies your research procedures as high quality and more valuable for your customers, giving you a competitive advantage.
-
Academia
Having your research process evaluated by PAASP will show funders and industry that your research is according to best practice.
-
Biotech
Getting confirmation that your research projects are on the right track to get the next investment and working towards the next block buster.
Sign up for our bi-monthly Newsletter
PAASPort®
The tool to assess and improve quality of research practices!
Scientists at PAASP have developed a unique evaluation protocol which was named PAASPort® to identify potential bias in research.
This tool allows the identification of risk factors, leading to biased research data, and protection mechanisms, preventing such bias. This allows to create a comprehensive picture about the quality of research data.
PAASP Network
The partnership consists of a network of experts in the US and Europe and performs services around the globe.
Find out more about the PAASP team or get directly in contact.
PAASP Resource Center
PAASP collects and curates information for researchers in the Resource Center on the reproducibility crisis.
The resources provide support and background information for improving the research process but are also fun!
Case study
FROM FECAL TRANSPLANTS, OBESITY AND THE IMPORTANCE OF RANDOMIZATION AND NORMALIZATION
In one of the last Newsletter issues (LINK), we wrote about the uncertainties which sometimes exist when trying to find the best normalization approach for a given study. In this context, the need to be transparent about normalization or randomization steps and provide sufficiently detailed information is nicely demonstrated by the following example:
One of the most influential papers about the role of the gut microbiota in obesity has been published in Nature in 2006 by Turnbaugh and colleagues. It has already been cited more than 8000 times. …