Review and Critique of the MRHA Data Integrity Guidance for Industry — Part 4: System Design, Definitions and Overall AssessmentMay 29, 2015 1:56 pm | by R.D. McDowall, Ph.D. | Comments
This is the final part of a series reviewing and critiquing recent MHRA guidance for industry on data integrity. The first part of the series provided a background to the guidance document and discussed the introduction. The second part reviewed the data governance system, and the third part discussed data criticality and lifecycle. This part reviews the system design, some of the definitions, and finishes with an overall assessment.
Review and Critique of the MRHA Data Integrity Guidance for Industry — Part 3: Data Criticality and Data Life CycleMay 29, 2015 12:21 pm | by R.D. McDowall, Ph.D. | Comments
This is the third of a four-part series reviewing and critiquing the recent Medicines and Healthcare products Regulatory Agency guidance for industry document on data integrity. The first part of the series provided a background to the guidance document and discussed the introduction to the document. The second part reviewed and discussed the data governance system. In this part, we will look at data criticality and the data life cycle.
Review and Critique of the MRHA Data Integrity Guidance for Industry — Part 2: Data Governance SystemMay 29, 2015 11:27 am | by R.D. McDowall, Ph.D. | Comments
This is the second of a four-part series reviewing and critiquing the recent Medicines and Healthcare products Regulatory Agency (MHRA) guidance for industry document on data integrity. The first part of the series provided a background to the guidance document and discussed the introduction to the document. In this part, we will look at the MHRA requirement for a data governance system.
This new series of four articles takes a look at the new UK’s Medicines and Heathcare products Regulatory Agency (MHRA) guidance for industry on data integrity. Topics in this introductory article include global data integrity problems, the MHRA approach to data integrity, a MHRA data integrity guidance overview, and an introduction to the MHRA guidance.
The real benefit of laboratory information management systems (LIMS) is difficult to calculate. Let’s take a look at some key considerations, starting with the question of whether to build the LIMS yourself or buy a commercial LIMS… Advocates for building a new LIMS themselves usually state that their lab is so unique, they cannot use a commercial LIMS. However, very few labs are truly unique ...
Scientific Computing periodically features special informatics focus articles that attempt to help you with such complex tasks as selecting a laboratory information management system or interfacing systems together. Unfortunately, there is only a limited amount of information that one can cram into one of these articles. Where we are limited to just a few pages for each of our attempts, Joe Liscouski has written a whole book on the subject
On a telescope at the summit of Mauna Kea in Hawaii, it’s not easy to put in a full night of work. At 14,000 feet, you’re operating at only 60 percent of the oxygen available at sea level, which makes concentrating difficult. Top that off with a shift that begins at 6:30 pm and ends at 6:30 am, and it becomes hard to imagine astronomers working like that year-round. Luckily, most of us don’t have to.
Today's LIMS allow research institutions to monitor and manage a broad array of biomedical research processes end-to-end and remotely. But how do they accommodate the ongoing flood of discoveries in areas such as genetics, the -omics, regenerative medicine and behavior, ongoing adjustments to workflows and protocols, tens of thousands of animals, and the evolution of legislative, welfare quality, and ethics directives?
These days, using a LIMS seems to feature in every scientist's life, and for some small and medium-size labs, open source code is the way forward with a LIMS. In fact, businesses have grown up around helping labs implement open source LIMS and learn to make modifications in house. A bridge too far for a nonprofessional? Not according to Greg Wilson, who believes that most scientists can easily learn enough to slip into coding...
Quantum Computing has been a concept since the 1980s that has remained outside the domain of real-world HPC. Through the era of Moore’s Law and exponential progress in feature size, clock rates and resulting performance, the need for alternative paradigms and technologies has attracted little interest. But there has remained a curiosity among a limited community that has driven slow but persistent advances in associated ideas.
All the computing power in the world isn’t useful if the software designed to access it is poorly designed. And we’re all much more discerning about user interfaces and usability: we expect our laboratory software to behave as intuitively as our smartphones. After all, laboratory employees are unlikely to be preoccupied with lines of codes and processors — they’re focused more on how easy the software is to use.
There are a number of excellent commercial performance analysis tools on the market. Their big drawback is that they cost money. As a result, acquisition of commercial performance analysis software falls through the cracks, as most funding agencies discourage or prohibit the use of grant money for infrastructure improvements, and few grant authors are willing to take money away from research. Open-source tools are able to fill this gap.
This delightful and informative guide from my friends at No Starch Press comes with the following cover blurb: “Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern Science that will show you how to keep your research blunder-free.” It is somewhat pithy, but as to blunder free, I will quote the old maxim that “nothing is foolproof, as fools are so very clever.” Still, the book has much to recommend it.
Electromagnetic radiation – it might sound like something that you’d be better off avoiding, but electromagnetic waves of various kinds underpin our senses and how we interact with the world – from the light emissions through which your eyes perceive these words, to the microwaves that carry the Wi-Fi signal to your laptop or phone on which you’re reading it.
The World Health Organization reports that cardiovascular diseases are the number one cause of death globally. Working to address this imperative public health problem, researchers world-wide are seeking new ways to accelerate research, raise the accuracy of diagnoses and improve patient outcomes. Several initiatives have utilized ground-breaking new simulations to advance research into aspects such as rhythm disturbances and ...