With the end of the year quickly approaching, what better time to reflect on the work that your team has accomplished this year and recognize the foundation that lends itself to this success—laboratory culture. As the holidays are a great time to boost employee morale and encourage team building activities, here’s how you can spread holiday, or as I’d like to call it, “chemical” cheer in the lab this year to foster scientific discovery year round.
Data integrity has become an industry buzzword, but do people really understand what it means? A recent survey we conducted with Chemistry & Engineering News (CE&N) showed that scientists think about data integrity differently. Read on to learn more about our survey and the results we found.
Analytical data plays a critical role in R&D by supporting critical decision-making on a daily basis. Whether a synthetic chemist is looking to see if their reaction yielded the product they expected, a group of scientists in development are building an impurity control strategy, or experts in manufacturing are collecting data for regulatory submissions, applications of analytical data are ubiquitous. At a time when the volume of insight-rich data one can gather is extraordinary, chemists working in academic research, industry, and non-profit organizations alike face regular challenges in managing and sharing their data.
Chemical R&D generates a deluge of instrumental analytical data on a daily basis. As critical R&D decisions and regulatory submissions are based on this data, the need for quality data management is more important than ever before. A lot has changed since the days when paper notebooks were the leading data management ‘platform’ among scientists. Advancements in research and instrument hardware continue to increase the amount of data we are able to produce and process. Sanji Bhal sits down with Graham McGibbon, director of strategic partnerships at ACD/Labs, to discuss his outlook on the industry and the pressing need for better management of analytical chemistry data in R&D.
Studying chemistry can open up many career paths since it can be applied in a variety of fields. In fact, an understanding of chemistry helps provide answers to almost every question about the world and helps makes us more informed consumers. To support students pursuing a career in chemistry, ACD/Labs recently collaborated with Pearson to help undergraduate students learn about the relationship between spectroscopic data and chemical structures.
In 2017 we conducted our first formal customer service oriented survey. Completed by 482 industry professionals working in pharmaceuticals, academia, agrosciences, life science, and chemicals and materials science, our survey explored satisfaction with ACD/Labs’ support, responsiveness, employee knowledge, and overall software quality. A summary of the results is presented here.
We talk to a team at Dow AgroSciences about their decision to make changes in their laboratory to enable more efficient screening of method parameters. Using ACD/Labs’ method development software in their process enabled the team to implement a systematic approach to method development.
It’s that time of year—the leaves are falling, it’s a little cooler, I can’t decide whether to dress for my morning or evening commute, and we announce the release of a new version of ACD/Labs software—version 2017.1. This annual event is a result of the hard work of many. Most importantly, however, it is the culmination of requests of new functionality and tools by customers and partners.
Professor Marcel Jaspars, head of the Marine Biodiscovery Centre at the University of Aberdeen, and leader of the PharmaSea project, is featured in this video outlining his team's research methodologies, and their unexpected discoveries related to the treatment of Epilepsy and Alzheimer’s.
Andrew Anderson and Graham McGibbon sat down with Jack Rudd, senior editor at Technology Networks, to discuss the issue of data heterogeneity as well as some of the other challenges associated with modern labs and businesses. While there seems to be a general appreciation of the problems related to Big Data, it has become clear that the complications caused by data heterogeneity—or the different types and/or compositions of data—are often overlooked.