SciScore announces the release of its innovative solution, the first and only working application of its kind, in support of the pre-clinical scientific research community’s pursuit of reproducibility and transparency.
SciScore announces the release of its innovative solution, the first and only working application of its kind, in support of the pre-clinical scientific research community’s pursuit of reproducibility and transparency.
“Finding the cure for any medical ailment facing our society, costs money. And, rightly so, the public has great expectation that the money spent on research will advance healthcare,” says Anita Bandrowski, a neuroscience researcher at the University of California, San Diego and CEO of SciScore. “This tool makes it easier for researchers to focus on the work-at-hand by indicating when, or if, something was overlooked or omitted in the process of reporting the research in a manuscript.”
In January 2016, the National Institute of Health (NIH) introduced new grant review guidelines that focused on four key areas of reproducibility and transparency. This move changed the way in which grants are awarded today. “It remains to be seen in time but it’s possible that NIH changed the business of pre-clinical medical research for the better, and for good,” Bandrowski said.
In conjunction with NIH, many journals have revised author guidelines to direct researchers to include and emphasize elements required for reproducibility and transparency: PLoS, JBC, eLife, AACR, MBoC, and GSA. SciScore is being piloted by the following publishers: Wiley & Sons, NatureResearch, and eLife.
SciScore provides a score and supporting report that is used by the agency, publisher, or individual author to identify if key areas of reproducibility and transparency are addressed in the manuscript. It uses AI and deep learning technology to calculate a score by looking for evidence of randomization, blinded conduct of experiment, sample size estimation, whether sex is included as a biological characteristic, and cell line authentication or contamination. It also detects any resource ambiguity, like a mislabeled or unidentified cell line.
An author may improve a score by adding information that may be missing or correcting information that is obscure. The manuscript submitted for analysis is removed from the cloud server almost immediately after scoring, keeping information secure and private.
For more information, visit http://sciscore.com/.
About SciScore:
SciScore (SciScore.com) is an application developed by SciCrunch Inc. (scicrunch.com) supported by the Small Business Innovation Research (SBIR) program grants R43OD024432 and R44MH119094.
Media Contact:
Patricia J. Maxwell
Phone: 402.213.0681
Email: info@scicrunch.com
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Related link: http://sciscore.com/
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