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The Data Science Research Center

@machinelearnbot

Data Science Central launched this week the Data Science Research Center, a public online resource for practitioners to read, download or publish high quality papers. If your article is accepted, you are entitled to a signed, free copy of Dr Granville's book, may have your profile featured on DSC on the featured members page, and be featured in the members of the week section in our weekly digest. The Research Center is the place to post research papers, preprints and non commercial white papers discussing data science / big data new techniques, principles and methodology. Currently, the 11 articles listed are internal to our Research Lab. Also, all articles published in the Research Center are part of our RSS Feed System and may be distributed on partner websites, thus increasing your reach.


Scientific Research Suffers As Funding Falls, Faulty Results Could Rise

International Business Times

Dwindling funds for scientific research could encourage scientists to cheat, a report released Friday by the National Academies of Sciences, Engineering and Medicine finds. Additionally, research misconduct is eating up precious funds even as they grow scarcer. The report, "Fostering Integrity in Research," said the funding crunch could hamper progress as scientists skip protocols and arrive at faulty conclusions. Research misconduct, some of which was not detected for years, has led to an increase in the "number and percentage of research articles that are retracted and growing concern about low rates of reproducibility … [raising] questions about how the research enterprise can better ensure that investments in research produce reliable knowledge," Chairman Robert M. Nerem wrote in the report's preface. The U.S. devoted 2.81 percent of gross domestic product to research and development in 2012, with the private sector contributing two-thirds of that.


Chen

AAAI Conferences

With the growing volume of publications in the Computer Science (CS) discipline, tracking the research evolution and predicting the future research trending topics are of great importance for researchers to keep up with the rapid progress of research. Within a research area, there are many top conferences that publish the latest research results. These conferences mutually influence each other and jointly promote the development of the research area. To predict the trending topics of mutually influenced conferences, we propose a correlated neural influence model, which has the ability to capture the sequential properties of research evolution in each individual conference and discover the dependencies among different conferences simultaneously. The experiments conducted on a scientific dataset including conferences in artificial intelligence and data mining show that our model consistently outperforms the other state-of-the-art methods. We also demonstrate the interpretability and predictability of the proposed model by providing its answers to two questions of concern, i.e., what the next rising trending topics are and for each conference who the most influential peer is.


[In Depth] Human embryo research confronts ethical 'rule'

Science

Researchers studying human embryos in the lab have always hit a roadblock at about 7 days--the point where the embryo would usually attach to the uterus. Now, two teams report growing human embryos about a week past that point, revealing key differences between the development of human and mice embryos. The limit now isn't technological, but ethical: a widely held rule that dictates all embryo research should be stopped at 14 days. Now that culturing methods have finally made it possible to reach this point, some scientists and bioethicists are saying the rule should be revisited. But that won't be welcomed by those who consider the rule to have a firm moral grounding--nor by those who oppose all research on human embryos.