There are a few things to remember about altmetrics when tapping into non-traditional methods of metrics reporting. Graphic by Miguel Tovar/University of Houston

Alternative metrics, or “altmetrics,” refers to the use of non-traditional methods for judging a researcher’s reach and impact.

Being published in a peer-reviewed journal is surely a great feat. It’s the typical way professors get their research out there. But the tools established to measure this output might end up giving the skewed impression about an author’s impact in spheres both academic and social.

Traditional metrics

Web of Science and Scopus are the main platforms that researchers rely on for collecting article citations. Web of Science’s indexing goes back to 1900, and Scopus boasts the largest database abstract and citations. The caveat with these repositories is that each resource only gives you a rating based on the range and breadth of journals it indexes. Different journals are recorded in different tools, so you may not be getting a comprehensive metric from either.

Let’s talk about h index

The h index is probably never going away, although it is always being improved upon.

An h index is a complex equation that tells the story of how often a researcher is cited. For instance, if a scholar published six papers, and all six papers were each cited by at least six other authors, they would have an h index of 6.

This equation doesn’t work out too well for an academic who, say, had one paper that was continuously cited – they would still have an h index of 1. Brené Brown, Ph.D., even with her veritable empire of vulnerability and shame related self-help has h index of 7 according to Semantic Scholar.

On to altmetrics

When a psychology professor goes on a morning show to discuss self-esteem of young Black women, for instance, she is not helping her h index. Her societal impact is huge, however.

“When I use altmetrics to deliver a professor his or her impact report, I seek out nontraditional sources like social media. For instance, I check how many shares, comments or likes they received for their research. Or maybe their work was reported in the news,” said Andrea Malone, Research Visibility and Impact Coordinator at the University of Houston Libraries.

Altmetrics aim to answer the question of how academia accounts for the numerous other ways scholarly work impacts our society. What about performances done in the humanities, exhibitions, gallery shows or novels published by creative writers?

Alternative metrics are especially important for research done in the humanities and arts but can offer social science and hard science practitioners a better sense of their scope as well. With the constant connections we foster in our lives, the bubble of social media and such, there is a niche for everyone.

The equalizer

For some, Twitter or Facebook is where they like to publish or advertise their data or results.

“When altmetrics are employed, the general public finds out about research, and is able to comment, share and like. They can talk about it on Twitter. The impact of the work is outside of academia,” said Malone. She even checks a database to see if any of the professor’s works have been included in syllabi around the country.

Academia.edu is another social network offering a platform for publishing and searching scholarly content. It has a fee for premium access, whereas Google Scholar is free. Its profile numbers are usually high because it can pick up any public data – even a slide of a PowerPoint.

The Big Idea

At the University of Houston, altmetrics are categorized thusly: articles, books and book chapters, data, posters, slides and videos. While one would think there’s no downside to recording all of the many places academic work ends up, there are a few things to remember about altmetrics:

  1. They lack a standard definition. But this is being worked on currently by the NISO Alternative Assessment Metrics Initiative.
  2. Altmetrics data are not normalized. Tell a story with your metrics, but don’t compare between two unlike sources. Youtube and Twitter will deliver different insights about your research, but they can’t be compared as though they measure the same exact thing.
  3. They are time-dependent. Don’t be discouraged if an older paper doesn’t have much to show as far as altmetrics. The newer the research, the more likely it will have a social media footprint, for example.
  4. They have known tracking issues. Altmetrics work best with items that have a Digital Object Identifier (DOI).

So have an untraditional go of it and enlist help from a librarian or researcher to determine where your research is making the biggest societal impact.

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This article originally appeared on the University of Houston's The Big Idea. Sarah Hill, the author of this piece, is the communications manager for the UH Division of Research.

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Houston doctor wins NIH grant to test virtual reality for ICU delirium

Virtual healing

Think of it like a reverse version of The Matrix. A person wakes up in a hospital bed and gets plugged into a virtual reality game world in order to heal.

While it may sound far-fetched, Dr. Hina Faisal, a Houston Methodist critical care specialist in the Department of Surgery, was recently awarded a $242,000 grant from the National Institute of Health to test the effects of VR games on patients coming out of major surgery in the intensive care unit (ICU).

The five-year study will focus on older patients using mental stimulation techniques to reduce incidences of delirium. The award comes courtesy of the National Institute on Aging K76 Paul B. Beeson Emerging Leaders Career Development Award in Aging.

“As the population of older adults continues to grow, the need for effective, scalable interventions to prevent postoperative complications like delirium is more important than ever,” Faisal said in a news release.

ICU delirium is a serious condition that can lead to major complications and even death. Roughly 87 percent of patients who undergo major surgery involving intubation will experience some form of delirium coming out of anesthesia. Causes can range from infection to drug reactions. While many cases are mild, prolonged ICU delirium may prevent a patient from following medical advice or even cause them to hurt themselves.

Using VR games to treat delirium is a rapidly emerging and exciting branch of medicine. Studies show that VR games can help promote mental activity, memory and cognitive function. However, the full benefits are currently unknown as studies have been hampered by small patient populations.

Faisal believes that half of all ICU delirium cases are preventable through VR treatment. Currently, a general lack of knowledge and resources has been holding back the advancement of the treatment.

Hopefully, the work of Faisal in one of the busiest medical cities in the world can alleviate that problem as she spends the next half-decade plugging patients into games to aid in their healing.

Houston scientists develop breakthrough AI-driven process to design, decode genetic circuits

biotech breakthrough

Researchers at Rice University have developed an innovative process that uses artificial intelligence to better understand complex genetic circuits.

A study, published in the journal Nature, shows how the new technique, known as “Combining Long- and Short-range Sequencing to Investigate Genetic Complexity,” or CLASSIC, can generate and test millions of DNA designs at the same time, which, according to Rice.

The work was led by Rice’s Caleb Bashor, deputy director for the Rice Synthetic Biology Institute and member of the Ken Kennedy Institute. Bashor has been working with Kshitij Rai and Ronan O’Connell, co-first authors on the study, on the CLASSIC for over four years, according to a news release.

“Our work is the first demonstration that you can use AI for designing these circuits,” Bashor said in the release.

Genetic circuits program cells to perform specific functions. Finding the circuit that matches a desired function or performance "can be like looking for a needle in a haystack," Bashor explained. This work looked to find a solution to this long-standing challenge in synthetic biology.

First, the team developed a library of proof-of-concept genetic circuits. It then pooled the circuits and inserted them into human cells. Next, they used long-read and short-read DNA sequencing to create "a master map" that linked each circuit to how it performed.

The data was then used to train AI and machine learning models to analyze circuits and make accurate predictions for how untested circuits might perform.

“We end up with measurements for a lot of the possible designs but not all of them, and that is where building the (machine learning) model comes in,” O’Connell explained in the release. “We use the data to train a model that can understand this landscape and predict things we were not able to generate data on.”

Ultimately, the researchers believe the circuit characterization and AI-driven understanding can speed up synthetic biology, lead to faster development of biotechnology and potentially support more cell-based therapy breakthroughs by shedding new light on how gene circuits behave, according to Rice.

“We think AI/ML-driven design is the future of synthetic biology,” Bashor added in the release. “As we collect more data using CLASSIC, we can train more complex models to make predictions for how to design even more sophisticated and useful cellular biotechnology.”

The team at Rice also worked with Pankaj Mehta’s group in the department of physics at Boston University and Todd Treangen’s group in Rice’s computer science department. Research was supported by the National Institutes of Health, Office of Naval Research, the Robert J. Kleberg Jr. and Helen C. Kleberg Foundation, the American Heart Association, National Library of Medicine, the National Science Foundation, Rice’s Ken Kennedy Institute and the Rice Institute of Synthetic Biology.

James Collins, a biomedical engineer at MIT who helped establish synthetic biology as a field, added that CLASSIC is a new, defining milestone.

“Twenty-five years ago, those early circuits showed that we could program living cells, but they were built one at a time, each requiring months of tuning,” said Collins, who was one of the inventors of the toggle switch. “Bashor and colleagues have now delivered a transformative leap: CLASSIC brings high-throughput engineering to gene circuit design, allowing exploration of combinatorial spaces that were previously out of reach. Their platform doesn’t just accelerate the design-build-test-learn cycle; it redefines its scale, marking a new era of data-driven synthetic biology.”