Since we recently announced our $10001 Binary Battle to promote applications built on the Mendel API (now Including POLO as well), I decided to take a look at the data to see what people have to work with. My analysis focused on our second largest discipline, Computer Science. Biological Sciences (my discipline) is the largest, but I started with this one so that I could look at the data with fresh eyes, and also because it’s got some really cool papers to talk about.
Here’s what I found: What I found was a fascinating list of topics, with many of the expected fundamental papers Like Chanson’s Theory of Information and the Google paper, a strong showing from Unprepared and machine learning, but also some interesting hints that augmented reality may be becoming more of an actual reality soon. The top graph summarizes the overall results of the analysis. This graph shows the Top 10 papers among those who have listed computer science as their discipline and chosen a susceptible.
The bars are colored according to susceptible and the number of readers Is shown on the x-axis. The bar graphs for each paper show the distribution of readership levels among spinelessness. 17 of the 21 CSS spinelessness re represented and the axis scales and color schemes remain constant throughout. Click on any graph to explore it in more detail or to grab the raw data. (N.B.: A minority of Computer Scientists have listed a susceptible. I would encourage everyone to do so. ) 1 .
Latent Directly Allocation (available full-text) LAD Is a means of classifying objects, such as documents, based on their underlying topics. I was surprised to see this paper as number one Instead of Chanson’s Information theory paper (#7) or the paper describing the concept that became Google (#3). It turns out that interest in this paper is very strong among those who list artificial intelligence as their susceptible. In fact, AH researchers contributed the majority of readership to 6 out of the top 10 papers.
Presumably, those interested in popular topics such as machine learning list themselves under AH, which explains the strength of this susceptible, whereas papers Like the Unprepared one or the Google paper appeal to a broad range of suppleness’s, Glenn those papers a smaller numbers spread across more spinelessness. Professor Bile is also a bit of a superstar, so that didn’t hurt. (the irony of a manually-categorized list with an LAD paper at the top has not escaped us) 2.
Unprepared : Simplified Data Processing on Large Clusters (available full-text) personalization technique for breaking down huge computations into easily executable and reconcilable chunks. The importance of the monolithic “Big Iron” supercomputer has been on the wane for decades. The interesting thing about this paper is that had some of the lowest readership scores of the top papers within a susceptible, but folks from across the entire spectrum of computer science are reading it. This is perhaps expected for such a general purpose technique, but given the above it’s strange that there are no AH readers of this paper at all. The Anatomy of a large-scale hypertext search engine (available full-text) In this paper, Google founders Sergey Bring and Larry Page discuss how Google was created and how it initially worked. This is another paper that has high readership across a broad swath of disciplines, including AH, but wasn’t dominated by any one discipline. I would expect that the largest share of readers have it in their library mostly out of curiosity rather than direct relevance to their research. It’s a fascinating piece of history related to something that has now become part of our every day ivies. . Distinctive Image Features from Scale-Invariant Checkpoints This paper was new to me, although I’m sure it’s not new to many of you. This paper describes how to identify objects in a video stream without regard to how near or far away they are or how they’re oriented with respect to the camera. AH again drove the popularity of this paper in large part and to understand why, think “Augmented Reality’. AR is the futuristic idea most familiar to the average sic-if enthusiast as Terminator-vision.
Given the strong interest in the topic, AR could be closer than we hind, but we’ll probably use it to layer Group deals over shops we pass by instead of building unstoppable fighting machines. 5. Reinforcement Learning: An Introduction (available full-text) This is another machine learning paper and its presence in the top 10 is primarily due to AH, with a small contribution from folks listing neural networks as their discipline, most likely due to the paper being published in IEEE Transactions on Neural Networks.
Reinforcement learning is essentially a technique that borrows from biology, where the behavior of an intelligent agent is is controlled by the here are many different interacting positive and negative stimuli. This is how we’ll teach the robots behaviors in a human fashion, before they rise up and destroy us. 6. Toward the next generation of recommender systems: a survey of the state-of-the- art and possible extensions (available full-text) Popular among AH and information retrieval researchers, this paper discusses recommendation algorithms and classifies them into collaborative, content-based, or hybrid.
While I wouldn’t call this paper a groundbreaking event of the caliber of the Shannon paper above, I can certainly understand why it makes such a strong honing here. If you’re using Mendel, you’re using both collaborative and content- based discovery methods! 7. A Mathematical Theory of Communication (available full-text) Now we’re back to more fundamental papers. I would really have expected this to be at least number 3 or 4, but the strong showing by the AH discipline for the machine learning papers in spots 1, 4, and 5 pushed it down.
This paper discusses the theory of sending communications down a noisy channel and demonstrates a few key engineering parameters, such as entropy, which is the range of states of a given communication. It’s one of the more fundamental papers of computer science, founding the field of information theory and enabling the development of the very tubes through which you received this web page you’re reading now. It’s also the first place the word “bit”, short for binary digit, is found in the published literature. 8.
The Semantic Web (available full-text) In The Semantic Web, Tim Burners-Lee, Sir Tim, the inventor of the World Wide Web, describes his vision for the web of the future. Now, 10 years later, it’s fascinating to look back though it and see on which points the web has delivered on its promise ND how far away we still remain in so many others. This is different from the other papers above in that it’s a descriptive piece, not primary research as above, but still deserves it’s place in the list and readership will only grow as we get ever closer to his vision. 9.
Convex Optimization (available full-text) processing. Convex optimization tries to find the provably optimal solution to an optimization problem, as opposed to a nearby maximum or minimum. While this seems like a highly specialized niche area, it’s of importance to machine learning and AH researchers, so it was able to pull in a nice readership on Mendel. Professor Boyd has a very popular set of video classes at Stanford on the subject, which probably gave this a little boost, as well. The point here is that print publications aren’t the only way of communicating your ideas.
Videos of techniques at Civvies or JOVE or recorded lectures (previously) can really help spread awareness of your research. 10. Object recognition from local scale-invariant features (available in full-text) This is another paper on the same topic as paper #4, and it’s by the same author. Looking across spinelessness as we did here, it’s not surprising to see two related papers, of interest to the main driving discipline, appear twice. Adding the readers from this paper to the #4 paper would be enough to put it in the #2 spot, Just below the LAD paper.
Conclusions So what’s the moral of the story? Well, there are a few things to note. First of all, it shows that Mendel readership data is good enough to reveal both papers of long- standing importance as well as interesting upcoming trends. Fun stuff can be done with this! How about a Mendel leadership? You could grab the number of readers for each paper published by members of your group, and have some friendly intention to see who can get the most readers, month-over-month.
Comparing yourself against others in terms of readers per paper could put a big smile on your face, or it could be a gentle nudge to get out to more conferences or maybe record a video of your technique for JOVE or Khan Academy or Just Youth. Another thing to note is that these results don’t necessarily mean that AH researchers are the most influential researchers or the most numerous, Just the best at being accounted for. To make sure you’re counted properly, be sure you list your susceptible on your profile, or if you can’t find your exact one, pick the closest one, eke the machine learning folks did with the AH susceptible.
We recognize that almost everyone does interdisciplinary work these days. We’re working on a more flexible discipline assignment system, but for now, Just pick your favorite one. These stats were derived from the entire readership history, so they do reflect a founder effect to some degree. Limiting the analysis to the past 3 months would probably reveal different trends and comparing month-to-month changes could reveal rising stars. To do this analysis I queried the Mendel database, analyzed the data using R, and prepared the figures with Tableau Public.
A similar analysis can be done dynamically using the Mendel API. The API returns SON, which can be imported into R using the fingernails package from Duncan Temple Lang and Carl Bitterer is implementing the Mendel API in R. You could also interface with the Google Visualization API to make motion charts showing a dynamic representation of this multi-dimensional data.