Sister species interactions in birds, and the potential for citizen science to change our perspectives

Every day, birders around the world record which species they see. Many of them contribute their sightings to the groundbreaking citizen science project called eBird, run out of the Cornell Lab of Ornithology in the US. One outcome from this collective activity is a worldwide record of which species have been reported in the same place at the same time – i.e. which species come into contact.

This citizen science has potential to really change the way we work at bird interactions.

Read about it here!

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Killer whales are winning on climate change

Change creates winners and losers, and that includes climate change, especially at the top of the world. On the losing side of the environmental ledger we find the polar bear, floating glumly on its ever-shrinking ice floe.

On the winning side, a new apex predator is cruising northern waters.

Which might be causing problems for other species of whales… read about it here!

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Thomas Jefferson Built This Country On Mastodons

Jefferson liked science more than he liked politics. He was a fastidious vegetable breeder and weather recorder, he led the American Philosophical Society for eighteen years, and he once spent a while re-engineering the plow according to Newtonian principals. He particularly loved fossils, and collected and speculated on them so avidly that he is considered “the founder of North American paleontology,” says Dr. Mark Barrow, an environmental history professor at Virginia Tech.

And he spent his life in a quiet war about the importance of american mastadons.

Read more about it here!

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T. Rex couldn’t stick out its tongue

Dinosaurs are often depicted as fierce creatures, baring their teeth, with tongues wildly stretching from their mouths like giant, deranged lizards. But new research reveals a major problem with this classic image: Dinosaurs couldn’t stick out their tongues like lizards. Instead, their tongues were probably rooted to the bottoms of their mouths in a manner akin to alligators.

Read more at: https://phys.org/news/2018-06-rex-couldnt-tongue.html#jCp

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Getting into industry: LinkedIn Profile

This post continues my discussion about academia to industry, and how I got from there to here. 

I made the decision to get a job in industry, and was immediately faced with the task of: “Ok, now how do I do that?” And the sad truth is that it’s entirely different than academia.

You need people to be able to find you from the sea of other options/people. You then need them to want to talk to you. And only then, at the interview, is the job won or lost. So we’ll start where I started, putting yourself into the ether of the job market and making yourself known. And you start by developing a LinkedIn profile.

I know a few academics have a profile, but most people in academia haven’t taken it too seriously. But recruiters and employers really do look at them. And building a good one is non-trivial.

Pick a professional photo for your profile picture. Not a photo of you pulling frogs from your experimental ponds, or pipetting like a pro (unless you’re applying for a laboratory role). Get a headshot of you looking smart and professional. And if you don’t have one, have one taken. Pay for it. It’s your first foot forward, and it’s worth the investment.

Write a “personal statement”. This should all be visible without someone having to expand the “more” tab. Make it catchy and easy to understand. “I’m a scientist passionate about making data driven decisions”. Or “I have spent a career focused on increasing understanding of statistics”

Next, list all your jobs. Include your PhD and MS and postdoc positions.  No need to go back before then, no one cares that you worked at McDonald’s in high school. (However, if you had significant work experience before or during your academic journey, consider whether the position might be relevant to include, especially if it involved related analytic work or was a management position.)  Under each position, list what you did in that position. This is remarkably similar to the academic PhD. Now, go back and make all your descriptions of your jobs entirely free of jargon. Make the bullet points simple and easy to understand. Each one should be no more than 10 words. NO MORE THAN 10 WORDS. You’re not trying to sound smart here, you’re trying to get a recruiter who has no or limited knowledge of science to know that you’re worth talking to. Feel free to steal language DIRECTLY from job posts of jobs you might want. If the job post says: “need to be able to multitask in a fast paced environment” write “I am able to multitask in a fast paced environment”. Plagiarism is ok here. Get used to this, you’re going to do it in a few different steps in the getting a job process.

Finally, go look at jobs you’re interested in on LinkedIn. At the bottom of each job, it says “you have X skills in common with others that are looking at this job”. Click on that and find out what skills your profile is missing. Do you have those skills too? Add them to your profile. Remember when I said it’s ok to plagiarize to some extent. When you have desired skills, you need to make sure other people – especially the job recruiters – know you do.  (NOTE:  there’s a huge difference in plagiarizing the description of the skills you have and making up a skill set you don’t have.)

Next week,  we’ll go over how to make your resume good enough that recruiters looking at you will want to talk to you.

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My current LinkedIn headshot. Photo credit to my great good friend Cat Thrasher

Moving into industry: You have most of the skills you need, and you can learn the few you are missing

Continuing on in my series about academia to industry, here’s how I gained the skills I needed to get a job outside of academia. 

I mentioned last week that I decided I wanted to leave academia, but then spent a full year preparing and learning what that meant. I started with an understanding that I had lots of skills, but wasn’t sure how those would translate to anything besides academia, and I ended with a job in industry. So I’ll start with the skills you do have that are applicable (yes, even you) and then talk about which ones you should make sure you have before starting to apply:

Skills you have:

Teaching and Presenting: One of the most critical traits desired in people who work with data is that they are able to communicate that data to other people. It is RARE in industry to have someone who can do the analyses AND communicate those to shareholders and relevant decision makers without them scratching their heads in confusion. But the good news: your entire academic career has prepared you for just this event. You know all those times you taught, and tried to get undergraduates to understand what you were talking about? Or those conferences where you build presentations to best present your results? They are training for doing that very thing which is VERY valuable in the world outside of academia. You already have this skill (high fives all around!).

Statistical knowledge: I know I sound like a jerk when I say this, but since moving into industry, I keep having to revise the list of things that I though everyone knew. I was asked “what is a p-value?” my first week, and have been asked more basic questions about statistics than I was ever asked in academia, even teaching undergraduates. You have a career full of experience doing experiments, collecting data, and making sense of that data by running statistical analyses. You are already ahead of the game in this regard.

Working hard: My mother told me: “The beginning of every job is the same, nose to the grindstone and work hard” and she was right. You need to be able to work harder and longer hours than you’re used to. Think back to your PhD – hours like that, only longer and in one place. The good news is that most of the multi-tasking you used to need to do is off your plate for now. The bad news is that means you have 9 + hours a day to focus on one task. Luckily, you’ve been doing this for awhile, and being a workaholic comes naturally to you. And even more luckily, this is not a long term commitment. You need to work hard at the beginning, but I’ve found it rare for people in my company (and other companies my friends have moved into) to work outside of regular business hours. This is a sprint, not a marathon.

Teaching yourself: In industry, they don’t expect you to be ready to go out of the box, like they often do in academia. In most industry environments, there is this whole process called “onboarding,” which I had never heard of before I started my current job. They know it’ll take you a little while to get up to speed. But, as an academic you’ve got a lifetime of experience teaching yourself. Great, it’ll serve you well. You can spend those first few months learning the things you’re going to need to know on the job, and that ability puts you ahead of your peers.

As you can see above, we’re already qualified, and have the potential to be successful. But you do need a few skills, at least for data science, that it is unlikely you have already attained. However, you can pick them up. Here are a few:

Python: Most academics I know code in R. The fancy ones also code in another language (I wrote simulations in C++)(I am not fancy though). While R is an excellent language for data analytics, and arguably the best in the world for statistics (come at me), for getting code into production, you need to know python. It’s similar to R, so shouldn’t be too hard, and if you have experience using R as a object oriented programming language, then it’ll be easier still. I learned python using DataCamp and strongly recommend it as a great resource. It has a jupyter notebook embedded in the site so you’re able to run code as you learn (learning by doing is important for me). But there are also books, youtube videos, coursera and udacity, and probably flash cards that will also help you learn this skill.

Querying database: In academia, we work on what are called “flat” data files, like CSV or excel files. Once you are looking at customer data, a flat file is simply too small. It’s like trying to open a NGS sequencing file – it’s just laughably impossible to do with your computer. So you need to learn how to query a database with schemas, like Postgres or MySQL. It’s a pretty simple programming language, and you’re going to learn to love to join tables. But this is a skill of taking a mountain of data, and finding the flower you want to study amidst the rocks. It takes time and practice, but is learnable.

Docker: I know I’m focusing a lot on the technology, but it has stood out to me as the one thing that’s very different between industry and academia. Docker is a way to run code locally that is entirely repeatable elsewhere. Your code is run within a “container” that is created with all the things you need installed in it, in a requirements file and an image. If you’re missing a requirement that exists locally on your computer, but is not in your file, the requirement won’t run. As a result, with the code base alone, you’re able to entirely reproduce your work, regardless of where you’re running your code from. When putting your work into deployment, this is especially awesome because you know that your container is ready to go before you deploy it. And it’s becoming ubiquitous across industry, so jump on that container bandwagon and download docker.

I’m sure I’m missing some things, and there are wealth of cultural differences that I haven’t even approached (feel free to comment below). But the gist of this post is that you ALREADY HAVE MOST OF THE SKILLS YOU NEED TO SUCCEED IN INDUSTRY. And the ones you lack are trival to learn. Now that we know this,next week we’ll talk  about one of the things you need to do to actually apply: a linkedIn profile.

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You’ve presented your research, you’ve taught. You know how to communicate results, you’re already ahead of the game.

The giant salamander might be a pyramid scheme

The world’s largest amphibian should be easy to find. The Chinese giant salamander can be as big as your entire body, and on average resemble a labrador. And while they used to be abundant, after months of searching, scientist are struggling to find even a few. 24 individuals, across 50 sites where the salamanders once thrived. Moreover, the few found all have genetic markers indicating they had escaped or been released from farms. There may not be any wild individuals left.

But this tragedy is getting worse. Based on analyses of the salamander, it’s becoming clear that it’s not one species but five. And they are all facing imminent extinction in the wild.

Read more about it here.

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The silence of the microfauna

A generation ago Rachel Carson warned us of bird die-offs from pesticides in the classic “Silent Spring”. Now, a new silence might be rocking the world, and causing an increasingly creepy silence: flying insects are dying at an alarming rate and in staggering amounts. A study published last fall documented a 76% decline in total seasonal biomass of insects in Germany, and speculated how widespread their result might be.

Unfortunately, that question is difficult to even approach because of another problem: a global decline of field naturalists who study these phenomena.

Want to learn more about this awkward intersection? Read about it here!

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