The unseen taxes on post-docs

It is tax season in most countries. As most law abiding citizens post-docs pay taxes on their wages. However, there are many unseen taxes in a post-docs life. Most of them go undocumented.

Academics on soft money generally earn less than employees with a similar workload in industry. In the first 15 years after a PhD, post-docs, earn $239,970 less than their peers in industry. This lower pay is further exacerbated by job insecurity, through short term contracts. Most post-doc positions run between 1-3 years, with the occasional longer term (5 year) appointments. Lower pay and job insecurity are often a conscious trade-off for post-docs. Yet, the undocumented taxes that comes with the job often come by surprise. Here I list a few of the undocumented and unquantified taxes I encountered which rarely included in the wage discrepancy statistics referenced above.

The Mobility tax

It is almost a given these days that for a post-doc position you will need to relocate. Sadly, few institutions provide a moving allowance for post-docs. Depending on where you move this can be a substantial amount of money you will need to set aside for this (easily $3000 or more). In addition, visits to family and friends often requires traveling by plane. Although flights these days are cheap this can still add up to a substantial amount. During my time in the US flying back home would roughly cost me $4000/year or more.

The Administration tax

Few people are fond of tax season. However, for many post-docs, providing the short duration of most contracts, have to juggle taxes for multiple countries, employers. In the US this is further complicated as health insurance is tied to your employer. This accounting task can take up substantial time, time which isn’t reimbursed in any way. Although international offices often offer support during tax season, it will obviously not provide this service for wages earned in other countries or institutes. If things get too complicated you might be forced to consult an (international) tax accountant. These services do not run cheap (easily >$100/h).

The Health tax

Most academic institutes provide health coverage for post-doc positions (although not guaranteed). But do not be fooled, even with coverage you do pay extra if not monetary, with your health. Mobility often makes it difficult to get reimbursed after leaving the country and legal disputes regarding medical bills are all but impossible.

Short term (international) contracts often result in the fact that no meaningful relationship with a family doctor can be established. The lack of familiar setting is a hurdle when looking for medical advice, especially concerning mental health issues. Furthermore, it often leaves you with a fragmented medical history which negatively influences diagnosis.

Finally, given the short term nature of most contracts it is hard to convince institutes and PIs to invest in policies which only pay of in the long term. For example, simple interventions such as ergonomic office chairs, standing desks which have huge short and long term benefits are often overlooked. These requests are often ignored as post-docs aren’t around long enough to benefit from it (at this location) or fight for it.

The Exchange Rate tax

Although most post-docs are not motivated by money, it might be prudent to consider the exchange rate between where you earn your wages and where you hope to end up. For example, the exchange rate of the dollar to the euro varied up to 20% percent points over the last 5 years. In short, when moving between countries you lose a substantial amount of earning potential on your savings not necessarily offset by higher wages in the country of origin. Obviously, if you are lucky this might also works in your favour.

The Career tax

It is often cited that scientists who worked abroad have a broader network and more international collaborators, and higher impact work. However, this mobility comes at a price. What you gain in international contacts you often lose in terms of local (political) influence. Leaving a country means you leave the local watercooler talk and employability that comes with it. Although science pretends to be unbiased, supposedly hiring the best candidate, it often hires the most available one (known quantity). If you are not at the watercooler, you are not a known quantity nor available. Unless you return within a short time frame (1-2 years) consider your chances of being employed or appointed a permanent position to diminish.

Compounding factors

All these taxes are compounded by additional factors and multiply their severity (cost). I’ve written this from my personal perspective, but I can imagine that having a family, being disabled or suffering from a (chronic) illness will increase the Administration tax and all others substantially. As a cis-gender white male I’m not going to write extensively about this, due to a lack of personal experience. However, looking at the racial/gender dynamics in the US I often wondered how people manage their anxiety with respect to these issues. So there is a serious Racial / Gender tax as well.

On multi-disciplinary research, context and navel gazing

I recently came across the publication titled:  “A global geography of synchrony for terrestrial vegetation” by Defriez and Reuman.  The paper discusses the synchrony of terrestrial vegetation as measured through cyclical patterns in the enhanced vegetation index (EVI) using a systematic analysis of the concordance of time series across the earth surface.

The paper looks at cyclical events in vegetation indices (i.e. vegetation growth) and some reference to phenology, i.e. the study of recurrent life cycle events, and it’s ecological implications would have been in place if not necessary. Sadly, no direct reference to phenology was made in the text while only two references directly mention phenology in their title.

Instead the focus of the paper was on the metric “synchrony” and the concept of the Moran effect. The Moran effect can be seen as Tobler’s first law of geography,  which postulates that “everything is related to everything else, but near things are more related than distant things “ with a time component added. For example, on a landscape level, most trees (of the same species) in a particular area will leaf out around the same time of year. Deviations from these patterns of synchrony therefore inform you about local abiotic or biotic changes in the landscape and through time, and the related species responses.  (Note, that framing this without touching on real life ecological context or phenology is rather hard).

Provided this oversight of any mention of the phenology or any larger ecological context I decided to write a very polite rebuttal using a key statement made in the paper:  “… However, our new result that geographies of synchrony in temperature and precipitation are important correlates of the geography of EVI synchrony, while true, does not follow automatically from the earlier knowledge.” The latter statement is false - and the many phenology models in existence prove this (if you want to read the literature that is). There are cases where synchrony is an interesting study topic, but most of these cases are lost at the scale on which the analysis is executed.

Sadly, my comment was not well received by the Global Ecology and Biogeography editors and I quote:  “ Critique letters need to be novel enough and interesting enough to attract readers attention at the same level as an original paper. I don’t feel that debate over whether a one sentence claim to novelty was exaggerated or not rises to that level. Especially as they go on to qualify that statement.” Aside from the point that comments only seems welcome if they generate readers not when they are factually correct (so far for peer review), I used this one sentence a problem emblematic to the whole paper to pitch my argument. A subtlety not picked up by the editors, which seemingly have an equally narrow frame of mind as the authors.

Today’s research is multi-disciplinary and failing to acknowledge this by not reading widely and referring to previous research and providing context puts the author at fault.  The editors pointedly refer to the fact that the authors qualify the statement,  sadly doing so within a very narrow context of bio-geography and landscape ecology and navel-gazing on a technical discussion surrounding the Moran effect. Research never happens in isolation. Yet the authors fails to bridge fields, ignores previous findings, adding little to the research community without this proper context (overall a rather impressive feat).

Aside from the lack of ecological context the paper does not address the fact that the analysis is dealing with land surface dynamics, not terrestrial vegetation. The EVI signal as aggregated by the authors combines multiple land cover types, ranging from urban areas, over crop fields with their distinct growing cycles (if not in rotation), over barren areas. Lumping these together at a 1 degree scale (~100x100 km) is questionable at best. Patterns derived are thus tainted by the proportion of land cover type(s) which make up a 1 degree grid cell used in the analysis. Thus, patterns are representative of the terrestrial land surface, not terrestrial vegetation. The latter greatly impacts, if not discredits, any further analysis and direct attribution to vegetation changes.

Finally, the lesson I have learned from this is that when writing a comment on a paper you need to utterly destroy the previous authors.  Do not leave a piece standing. Surely, being considerate and constructive will not “attract a sufficient readership” to merit publication.

Why scientists should learn from Aaron Swartz. part 2: on standards and frameworks

Instead of the -let's just build something that works- attitude that made the Web (and Internet) such a roaring success, they brought the formalizing mindset of mathematicians and the institutional structures of academics and defense contractors. ... With them has come academic research and government grants and corporate R&D and the whole apparatus of people and institutions that scream -pipedream-. And instead of spending time building things, they've convinced people interested in these ideas that the first thing we need to do is write standards.

In an excerpt of A Programmable Web, Aaron Swartz argues against the bureaucracy which slowed progress toward a semantic web. The Web and the data which resides on it, scientific or not, has been characterized by being used, re-used, cut, re-mixed, copied and mashed-up. This fast, transparent sharing of data is what made the Web and revolutionized how we think about data.

A common misconception in academia is that all standards and frameworks need to be defined up front, therefore creating rigid structures. This leads to publications which posit that a community is in dire need of a new standard or “framework”. Yet, all too often these works overlook easier more flexible solutions which build on existing infrastructure and more agile community driven movements.  At times they even lead to more fragmentation (obligatory XKCD comic below).

As Aaron carefully observed: “To engineers, this is absurd from the start – standards are things you write after you’ve got something working, not before!”

Although I acknowledge that standards are important, within the context of data use and re-use this carries less weight and data accessibility is the limiting factor. In this day and age, if the service isn’t created and carried by the user community, as a software package or larger initiative, chances are that there is little need for such a service (standard, or framework). Unless demonstrated to work first, diverting money to a service no-one wants or needs seems wasteful.

Creating well documented application program interfaces (APIs) to (ecological) data would go a long way in facilitating interoperability without the added cost of supporting a new aggregating platforms or standards (and the various committee members that come with it). Or, talk is cheap and fast and easy access through APIs and ad-hoc integration often trumps institutional frameworks and standardization.

Auto-align time-series of spherical images

From time to time the spherical Theta S camera used in my virtualforest.io project needs maintenance, such as a reset after a power outage or lens cleaning. These manipulations guarantee consistency in the quality of the recorded images but sometimes cause misalignment from one image to the next.

Using the internal gyroscopic data of the camera one can correct the deformations due to an inclination of the camera, which deform the horizon. Sadly, the included digital compass data is mostly unreliable without an external GPS connected. With no sensor data to rely on the challenge remains in aligning these images (automatically).

[caption id=”attachment_1708” align=”aligncenter” width=”640”] Two spherical images with an offset along the x-axis.[/caption]

In the above image corrections were made to straighten the horizon. Yet, there still is a clear shift along the x-axis of the images (or a rotation along the vertical axis of the camera). Although the images are fine, they can’t be used in a time series or a movie as their relative position changes. Due to this misalignment it also becomes really hard to monitor a specific part of the image for research purposes. One solution would be to manually find and correct these shifts. But, with ~100Gb of virtualforest.io data on file (2017-12-25) this is not a workable solution.

A standard way of dealing with misaligned images which are only translated (movement along x and y axis) is by applying phase correlation. Phase correlation is based upon aligning the phase component (hence the name) of a (dicrete) fourier transform of the image. A fourier transform translates (image) data and expresses it as a sum of sinus waves (plus their intensities / frequencies) and the relative position of these waves (or phase).  In more technical terms it translates data from the time / space domain in to the frequency / phase domain in order to among others speed up convolutional calculations or filter data based frequency (noise reduction). The use of a fourier transform in this case can be seen as a way to speed up calculating a cross-correlation between two images.

[caption id=”attachment_1701” align=”aligncenter” width=”640”] A selection of an image to use in determining lateral shifts (along the x-axis).[/caption]

In general, the phase correlation algorithm is fairly robust with respect to noise but self-similarity of vegetation corrupts the algorithm non the less. As such, I decided to use only a portion of the original spherical images to determine lateral shifts. I extracted the stems of the trees out of the image as this provides the most information with respect to lateral shifts. In a way the stems are a barcode representing the orientation of the camera. (In other use cases, where more man-made structures are included, this extra step is probably not needed.)

Using only these stem barcode sections I was able to successfully align one season of images. The result is an image time series which can be stacked for movies, analysis of the same portion of the image or used in interactive displays without any visible jumps!

[caption id=”attachment_1707” align=”aligncenter” width=”640”] The same two spherical images aligned using an offset calculated with phase correlation.[/caption]

An interactive temporal spherical display covering one year of virtualforest.io imagery can be seen at this link:

https://my.panomoments.com/u/khufkens/m/vr-forest-one-year

 

Why scientists should learn from Aaron Swartz.

"He wanted openness, debate, rationality and critical thinking and above all refused to cut corners." -- Lawrence Lessig

Aaron Swartz helped draft the RDF Site Summary (RSS) standard at age 13 and was in many respects a prodigy. As Lawrence Lessig wrote about Aaron: “He wanted openness, debate, rationality and critical thinking and above all refused to cut corners.” Sadly, he perished by his own hand after particularly severe legal action against his person for copyright infringements. The documentary The Internet’s Own Boy: The Story of Aaron Swartz  provides a homage to his  life and work.

He left a legacy of writings which excel in clarity and brilliance I’ve rarely encountered. This is further contrasted by the age at which a lot of these blog posts or essays were written. Few people come close to the the way Aaron articulated his ideas in writing.

In a series of blog posts I’ll summarize some of his ideas with respect to technology, politics and media within the context of contemporary scientific (ecological) research. The fact that his ideas and his vision remain key to what I consider solid scientific practice reflect his genius and insight.

release late, release rarely (release early, release often)

In a blog post written on July 5, 2006 (release late, release rarely) Aaron outlines how to develop software. Yet, this essay could as well apply to scientific research, going from idea to publication.

Similarly to software (pet) projects, the subject of this blog post, science projects often have strong emotions attached to it. While these emotions are truthful the content or quality of the research might not pass muster.

"When you look at something you’re working on ... you can’t help but see past the actual thing to the ideas that inspired it... But when others look at it, all they see is a piece of junk."

In science, this basically means that you should do your homework and don’t oversell your research. In peer-review reviewers will see past these claims and, rightfully so, reject manuscripts because of it. So when you publish, release late, aim for quality not quantity.  This will raise the chance of getting your work published, while at the same time increasing the likelihood of stumbling on errors. Raising the true quality, or making it look good, often highlights inconsistencies you can’t move past in good conscious.

“Well, it looks great but I don’t really like it” is a lot better then “it’s a piece of junk”.

Releasing work late means that no one knows what you are doing and you might miss out on key feedback. So, informally, research benefits from releasing early.

"Still, you can do better. Releasing means showing it to the world. There’s nothing wrong with showing it to friends or experts or even random people in a coffee shop. The friends will give you the emotional support you would have gotten from actual users, without the stress. The experts will point out most of the errors the world would have found, without the insults. And random people will not only give you most of the complaints the public would, they’ll also tell you why the public gave up even before bothering to complain."

Releasing early, means that you get valuable feedback that might otherwise would not make it into a high quality paper (released late). This feedback does not only come from experts, but as correctly observed, from everyone within a larger (research) community.

In short, scientific communication and progress requires a split approach where manuscripts should be released as late as possible, with ideas mature and solidly supported by open code and data, which was released as early as possible.

Note: Although the argument can be made that conferences serve the purpose of “early releases” I have yet to see a conference where people present truly early work. Most of the time either published or nearly published work is presented.

 

Pagination


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