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I am coding a readme for a repo in github, and I want to add a reference to a paper. What is the most adequate way to code in the citation? e.g. As a blockquote, as code, as simple text, etc?
Suggestions?
I agree with Horizon_Net that it depends on personal preference.
I like to have something which looks similar to LaTeX.
An example is provided below.
Note that it demonstrates a numeric citation style.
Alphabetic or reading style are possible too.
For numeric citation style, higher numbers should appear later in the text, and this can make satisfying numeric citation style cumbersome.
To avoid this problem, I typically use an alphabetic citation style.
"...the **go to** statement should be abolished..." [[1]](#1).
## References
<a id="1">[1]</a>
Dijkstra, E. W. (1968).
Go to statement considered harmful.
Communications of the ACM, 11(3), 147-148.
"...the go to statement should be abolished..." [1].
References
[1]
Dijkstra, E. W. (1968).
Go to statement considered harmful.
Communications of the ACM, 11(3), 147-148.
On GitHub flavored Markdown and most other Markdown flavors, you can actually click on [1] to jump to the reference.
Apologies for taking Dijkstra his sentence out of context.
The full sentence would make this example more difficult to read.
EDIT:
If the references all have a stable link, it is also possible to use those:
The field of natural language processing (NLP) has become mostly dominated by deep learning approaches
(Young et al., [2018](https://doi.org/10.1109/MCI.2018.2840738)).
Some are based on transformer neural networks
(e.g., Devlin et al, [2018](https://arxiv.org/abs/1810.04805)).
The field of natural language processing (NLP) has become mostly dominated by deep learning approaches
(Young et al., 2018).
Some are based on transformer neural networks
(e.g., Devlin et al, 2018).
From what I know, there is no built-in mechanism for this. This leads to more subjective opinions, depending on personal preference. I personally like to have a separate section called references. An example would look like the following
References
some reference
another reference
Update
Another way would be to use simple HTML embedded in your Markdown.
The alternative (to pure markdown) is to use the new GitHub integration from Aug. 2021:
Enhanced support for citations on GitHub
GitHub now has built-in support for CITATION.cff files.
This new feature enables academics and researchers to let people know how to correctly cite their work, especially in academic publications/materials.
Originally proposed by the research software engineering community, CITATION.cff files are plain text files with human- and machine-readable citation information.
When we detect a CITATION.cff file in a repository, we use this information to create convenient APA or BibTeX style citation links that can be referenced by others.
How this works
Under the hood, we’re using the ruby-cff RubyGem to parse the contents of the CITATION.cff file and build a citation string that is then shown in GitHub when someone browses a repository with one of those files1.
Now, that is helping others making your Github paper easily citable.
But, as documented, to "add a reference to a paper":
Citing something other than software
If you would prefer the GitHub citation information to link to another resource such as a research article, then you can use the preferred-citation override in CFF with the following types.
Resource
Type
Research article
article
Conference paper
conference-paper
Book
book
Extract
preferred-citation:
type: article
authors:
- family-names: "Lisa"
given-names: "Mona"
orcid: "https://orcid.org/0000-0000-0000-0000"
Have 10 years of archived article data, most of it riddled with MS Word save-as-html markup like <p class="MsoNormal">
First of all, is html tidy up to the task of stripping out MS Word generated markup, or do I need to take another approach?
Secondly, the first few years of articles are globbed together by month and stored in DB as text storage type. I'd dearly love to break these out into individual articles so I can make the site more easily searched (i.e. not bring up an entire month of news when a search term/phrase matches). The only clear pattern I have to work with to isolate the articles is the article title (in bold, between 16-20px) and the article date, generally 10px; both title and date appear prior to article body text. Is there a way to detect the <h1>-ness or <small>-ness of markup when I do not have exact markup to match against?
This may be next to impossible to answer, but just in general, what approach would you take to this unenviable task? ;-) I'm on the JVM in Scala, but could do the cleanup job on LAMP stack as well.
Ideas appreciated!
If I was you, I'd use my favorite HTML::Parser kit for Perl. If goes very well for complex and fuzzily stated problems like yours one.
I'm developing a multilingual PHP web application, and I've got long(-ish) texts that I need to translate with gettext. These are email templates (usually short, but still several lines) and parts of view templates (longer descriptive blocks of text). These texts would include some simple HTML (things like bold/italic for emphasis, probably a link here or there). The templates are PHP scripts whose output is captured.
The problem is that gettext seems very clumsy for handling longer texts. Longer texts would generally have more changes over time than short texts — I can either change the msgid and make sure to update it in all translations (could be lots of work and very error-prone when the msgid is long), or I can keep the msgid unchanged and modify only the translations (which would leave misleading outdated texts in the templates). Also, I've seen advice against including HTML in gettext strings, but avoiding it would break a single natural piece of text into lots of chunks, which will be an even bigger nightmare to translate and reassemble, and I've also seen advice against unnecessary splitting of gettext strings into separate msgids.
The other approach I see is to ignore gettext altogether for these longer texts, and to separate those blocks in external subtemplates for each locale, and just include the one for the current locale. The disadvantage is that I'm separating the translation effort between gettext .po files and separate templates located in a completely different location.
Since this application will be used as a starting point for other applications in the future, I'm trying to come up with the best approach for the long term. I need some advice for best practices in such scenarios. How have you implemented similar cases? What turned out to work and what turned out a bad idea?
Here's the workflow I used, on a very heavily-trafficked site that had about several dozen long-ish blocks of styled textual content, translated into six languages:
Pick a text-based markup language (we used Markdown)
For long strings, use fixed message IDs like "About_page_intro_markdown" that:
describes the intent of the text
makes clear that it will be interpreted in markdown format
Have our app render "*_markdown" strings appropriately, making sure to allow only a few safe HTML tags
Build a tool for translators that:
shows them their Markdown rendered in realtime (sort of like the Markdown dingus)
makes it easy for them to see the now-authoritative base language translation of the text (since that's no longer in the msgid)
Teach translators how to use the new workflow
Pros of this workflow:
Message IDs don't change all the time
Because translators are editing in a safe higher-level syntax, hard to mess up HTML
Non-technical translators found it very easy to write in Markdown, vs. HTML
Cons of this workflow:
Having static unchanging message IDs means changes in the text need to be transmitted out of band (which we'd do anyway, as long text can raise questions about tone or emphasis)
I'm very happy with the way this workflow operated for our website, and would absolutely recommend it, and use it again. It took a couple of days to get started, but it was easy to build, train, and launch.
Hope this helps, and good luck with your project.
I just had this particular problem, and I believe I solved it in an elegant way.
The problem: We wanted to use Gettext in PHP, and use primary language strings as keys translations. However, for large blocks of HTML (with h1, h2, p, a, etc...) I'd either have to:
Create a translation for each tag with content.
or
Put the entire block with tags in one translation.
Neither of those options appealed to me, so this is what I did:
Keep simple strings ("OK","Add","Confirm","My Awesome App") as regular Gettext .po entries, with the original text as the key
Write content (large text blocks) in markdown, and keep them in files.
Example files would be /homepage/content.md (primary / source text), /homepage/content.da-DK.md, /homepage/content.de-DE.md
Write a class that fetches the content files (for the current locale) and parses it. I then used it like:
<?=Template::getContent("homepage/content")?>
However, what about dynamic large text? Simple. Use a templating engine. I decided on Smarty, and used it in my Template class.
I could now use templating logic.. within markdown! How awesome is that?!
Then came the tricky part..
For content to look good, at times you need to structure your HTML differently. Consider a campaign area with 3 "feature boxes" beneath it. The easy solution: Have a file for the campaign area, and one for each of the 3 boxes.
But I could do better than that.
I wrote a quick block parser, so I would write all the content in one file, and then render each block seperately.
Example file:
[block campaign]
Buy this now!
=============
Blaaaah... And a smarty tag: {$cool}
[/block]
[block feature 1]
Feature 1
---------
asdasd you get it..
[/block]
[block feature 2] ...
And this is how I would render them in the markup:
<?php
// At the top of the document...
// Class handles locale. :)
$template = Template::getContent("homepage/content", [
"cool" => "Smarty variable! AWESOME!"
]);
?>
...
<title><?=_("My Awesome App")?></title>
...
<div class="hero">
<!-- Template data already processed! :) -->
<?=$template->renderBlock("campaign")?>
</div>
<div class="featurebox">
<?=$template->renderBlock("feature 1")?>
</div>
<div class="featurebox">
<?=$template->renderBlock("feature 2")?>
</div>
I'm afraid I can't provide any source code, as this was for a company project, but I hope you get the idea.
gettext wasn't really designed for translating large pieces of text.
fwiw I've included basic HTML (strong, a, etc) in gettext strings as I was confident our translators knew what they were doing (mostly right) and that the translations would be well tested.
I've tried the approach of breaking up the text into one string per paragraph. Roughly as it looks odd if there's one paragraph of English in the middle of the text. Where one of those strings have changed this has meant that we have had to wait for translations before releasing a new version, which has slowed us down. On the plus side it's easy for translators to see which part of the text has changed. This approach worked well for the one application I've tried it with.
Splitting some text out into external locations also worked, but it caused management overhead, rather than just a .po file or two, there was a whole bunch of other text that had to be manually compared to the English version and updated accordingly. This is doable if you remember to provide notes to your translators explaining where and what the difference was in the English version.
I'm still not sold on either approach myself.
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I work as support staff in a biology research institute as a student, and Perl seems to be used everywhere. Not for every single project, but it seems that more than half the people here have a few Perl books in/on their office/desk.
Why is Perl used so much in biology?
Lincoln Stein highlighted some of the saving graces of Perl for bioinformatics in his article:
How Perl Saved the Human Genome Project.
From his analysis:
I think several factors are responsible:
Perl is remarkably good for slicing, dicing, twisting, wringing, smoothing, summarizing and otherwise mangling text. Although the biological sciences do involve a good deal of numeric analysis now, most of the primary data is still text: clone names, annotations, comments, bibliographic references. Even DNA sequences are textlike. Interconverting incompatible data formats is a matter of text mangling combined with some creative guesswork. Perl's powerful regular expression matching and string manipulation operators simplify this job in a way that isn't equalled by any other modern language.
Perl is forgiving. Biological data is often incomplete, fields can be missing, or a field that is expected to be present once occurs several times (because, for example, an experiment was run in duplicate), or the data was entered by hand and doesn't quite fit the expected format. Perl doesn't particularly mind if a value is empty or contains odd characters. Regular expressions can be written to pick up and correct a variety of common errors in data entry. Of course this flexibility can be also be a curse. I talk more about the problems with Perl below.
Perl is component-oriented. Perl encourages people to write their software in small modules, either using Perl library modules or with the classic Unix tool-oriented approach. External programs can easily be incorporated into a Perl script using a pipe, system call or socket. The dynamic loader introduced with Perl5 allows people to extend the Perl language with C routines or to make entire compiled libraries available for the Perl interpreter. An effort is currently under way to gather all the world's collected wisdom about biological data into a set of modules called "bioPerl" (discussed at length in an article to be published later in the Perl Journal).
Perl is easy to write and fast to develop in. The interpreter doesn't require you to declare all your function prototypes and data types in advance, new variables spring into existence as needed, calls to undefined functions only cause an error when the function is needed. The debugger works well with Emacs and allows a comfortable interactive style of development.
Perl is a good prototyping language. Because Perl is quick and dirty, it often makes sense to prototype new algorithms in Perl before moving them to a fast compiled language.
Sometimes it turns out that Perl is fast enough so that of the algorithm doesn't have to be ported; more frequently one can write a small core of the algorithm in C, compile it as a dynamically loaded module or external executable, and leave the rest of the application in Perl (for an example of a complex genome mapping application implemented in this way, see http://waldo.wi.mit.edu/ftp/distribution/software/rhmapper/).
Perl is a good language for Web CGI scripting, and is growing in importance as more labs turn to the Web for publishing their data.
The real answer probably has less to do with Perl than you think. Many of the things that happen are accidents of history. At the time, way back when, Perl was pretty popular, Java was getting more popular, not too many people were paying attention to Python, and Ruby was just getting started.
The people who needed to get work done used Perl and made some libraries in Perl, and other people started using those libraries. Once people start using something that is moderately useful to them, they tend not to switch (economists call those "switching costs"). From there, even more people start using it because a lot of other people are using it.
The same evolution might not happen today. I'd say that Perl, Python, and Ruby are all completely adequate and up to the task. All the things that mobrule quotes from Lincoln Stein could apply to any of the three today. If everyone had to start from scratch today, any one of those languages could be the one that everyone uses.
I've noticed, from my own client base though (a very small and unrepresentative sample of biotech), that the people pushing the programming for a lot of the biological stuff seemed to be at least part-time sysadmins who were supporting scientists. The scientists worried about the science and did some light programming, but the IT support people were doing a lot of the heavy lifting for the non-science parts. Perl is very well positioned as a sysadmin tool since it's the duct-tape of the internet.
Probably because Perl is good at manipulating strings, and much research in genetics involves the manipulation of veeery long "ACTGCATG..." strings. Just guessing...
I use lots of Perl for dealing with qualitative and quantitative data in social science research. In terms of getting things done (largely with text) quickly, finding libraries on CPAN (nice central location), and generally just getting things done quickly, it can't be surpassed.
Perl is also excellent glue, so if you have some instrumental records, and you need to glue them to data analysis routines, then Perl is your language.
Perl seems to be the language of choice for bioinformatics - there's even an O'Reilly title on just this subject: Beginning Perl for Bioinformatics.
Perl is very powerful when it comes to deal with text and it's present in almost every Linux/Unix distribution. In bioinformatics, not only are sequence data very easy to manipulate with Perl, but also most of the bionformatics algorithms will output some kind of text results.
Then, the biggest bioinformatics centers like the EBI had that great guy, Ewan Birney, who was leading the BioPerl project. That library has lots of parsers for every kind of popular bioinformatics algorithms' results, and for manipulating the different sequence formats used in major sequence databases.
Nowadays, however, Perl is not the only language used by bioinformaticians: along with sequence data, labs produce more and more different kinds of data types and other languages are more often used in those areas.
The R statistics programming language for example, is widely used for statistical analysis of microarray and qPCR data (among others). Again, why are we using it so much? Because it has great libraries for that kind of data (see bioconductor project).
Now when it comes to web development, CGI is not really state of the art today, but people who know Perl may stick to it. In my company though it is no longer used...
I hope this helps.
Perl basically forces very short development cycles. That's the kind of development that gets stuff done.
It's enough to outweigh Perl's disadvantages.
Bioinformatics deals primarily in text parsing and Perl is the best programming language for the job as it is made for string parsing. As the O'Reilly book (Beginning Perl for Bioinformatics) says that "With [Perl]s highly developed capacity to detect patterns in data, Perl has become one of the most popular languages for biological data analysis."
This seems to be a pretty comprehensive response. Perhaps one thing missing, however, is that most biologists (until recently, perhaps) don't have much programming experience at all. The learning curve for Perl is much lower than for compiled languages (like C or Java), and yet Perl still provides a ton of features when it comes to text processing. So what if it takes longer to run? Biologists can definitely handle that. Lab experiments routinely take one hour or more finish, so waiting a few extra minutes for that data processing to finish isn't going to kill them!
Just note that I am talking here about biologists that program out of necessity. I understand that there are some very skilled programmers and computer scientists out there that use Perl as well, and these comments may not apply to them.
People missed out DBI, the Perl abstract database interface that makes it really easy to work with bioinformatic databases.
There is also the one-liner angle. You can write something to reformat data in a single line in Perl and just use the -pe flag to embed that at the command line. Many people using AWK and sed moved to Perl. Even in full programs, file I/O is incredibly easy and quick to write, and text transformation is expressive at a high level compared to any engineering language around. People who use Java or even Python for one-off text transformation are just too lazy to learn another language. Java especially has a high dependence on the JVM implementation and its I/O performance.
At least you know how fast or slow Perl will be everywhere, slightly slower than C I/O. Don't learn grep, cut, sed, or AWK; just learn Perl as your command line tool, even if you don't produce large programs with it. Regarding CGI, Perl has plenty of better web frameworks such as Catalyst and Mojolicious, but the mindshare definitely came from CGI and bioinformatics being one of the earliest heavy users of the Internet.
Perl is very easy to learn as compared to other languages. It can fully exploit the biological data which is becoming the big data. It can manipulate big data and perform good for manipulation data curation and all type of DNA programming, automation of biology has become easy due languages like Perl, Python and Ruby. It is very easy for those who are knowing biology, but not knowing how to program that in other programming languages.
Personally, and I know this will date me, but it's because I learned Perl first. I was being asked to take FASTA files and mix with other FASTA files. Perl was the recommended tool when I asked around.
At the time I'd been through a few computer science classes, but I didn't really know programming all that well.
Perl proved fairly easy to learn. Once I'd gotten regular expressions into my head I was parsing and making new FASTA files within a day.
As has been suggested, I was not a programmer. I was a biochemistry graduate working in a lab, and I'd made the mistake of setting up a Linux server where everyone could see me. This was back in the day when that was an all-day project.
Anyway, Perl became my goto for anything I needed to do around the lab. It was awesome, easy to use, super flexible, other Perl guys in other labs we're a lot like me.
So, to cut it short, Perl is easy to learn, flexible and forgiving, and it did what I needed.
Once I really got into bioinformatics I picked up R, Python, and even Java. Perl is not that great at helping to create maintainable code, mostly because it is so flexible. Now I just use the language for the job, but Perl is still one of my favorite languages, like a first kiss or something.
To reiterate, most bioinformatics folks learned coding by just kluging stuff together, and most of the time you're just trying to get an answer for the principal investigator (PI), so you can't spend days on code design. Perl is superb at just getting an answer, it probably won't work a second time, and you will not understand anything in your own code if you see it six months later; BUT if you need something now, then it is a good choice even though I mostly use Python now.
I hope that gives you an answer from someone who lived it.
I've found a few related questions, like Python vs. Perl (now deleted) and Is Perl Worth it? (now deleted), but I can't seem to find anything that directly addresses this question.
Is there a legitimate future in Perl? I work in a Perl shop right now, and I came from PHP so I see some of the advantages of an arguably "lower" level language when doing things on the server-level, but it seems to me a lot of the tasks in Perl can be performed more quickly in PHP, and SOME ARGUE (subjective, not my opinion) that Python does these tasks in a more explicit way that's easier to maintain.
Is having this job on my resume ultimately going to make me less employable, especially if the language no longer grows?
A few notes:
I love Perl, so don't think I'm bashing the language. It's fun to use and we use a fairly verbose syntax that is relatively easy to maintain.
I realize that "Vaporware" is a buzzword that isn't necessarily applicable to this situation, because Perl doesn't have a marketing department and they're not "promising" Perl 6 by any date.
I realize that CPAN keeps the community going, so whether Perl 6 comes out or not people continue to build modules that increase possibilities in the language, but that doesn't mean that industry shops realize this, and switch to "more supported" languages that keep coming out with revised versions of the language like Python and (especially) PHP.*
EDIT {CLARIFICATION}
Cade Roux and Telemachus both brought up good points about whether or not your future can be defined by your resume.
To be honest, this was brought up when one of my former employers said "I don't hire anyone with Perl as their last job. That's OLD technology." This was a PHP shop, so take all that with a grain of salt.
Now without defaming my former employer, she's not a tech person AT ALL, so she was really expressing an opinion of a layperson, and in this case my question was more along the lines of "Is there a stigma on this particular technology placed on it by people who don't utilize it?", specifically more along the lines of people who may have had past experience with similar employers. I'm not asking you to look into the future with a magic glass to assume what the next "hot" language would be, but rather if this particular language (which is accused of stunted growth, again by laypeople) has negative connotations placed upon it.
I hope that makes a little more sense.
Plenty of shops - including on Wall Street - heavily use Perl and will continue to do so.
However, I have never seen a PHP or Python used in this industry (not saying it is not used, but that I never encountered. Purely personal anecdote. Nor have I EVER heard any conversation of "Perl can not do X that Python can, let's use Python").
Perl6 is irrelevant to job picture.
Many shops are still on 5.8 or G-d forbid 5.6
More importantly, perl5 continues to evolve, including with features/ideas from Perl6. See Perl 5.10 and 5.11
Plus evolution includes really cool framework like Moose etc...
I can probably come up with more bullets later, but the summary is that no, having a Perl job will in no way negatively affect your career prospects.
However, knowing nothing but Perl may affect it negatively, so make sure you know Java, C#, C++ or something besides dynamic interpreted languages. Not many shops would hire "Perl Only" developer, even if they gladly hire "Perl + other stuff" ones.
See Tim Bunce's Perl Myths slides on slide share.
In short, Perl is not dead and has lots of jobs available.
Anyone who actually watches the development of Perl, would know that that there has perhaps been more work on the Perl language in the past decade, than in the previous decade.
This has been spurred on by the introduction of Perl6.
The introduction of Perl 6 spurred on, the now deeply ingrained, testing culture.
Just look at how much the Rakudo implementation of Perl 6, is tested:
Rakudo Progress http://rakudo.de/progress.png
There has also been a lot of back-porting of Perl 6 features into Perl 5.
For example, the Perl 6 "switch" statement
#!/usr/bin/perl
use strict;
use warnings;
use 5.10.1;
# or
use feature qw'switch say';
my $str = "testing 123";
given( $str ){
when(/(\d+)/){
say $1;
}
when( [0..10] ){
say $_, 'is equal to some number between 0 and 10';
# given, sets the current topic "$_"
}
}
There are few languages I would tie my career to. Perl will always be there and it will always be the best tool for certain kinds of jobs. But this is true for many languages. However, there are also languages which have more competition in some of the spaces where they are used. Perl is one language that has a lot more strong niches.
Still, you wouldn't restrict yourself to using just one language for your entire life - or even in one project if there are better options to solve a problem.
Career-wise, there are basic technologies which are fairly universally used, and of these I think a few of the most valuable are: relational database concepts and SQL, XML/HTML/HTTP/DOM, regular expressions. These are all basically independent of any particular vendor or language, and if you are strong in these areas, choice of language and platform are going to be informed by the problem being addressed.
Perl is, and always will be, a practical language for manipulating large amounts of data. I work in an industry where moving, converting, and parsing large amounts of text and image data is what we do, and I couldn't live without Perl.
Likewise, if you're a sysadmin (especially a Unix one), Perl is a necessary tool. There are tons of places where you need to be able to whip up a quick and dirty application that runs right along with the shell functions.
Languages have niches. Perl has a big stable niche, in many ways much more stable than fad-driven web languages. PHP, for example, is a nice little web language, but its saving grace is that it's quick and easy to develop in, not that it is a particularly great language. I'll tend to use PHP over Perl for web applications (though I use Python over PHP, if I have time), but 90% of the stuff I do in my day-to-day would be nearly impossible in PHP, and is flat trivial in Perl.
#Nate: I love Python. LOVE it. I actually worry that I love it too much, and I'm being irrational about it. PHP is a nice tool, but when your main selling point is "Quick and Easy" then you're running a risk. That was the big push behind original Visual Basic, and we all know how that worked out.
I'd discourage you from putting Perl on your resume - there's already too many people in the perl market and we don't want any more! ... just kidding.
The past is supposedly no guide to the future, but, despite having plenty of C (etc.) and Java in my 'skills toolbag' I've seen more gainful employ from my Perl than anything else over the last decade.
I suspect that offshore-perl-new-build may not be the biggest market in the future, but there's certainly active development in the city and media industries in the UK.
Otherwise, I'd just agree with the points above. Technicians with diverse skills are more able to pick the right tools, and less inclined to 'get religious' about language choice.
If you're looking at a post where the non-technical management have a strong point of view about what technology should and shouldn't be used - I'd place that one in the 'avoid' pile.
To add another separate answer - as you have noted - there is a very real danger when dealing with recruiters and others that your resume will be interpreted and things inferred that are not necessarily how you see yourself, and you might get pigeon-holed.
This WILL happen both ways - too much variation and you aren't an expert in anything OR too little variation and you are only good at one thing.
I don't have a simple answer for combatting that, except to ensure that you emphasize portable skills and also achievements which are independent of technology - making the company more money, landing new business, making new markets, etc.
Perl is another tool in your toolbox. If I have an opening and one person is narrow focused to a specific technology, and another has a broad range of skills I would be more inclined to hire the one with the wider range of skills even if they might not be quite as deeply knowledgeable. Some one who has a wide range of skills on a range of platforms is someone who can think, innovate and adapt.
I don't understand the point of this question. You have a job and you already know Perl. You can ask whether or not to learn new languages and which ones to learn (please don't, but you could), but none of us can or should predict whether or not you're going to get another job using Perl.
You ask, "Is having this job on my resume ultimately going to make me less employable, especially if the language no longer grows?"
Well, it's better than a blank resume, and you can't change your past, so really what are we talking about here?