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Digital object identifier

From Wikipedia, the free encyclopedia

For the use of digital object identifiers on Wikipedia, see Wikipedia:Digital Object Identifier.
Digital object identifier
DOI logo.svg
Acronym DOI
Introduced 2000 (2000)
Managing organisation International DOI Foundation

In computing, a Digital Object Identifier or DOI is a persistent identifier or handle used to uniquely identify objects, standardized by the ISO.[1] An implementation of the Handle System,[2][3] DOIs are in wide use mainly to identify academic, professional, and government information, such as journal articles, research reports and data sets, and official publications though they also have been used to identify other types of information resources, such as commercial videos.

A DOI aims to be "resolvable", usually to some form of access to the information object to which the DOI refers. This is achieved by binding the DOI to metadata about the object, such as a URL, indicating where the object can be found. Thus, by being actionable and interoperable, a DOI differs from identifiers such as ISBNs and ISRCs which aim only to uniquely identify their referents. The DOI system uses the indecs Content Model for representing metadata.

The DOI for a document remains fixed over the lifetime of the document, whereas its location and other metadata may change. Referring to an online document by its DOI provides more stable linking than simply using its URL, because if its URL changes, the publisher only needs to update the metadata for the DOI to link to the new URL.[4][5][6]

The developer and administrator of the DOI system is the International DOI Foundation (IDF), which introduced it in 2000.[7] Organizations that meet the contractual obligations of the DOI system and are willing to pay to become a member of the system can assign DOIs.[8] The DOI system is implemented through a federation of registration agencies coordinated by the IDF.[9] By late April 2011 more than 50 million DOI names had been assigned by some 4,000 organizations,[10] and by April 2013 this number had grown to 85 million DOI names assigned through 9,500 organizations.

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>> [MUSIC PLAYING] [MUSIC - ROSSINI, "RANZ DES VACHES" FROM WILLIAM TELL] >> [MUSIC - THE ENGLISH BEAT, "MARCH OF THE SWIVEL HEADS"] >> [APPLAUSE AND CHEERING] >> DAVID MALAN: So this is CS50. My name is David Malan. And 73% of you have no prior experience with computer science, contrary to what you might think. So today we thought we would chip away at that lack of familiarity, but also give you a sense of, for those of you with more comfort, which directions you can go this semester. >> So let's start with this. I really have no idea what's inside of a computer, even though, like you, I use it every day. But it's some kind of box, and there's not many inputs into it. Minimally, there's, what? Probably a power cord. >> And indeed with this one ingredient, electricity, we seem to be capable of doing quite a bit these days. But at the end of the day, we have to represent the things that we care about. We have to represent information in some form. And you're probably at least vaguely familiar with the idea by binary or bits somehow or other, computers reduced to zeros and ones. But can we embrace that and at least put a bit of light to that? >> So I have these little desk lamps here. I have an electrical outlet here. And I'm going to propose that inside of my computer is at least one of these things, something capable of being switched on or off. In this case, it's indeed a desk lamp, but at the lower level, it's something called a transistor. >> But in our world, it's a desk lamp, so I'm going to go ahead and plug this into my electricity here. And I claim that using this simple, simple device, this simple switch, I can represent information. For instance, right now, I am representing nothing, right? I'm representing what I'll call 0 or false, the opposite of something actually being present. But if I simply turn this switch, now I've represented a 1. So using this very simple piece of memory, if you will, I can represent information. >> Now unfortunately, my computer can't do all that much. It can only represent two values in the whole world-- 0 or 1. But what's an obvious solution, now, if we want to expand our computer's memory and represent more than just 0 and 1? >> Well, let's grab another such bit. Let's grab another switch, another transistor, however you'd like to think about it. Let me go ahead and plug this into my computer as well. And I'm going to claim, now, that by using a bit more electricity and turning more of these switches on and off, I can represent more such information. >> So right now, this is 1. If I want to now represent 2, I could do this. But typically, convention, as we'll eventually see, will have me do this. So this is 0, this is 1. This would be 2. And not surprisingly, this would be 3. >> So in this way, still, can we count up even further? If I get a third bit, a third switch, what's the highest number I can now count up to from 0? So 6 if I'm starting at 0, right? Because if I turn this light on and actually plug this third and final light into my electrical socket here, then I have the ability to represent any of two values here, two values here, two values here-- and so I can represent 2 times 2 times 2, or eight possible values. And if I start accounting at 0, so that's 0, 1, 2, 3, 4, 5, 6, 7. >> So this binary. It really is as simple as that. And I'd argue that this is actually quite familiar to most everyone in this room. Let me go ahead and open a little text editor here. >> And you might recall from grade school that we had things like the hundreds place, the tens place, and the ones place. And recall that if you had some decimal number, like something random like 123, you would essentially write that out in the form of these three columns. And why is 1, 2, 3 what we know as 123? Well, in the leftmost column, we have one 100 plus two 10s, so that's 120, plus three 1s, so that's 123. >> Now this world that we just illuminated is exactly the same as you've been familiar with for years, except now, our columns aren't powers of 10. They're just powers of 2. So whereas that's the ones place, this is going to be the twos place, this is going to be the fours place. >> And because I am only using the simplest of mechanisms to turn things on and off-- electricity is flowing or electricity is not flowing-- I don't quite have the same expressive range as 0 through nine. We're going to keep it super simple in this world of computers. I only have 0 or 1-- off or on, false or true. >> And so what I'm representing right now is 1, 1, 1, because each of these lights is illuminated. Well, that gives me one 4 plus one 2, so that's 6, plus one 1, and that's 7. And ergo does this sequence of three bits represent the number 7. >> So all this time, inside of your computer, have been any number of transistors, any number of bits. But at the end of the day, we can represent information as simply as that. Now unfortunately, we've only counted up to 7 in CS50 thus far, but hopefully we can do a bit better than that. And indeed we can. >> Suppose that we as humans just arbitrarily decided that we are going to associate numbers like 1 and 2, 3, 4, 5, 6, 7, with specific letters of the alphabet. And for historical reasons, I'm going to start somewhat arbitrarily, but I'm going to say, humans, we are going to decide as a standard, globally, that 65 represents the number the letter A. 66 will represent B. Dot, dot, dot. 90 will represent the letter Z. >> And let's suppose, if we really put some thought into it, we could come up with numbers for exclamation points and lowercase letters, and indeed, other people have done that for us. So now we had bits with which we can represent numbers, numbers with which we can represent letters, and with letters can we now start composing emails and printing characters on the screen. >> So let me invite, if I could, eight brave volunteers-- who don't mind appearing not only on camera but on the internet-- to come up here and represent eight such bits, rather than these three. So how about one, two? How about three? How about four in light blue, five on the end? About someone over here? Six in front, seven in front, and eight in front, as well. >> So I just so happened to come prepared with a whole bunch of slips of paper. And on these pieces of paper are numbers that represent what columns you guys are going to represent. So you will be-- what's your name? >> STUDENT: Anna Leah. >> DAVID MALAN: Anna Leah, you will be the 128s column. You are? >> STUDENT: Chris. >> DAVID MALAN: Chris will be the 64s column. You are? >> STUDENT: Dan. >> DAVID MALAN: Dan will be the 32s column. >> STUDENT: Pramit. >> DAVID MALAN: Pramit will be the 16s column. >> STUDENT: Lillian. >> DAVID MALAN: Lillian will be the 8s. >> STUDENT: Jill. >> DAVID MALAN: Jill will be the 4s column. >> STUDENT: Mary. >> DAVID MALAN: Mary will be the 2s, and? >> STUDENT: David. >> DAVID MALAN: David will be the 1s column. So if you guys could step a little forward so that everyone can see. What you guys don't see is that on the back of these slips of paper is a little cheat sheet that's about to instruct these eight bits to either raise their hand or not raise their hand. If their hand goes up, they're representing a 1. If their hand stays down, they're representing a 0. >> Meanwhile, we the audience should be able to figure out, based on this mapping, what three-letter word these folks are about to spell out. So in just a moment, you're going to read the first line off the back of your cheat sheet, and you're either going to raise or not raise your hand. If you're a 1, you raise, if you're a 0, you stand there awkwardly, just like that. Go. What number, first and foremost, are these guys representing? >> 66. 66, right? We have a 1 in the 64s column, a 1 in the 2s column. That gives me 66, so that appears to be representing B. So you guys have spelled-- OK, that's enough. B. >> So now let's move onto our second letter. Go. Who's quickest at math here? So 79. Again, if we add up all of the columns in which there's a 1, currently, just like we did before with the simplest of examples of 7, we now get the number 79. Which according to our mapping is the letter O. So we're almost there. B, O. And lastly, go. >> What are they representing now? Less consensus. That's just an absolute murmur. Yes, it's in fact 87. Good. >> So if we now map that back up to-- let's start calling our ASCII chart, American Standard Code for Information Interchange. That gives us the letter-- not "bo" but "bow." And that's a perfect cue for you guys to take a bow and head on back. Thank you very much. >> [APPLAUSE] >> DAVID MALAN: You can keep them. Though actually, would anyone like a desk lamp, also? >> [HOOT FROM AUDIENCE] >> DAVID MALAN: Desk lamp? >> [LAUGHTER] >> DAVID MALAN: Really? Desk lamps for everyone? All right. So starting with the very simplest of principles, we've now not only counted up from 0 all the way up to 7, we've assumed that just by throwing more bits or more lights or more transistors at this problem, we can represent bigger and bigger numbers, and ergo, bigger and bigger ranges of alphabets, like English. And just let's take on faith for today that similarly could we start to represent graphics and video and any number of other media with which we're familiar today. >> So this is CS50, and in this class alongside of you are, again, very many classmates who have as little experience as you. And I mention this only because quite often, including as recently as one of the freshman advising events and at last spring's sophomore advising event, we often hear students disclaim when coming up to the CS table, well, I've been thinking about taking this intro class, but I'm not really a computer person. Or, but everyone surely knows more than me. And I put this in the biggest font possible, to convey this message that that's not in fact the case. >> And if you're wondering, should I, in fact, be here? Realize that not only is this course's title Introduction to Computer Science, it is Introduction to Computer Science I. So there is indeed a second such introduction. So you're not, in fact, in the wrong place. And among the goals I have for today are to assuage any such concerns you might have, but also to paint a picture of what's in store for students less and more comfortable alike in this course. >> But first, a word on one of the handouts you have today, among which are a number of FAQs. It's been a vision of ours for some time now to introduce a new grading option into this course-- namely, SAT/UNSAT. Philosophically for me, it is much much, much more important that the students in this class engage with the material, be challenged by the material, and worry far, far less about the mechanics of actual scores and letter grades at semester's end, but truly embrace the course and its material. And really this feels, more generally, for what's interesting to them, to feel challenged and rewarded but without fear of failure. >> And indeed, this too is a recurring theme in this and other introductory courses in other fields, that you have this trepidation when it comes to putting one's toes in unfamiliar waters. I myself, back in 1995, was a freshman. I was very much focused on being a Gov concentrator here. And yet I'd always grown up with a bit of an interest in computer science. I was always curious. >> But back then, even, I had this fear of even stepping foot in CS50, so much so that I didn't even shop it freshman year. And the only reason I put a foot in the door sophomore year was because I was allowed to take it pass/fail. But even pass/fail required that I get up the nerve to make an appointment with Professor Kernehan at the time, bring this big sheet of paper, and ask him for his signature and his permission to explore these unfamiliar waters. >> And it hasn't helped in recent years that when doing this in CS50, when we used to be pass/fail, similarly would dozens or hundreds of your classmates have to come up, God forbid, at the front of Sanders with this form, that in some minds represents an inability, I dare say, to perform are your peers' level. Which is ridiculous, but I do think there's that mentality. And there's never been in this culture of SAT/UNSAT, or pass/fail more generally, in this course, or really on this campus. >> So this year we changed that. I would be ecstatic half of this class or more ended up taking CS50 SAT/UNSAT. In a year's time, it would be wonderful if almost everyone is. Thereafter perhaps we'll work on letter grades at Harvard College more generally. But for now, we'll do this within our own sphere, and I would heartily encourage you to review those FAQs and ask questions as you see fit, so that hopefully you, unlike me, won't quite have that same fear factor when exploring what's probably an unfamiliar place. >> So what is CS50? It is an introduction to the intellectual enterprises of computer science and the art of programming. But what does that really mean? >> Well, thus far, we talked very briefly about representing information. But suppose that we actually want to do something with it. We need to introduce the notion of what we'll call an algorithm. An algorithm is a procedure, a process, a set of instructions for doing something. >> And an algorithm can be something super simple. For instance, an example with which some of you might be familiar is this thing here. So this book here is increasingly dated, but once upon a time, it contained a whole lot of names and phone numbers. And indeed, if I wanted to find someone in this phone book-- say, someone named Mike Smith-- I could find Mike Smith in any number of fairly straightforward ways. I could start at the beginning and move on to page 1, not there. Page 2, not there. Page 3. Is that algorithm, is that process, correct? >> So it is correct, right? I'm kind of an idiot for doing it in that manner, but eventually I will find the surname S, and hopefully Mike is in that section, and I will become done with my algorithm. But surely it's not intuitive. Most every reasonable human in this room would not have done that. What would you have done? >> You'd have gone straight to the middle, right? Roughly to the middle. And you realize, oh, these are the Ms. So Mike Smith, last name being Smith, is not, clearly, then in the left half of the book. He must be toward the S's in the right. And at this point, though most of us don't do this in reality, we can literally tear this problem in half. >> [CHEERING AND APPLAUSE] >> DAVID MALAN: Thank you. >> [CHEERING AND APPLAUSE] >> DAVID MALAN: You can literally tear this problem in half, leaving me with, literally, a problem half as big. So if this phone book was-- and it probably was-- about 1,000 pages, now it's only 500. If I do this again and I realize, oh, damn, I went too far, I'm in the Ts section, I can similarly-- figuratively or literally-- rip the phone book-- it was actually much easier that time. I can literally rip the phone book in half, leaving me now with not 1,000, not 500-- 250 pages. And I can go 125, and half of that, and half of that, and half of that, until finally I'll be left with just one single page. >> [LAUGHTER] >> DAVID MALAN: That's the part I fail on. One single page on which Mike hopefully is. Now those different algorithms can be sort of assessed or evaluated in different ways. The first one was very linear, right? Turn page, look for Mike. Turn page, look for Mike. It's very linear. If there's one more page in the phone book, it's probably going to take me one more second, one more unit of time, however we're computing time. >> So I might draw like this this line here, whereby as the size of the problem increases from left to right-- phone book gets smaller to bigger-- and time is going to increase on the vertical axis, the bigger the phone book is. So n is just a general variable that computer scientists use to represent some value, some number. So n is going to increase linearly. Double the size of the phone book, it's going to take me twice as much time, most likely, to find Mike. >> Now I could have been smart about this, right? I was getting bored quickly. Could have done this by twos. So two pages, then four, then six, then eight. And I could start flying through it a little faster, albeit at minor risk of overshooting Mike, but that curve isn't going to be all that different. It's still going to be a straight line, but slightly faster. >> But what did I do? I actually did something fundamentally better. I achieved what we'll call logarithmic time, log of n, whereby this green line has a much, much, much less straight edge to it. And rather, it suggests, as it sort of approaches infinity ever so gradually, that I could actually take a 1,000-page phone book, double its size next year-- because suppose a lot more people move into town. >> So now I've got 2,000 pages, but how many more steps is that smarter algorithm going to take? Just one. I mean, that's a powerful thing. If we go to 4,000 pages next year, that's going to take me only two more steps. So you can throw bigger and bigger problems at me, not unlike the web is throwing bigger and bigger problems every day at Googles and Facebooks of the world, and it's not such a big deal. Because I put more thought and care into my algorithm with which to solve problems efficiently. >> And indeed, that will be one of the goals of this course. You will, along the way, learn how to program. You'll learn how to program in any number of languages. But at the end of the day, the course is about solving problems and getting better at solving problems-- and, as in cases like this, solving problems more efficiently. >> Now thus far, we've done this fairly intuitively. Let's introduce something fairly generic called pseudocode. So we'll eventually get, in this course, to various programming languages. But today we'll do it in English-like syntax, where you just kind of say what you mean, but you're ever so succinct and you don't worry about grammar and complete sentences. You just express yourself as concisely as possible. >> So pseudocode is English-like syntax that represents a programming language. And toward that end, let me propose that we now model the process we just described of counting something a little differently, this time taking a look at this five-minute video produced by our friends at TED that defines what pseudocode is, defines what algorithmic thinking is, and even though the example you're about to see is, in of itself, super simple, it's going to start to give us the mental model, the vocabulary, with which to do much, much more complex algorithms quite quickly. >> [BEGIN VIDEO PLAYBACK] >> [MUSIC PLAYING] >> NARRATOR: What's an algorithm? In computer science, an algorithm is a set of instructions for solving some problem step by step. Typically, algorithms are executed by computers, but we humans have algorithms, as well. For instance, how would you go about counting the number of people in a room? Well, if you're like me, you'd probably point at each person, one at a time, and count up from 0. 1, 2, 3, 4, and so forth. >> Well, that's an algorithm. In fact, let's try to express it a bit more formally in pseudocode-- English-like syntax that resembles a programming language. Let N equal 0. For each person in room, set N equal to N plus 1. >> How to interpret this pseudocode? Well, line one declares, so to speak, a variable called N and initializes its value to 0. This just means that at the beginning of our algorithm, the thing with which we're counting has a value of 0. After all, before we start counting, we haven't counted anything yet. Calling this variable N is just a convention. I could have called it most anything. >> Now line two demarks the start of a loop, a sequence of steps that will repeat some number of times. So in our example, the step we're taking is counting people in the room. Beneath line two is line three, which describes exactly how we'll go about counting. The indentation implies that it's line three that will repeat. >> So what the pseudocode is saying is that after starting at 0, for each person in the room, we'll increase N by 1. Now is this algorithm correct? Well, let's bang on it a bit. Does it work if there are two people in the room? Let's see. >> In line one, we initialize N to 0. For each of these two people, we then increment N by 1. So on the first trip through the loop, we update N from 0 to 1. On the second trip through that same loop, we update N from 1 to 2. And so by this algorithm's end, n is 2, which indeed matches the number of people in the room. >> So far, so good. How about a corner case, though? Suppose there are 0 people in the room-- besides me, who's doing the counting. In line one, we initialize N to 0. This time, though, line three doesn't execute at all since there isn't a person in the room. And so N remains 0, which matches the number of people in the room. Pretty simple, right? >> But counting people one at a time is pretty inefficient, too, no? Surely we can do better. Why not count two people at a time? Instead of counting 1, 2, 3, 4, 5, 6, 7, 8, and so forth, why not count, 2, 4, 6, 8, and so on? It even sounds faster, and it surely is. >> Let's express this optimization in pseudocode. Let N equal 0. For each pair of people in room, set N equal to N plus 2. Pretty simple change, right? Rather than count people one at a time, we instead count them two at a time. This algorithm's thus twice as fast as the last. >> But is it correct? Let's see. Does it work if there are two people in the room? In line one, we initialize N to 0. For that one pair of people, we then increment N by two. And so by this algorithm's end, N is 2, which indeed matches the number of people in the room. >> Suppose next that there are 0 people in the room. In line one, we initialize N to 0. As before, line three doesn't execute at all, since there aren't any pairs of people in the room. And so N remains 0, which indeed matches the number of people in the room. >> But what if there are three people in the room? How does this algorithm fare? Let's see. In line one, we initialize N to 0. For a pair of those people, we then increment N by 2. But then what? There isn't another full pair of people in the room, so line two no longer applies. And so by this algorithm's end, N is still 2, which isn't correct. >> Indeed, this algorithm's said to be buggy, because it has a mistake. Lets redress with some new pseudocode. Let n equal 0 for each pair of people in room. Set N equal to N plus 2. If one person remains unpaired, set N equal to N plus 1. To solve this particular problem, we've introduced, in line four, a condition, otherwise known as a branch that only executes if there's one person that we could not pair with another. And so now, whether there's one or three or any odd number of people in the room, this algorithm will now count them. >> Can we do even better? Well, we could count in 3s or 4s or even 5s and 10s, but beyond that, it's going to get a little bit difficult to point. At the end of the day, whether executed by computers or humans, algorithms are just a set of instructions with which to solve problems. These were just three. What problem would you solve with an algorithm? >> [END VIDEO PLAYBACK] >> DAVID MALAN: That is the only time I will appear in cartoon form. But where that story leaves off, now, is how can we do better? Threes and fours, we claim, we can count people much faster, but can we do fundamentally better than that? And I wager we can. >> If we introduce a bit of our own pseudocode here, I'm going to propose that we can achieve a line like this. We're not going to count people one, two, three, four. We're not going to go two, four, six, eight. We're going to do fundamentally better by rethinking the problem, and in this case, leveraging an otherwise underutilized resource. >> In just a moment, I hope you'll forgive and humor us by standing up in place, at which point we're going to ask each of you to take on in your minds the number 1. You're then going to increasingly awkwardly, as time passes, find someone else who is standing, combine your numbers together by adding them up. One of you is then going to race to sit down first, and the other person is going to repeat. >> So in other words, by seeding all of you with the number 1, and then combining those 1s into 2s and those 2s into 4s, with everyone increasingly sitting down, we should, at the end of this algorithm, have just one loan soul who didn't sit down fast enough but who has the entire audiences count in his or her mind. >> So if you would, let's go ahead and-- step one-- stand up in place. And execute. >> [CROWD MURMURING] >> DAVID MALAN: Do you know where Lauren is? 729? >> [CROWD MURMURING] >> DAVID MALAN: All right? >> [CROWD MURMURING] >> DAVID MALAN: All right, we should be nearing the end. We see one fellow standing here still. Who else needs to be paired? If you guys want to pair off. Someone up top. Why don't I lend a hand here. For the very few people who are still standing, what numbers do you have in your mind? >> STUDENT: 78. >> DAVID MALAN: 78 plus-- who's standing down here? >> STUDENT: 39. >> DAVID MALAN: Plus 39. Plus who else is still standing? 81? OK, who else? Another 81? Wow. And then what's in back? >> STUDENT: 49. >> DAVID MALAN: 49, plus? >> STUDENT: 98. >> DAVID MALAN: 98 plus? Is that someone else? 12? Good job. >> [LAUGHTER] >> DAVID MALAN: Oh, 112-- oh. Good job! >> [LAUGHTER] >> [APPLAUSE] >> DAVID MALAN: Anyone else still standing? Sorry? >> STUDENT: 99. >> DAVID MALAN: 99. Anyone else still standing? And the total number of students here is actually, according to-- do you have a number? Oh, the actual number of people in the room, according to the account that the teaching fellows were doing on everyone's way in, was 729. So out of a roomful of Harvard students who counted themselves, the answer is 637. >> [LAUGHTER] >> DAVID MALAN: So close. But still. OK, so that's a teaching moment, right? This now is what we describe as a bug. Somewhere along the way, we did some arithmetic wrong, or someone sat down, or left, or something went wrong. But that's fine. Because even still, we got pretty close. And I'd argue that we got to the wrong answer a lot faster than I would have using my more linear approach. >> So let's assume we did in fact get that correct, but think now about what was happening each time, versus my own naive pointing algorithm. One, two, three. If there are indeed 729 or 637 people here, that would have taken me literally 637 or 729 pointings of the finger and incrementing my total count. And I could do a little better by going two, four, six, eight, and double that speed, maybe even triple or quadruple, depending how well I can do that counting in my head. >> But this approach that you guys took was fundamentally different. Because at the beginning, all of you stood up. So all 729. And then literally half of you sat down. And after that, another half of you sat down. And after that, another half of you sat down. >> And the total number of times that you guys could have sat down is roughly eight or nine or ten total times, depending on what our total count is. And we can sort of do this the other way. If we had 1,024 people in the room, the total number of times you could halve 1,024 people is 10. >> Now think about it in the other direction. Suppose, ridiculously, that we had, say four billion people in this room, or a slightly larger room. How many times would we have gone through this algorithm, such that half of that class sits down? It's only going to take 32 such operations, even in a class of size four billion. Why? Because four billion goes to two billion, goes to one million, goes to 500 million, goes to 250 million, dot, dot, dot. I can only do that division some 32 times, at which point, everyone except one person would be left standing. >> And that, too, is sort of a powerful idea that increasingly we'll try to leverage in this course, and in programming and computer science more generally, these germs of an idea with which we can then solve problems much, much more powerfully. So we started quite simple with that pseudocode and a guy in a room, but now with a whole room full of people have we done fundamentally better. >> Well, let's now transition from pseudocode to some actual code. This language you're about to see happen to be called JavaScript, and we'll return to this toward semester's end. It's a programming language that you use to make websites and other such software these days. And we have used it, thanks to a friend of ours at Stanford, to encode some hidden information here. This is the art of steganography, so to speak, where you can hide information in what otherwise appears to be noise or a completely different image altogether. But embedded in this particular image is indeed a secret message of sorts. >> So let me go ahead and pull up the same image here, this time in a web browser. And I'm going to wave my hand at some of the details for today, particularly for those of you who this looks like not only JavaScript but Greek, as a completely unfamiliar language. But this is an example of a programming language. >> And for now, take on faith that this first line of code-- and by code, I just mean text. Text that I could have literally typed into Microsoft Word, if I had the right software to then do something with it. Programming source code, programming code, is really just text, and it looks different based on what language you're using, not unlike English and Spanish and Russian all look different when you type them at your keyboard. >> So this first line, for now take on faith, simply opens a graphic from the internet, that noisy graphic we just saw. This next line here is an example of a loop, and we actually saw that same jargon in the TED video. A loop is something that happens again and again, and even though this absolutely looks cryptic, with the keyword for, and some parentheses, and some semicolons. We'll come back to that before long, but that loop there essentially is telling the program, iterate over all of those noisy dots, from left to right, top to bottom. >> Because at the end of the day, an image like this-- and you can actually kind of see it on this projector-- is really just a grid of dots. So we can identify each of those dots by a coordinate, x, y, and with this program, now can we begin to do something to those dots. >> So what I'm going to go ahead here and do is I'm going to make some changes. First I'm going to go ahead and get rid of all of that greenish and bluish noise, and I'm going to go ahead and type the following admittedly cryptic syntax. im for image. set blue at location x, comma, location y, to 0. In other words, I want to just turn off all of the blue dots in that picture. >> I'm going to go ahead now and click this Run/Save button, and you'll notice on the right-hand side, the resulting image appears. Now its super green, but that's not surprising, because I literally turned off, by making a 1 a 0, all of the blue in that picture. >> Well, now let's do it a bit more. im for image, dot setGreen, x, y. And that just means iterate from left to right and then top to bottom. Turn that off with a value of 0, as well. Save. And on the projector, you can't actually really see anything at all. >> On my laptop screen, if I peer in just the right way, I can see a bit of an image, because they're still some red in there. If you've ever heard the acronym RGB-- red, green, blue-- it's referring to this composition of an image using just those three colors. And right now, we've thrown away all green, all blue, but there's not much red. >> So let me crank up the red. How can I do that? Well, first, I'm going to ask this program a question. I'm going to go ahead and let's call it a variable, just like in algebra. You can have x or y or z. I'm going to declare a variable and say, put in this variable, temporarily, the value of the images getRed value at x, y. >> And again, we'll come back to all of this detail in the future. But for now, just take on faith that this line is asking the program, what is the red value at x, y? At that particular dot? >> Then I'm going to do something to it. Then I'm going to do image dot set red at x, y, y but this time I'm going to boost it by doing red times, let's say, 10. So increase it by a factor of 10. Let me zoom out now and click could Run/Save. And voila, that was there the entire time, even though our human eyes couldn't quite see it. >> So again, this now is real code, an example of a language that we'll come back to before long. But realize, particularly those of you with no such experience, it's quite soon that we ourselves will be writing code like that there. In fact, a tool with which you're all somewhat familiar, perhaps, is CS50's own course-shopping tool, which was actually rebooted this summer by some of CS50's own former students, now turn TFs. >> So this happens to be a website built in a language called PHP. It uses a database called MySQL, things with which we'll get our hands dirty later in the semester. But believe it or not, even something like this ultimately reduces to the simplest of loops and conditions and branches, like those we saw just a moment ago in the TED video. >> What I thought I'd do now is share not just something we the staff have made for the campus, but rather something a former student-- three students, in fact-- made this past year, Sierra, Daniel, and Sam, the last of whom had no prior programing experience when he took CS50. And for their final project, they exhibited, at the CS50 Fair, an application called wrdly, which is a web-based program for which they made this video that I thought I'd share to give you a sense of just what is possible by term's end. >> [MUSIC PLAYING] >> DAVID MALAN: That's from Week Zero to Week 12 this past year. >> [APPLAUSE] >> DAVID MALAN: As a teaser, too, really to whet your appetite is to what's possible, you may have seen already, or may soon see,, a new tool that the course's team has been working on, this time in collaboration with Harvard Student Agencies, such that starting this year and continuing hopefully into this coming summer you'll have a standard opportunity on campus to buy and sell things of interest to you. And with partnership through HSA, you'll also be able to drop items off in one of HSA's physical stores at some point in the future, so as to proxy things, particularly as you graduate and don't necessarily want to discard things, but actually pay it forward to folks who might follow you here on campus. So more on that to come. >> But a little more concretely, a tool that's come out of CS50 in recent years, with which some of you might be familiar and others of you might be googling now, at, you'll find a link to a Chrome extension which is demonstrative of how you can use JavaScript, that same language we used with the Eiffel tower a moment ago, to implement 2x playback speed for all Harvard iSites videos. This is something that's built into CS50's own video player. But this, too, if you begin to dig into the source code, which we'll happily make available, you'll see how you can even solve problems like that, accelerating widgets in websites with which you're already well familiar. >> So a word now on the course and expectations and what lies ahead. In general, we'll indeed gather here on Mondays and Wednesdays-- though this Friday, we'll gather because of Shopping Week-- 1:00 to 2:00 PM, though sometimes until 2:30. Given that you might therefore want or have to take some class at 2:00 PM onward, or even before, do realize the course is supportive of what's called simultaneous enrollment, whereby we'll support a petition to the Ad Board and your resident deans on your behalf if you have a conflict somewhere in this 1:00 to 2:30 range. Head to that URL online for additional details. >> But in terms of the support structure that characterizes CS50, for students more and less comfortable alike, we offer distinct tracks of sections. And this is a couple of weeks off, but before long, you'll be asked as to your comfort level. Are you among those less comfortable, more comfortable, or somewhere in between? >> And we'll have three distinct tracks that cater to precisely those audiences. So at no point in the term should you even feel like you're competing against any student with more or less background than you. Indeed, the course is meant to be much more collaborative and much more open than that. >> In terms of the problem sets, you'll find, too, that in addition to the standard edition of each week's problem set, there's often a "hacker edition" that's meant to be targeted at the 5% to 10% or so of the demographic who's indeed among those more comfortable and would like more of a challenge than the standard edition of that pset expects. More details on those to be found in the syllabus. >> But also in there can be found details on the courses late days. Typically problem sets are due on Thursdays. However, you can extend many of your deadlines this fall from Thursdays to Fridays simply by meeting us halfway, so to speak, answering a few warm-up questions in some of the week's problem sets, that will automatically then give you an extra 24 hours. We will also drop your lowest score, as per the syllabus. >> To give you a sense of what the problem sets are-- because it's indeed the course's problem sets that ultimately define almost every student's experience, more so than lectures, more so than sections, more so than most any other aspect of the course. Last year, for instance, we began, as we'll begin this year, with Scratch. Particularly this Friday, we'll use, for just one day's time, a graphical programming language, with which we'll start programming by dragging and dropping puzzle pieces that only assemble physically if it makes sense to do so logically. >> Next week, we'll quickly transition to C, a fairly old but very small and simple language that will allow us to really go from 0 to 60 over the course of just a few weeks, and then parlay those same skills and knowledge of basic programming constructs into higher-level languages like PHP, JavaScript, and yet others still. >> Last year, the third pset in the course was that of cryptography, a domain-specific application whereby we challenged students to implement any number of ciphers, programs with which to scramble or unscramble information, to encrypt it. For the hacker edition, by contrast, we gave the hacker students a file from a standard Unix computer containing user names and passwords, the latter of which were encrypted, and we challenged those hacker students to decrypt, as best they could, those passwords, still on that same domain. >> Scramble, a game with which some of you are perhaps familiar. A forensics piece, where we ask students to recover data that had been otherwise deleted from my own digital camera's compact flash card, by actually writing software to figure out, where were the zeroes and ones in that digital camera that previously composed a JPEG graphic? >> A challenge of sorts last year involving writing the fastest spell-checker possible, competing against friends and classmates if they'd like. Implementing Huff 'n Puff, a compression program. And then ending the semester with CS50 Finance, a web-based application with which you create an eTrade-like website to buy and sell stocks, so to speak, by actually pulling nearly real-time quotes Yahoo! Finance. >> What we didn't do last year was one problem set that remains nonetheless a favorite. If you've never gone to, you'll see a user interface a little like this. But two years ago, the class implemented, using Google Maps and the Google Earth plug-in and a little bit of savvy with driving around campus, so that the objective of this game was, as you can see some of the faces, is to drive around campus looking for staff, teaching fellows and CAs, and when you do, putting them onto your shuttle bus. None of them actually seem to be here, so we're going to enter a cheat code. >> [LAUGHTER] >> DAVID MALAN: There we go. All right. And here now is the staff laced throughout campus. And as you can see, on the right-hand side of the screen, the shuttle bus has empty seats. And the objective was to write the code with which to simulate this driving and picking up and dropping off of passengers. That one, too, using a language called JavaScript. So realize that programs like that will be on our same trajectory this year, as well. >> In terms, now, of additional support, we have office hours. As you might have seen in your own house dining hall or in Annenberg, we'll be in the house dining halls four nights a week-- Leverett, Pfoho, Eliot and Annenberg this year, 8:00 PM to 11:00 PM. And what we thought we'd do this year is something a little different. >> If you heard rumblings last year that it was a bit too stressful, this year's office hours, as we'll describe next week, will be more organic, whereby upon arrival, you'll be dispatched to one particular table where multiple staff members await, and we'll do things much more organically. No more queue, no more iPad, but rather have more intimate conversations around a table of just eight or so students, so that we approximate the feel of what otherwise would be a much smaller class. >> We offer, as well, these things we called walkthroughs, videos filmed in advance by one of the course's teaching fellows, Zamyla, in which she walks you through the week's problem sets, offering tips and tricks for the challenges that lay ahead. And conversely, after problem sets are due, this year, we'll also release little clips call post-mortems that actually walk you through representative solutions, both good and bad, via which you can infer how you could have or should have implemented your own solution. >> And what we'll offer for the first time this year as well, particularly for those students who avail themselves of the course's other resources but nonetheless are struggling all too much, the course itself will pair those students, as resources permit, with tutors so that you have a much more intimate opportunity than house dining halls allow for one-on-one assistance. >> Now a final glimpse at some of the end games in sight. You might be familiar with the CS50 Hackathon. Well, coming this December, from 8:00 PM to 7:00 AM, at the beginning of Reading Period, will be an opportunity to gather with classmates-- this would be around 9:00 PM-- during which you dive into your final project's implementation alongside classmates, friends, and food. This would be around 1:00 AM, when the first batch of food arrived. And this is about 4:00 AM that particular year at the CS50 Hackathon. >> But the true climax of the course is meant to the CS50 Fair, a campus-wide exhibition of your own final projects, to which family and friends are all invited, as our recruiters and our friends from industry. This, for instance, is a glimpse of the 2,000-plus people who've attended past years. Expressions like this are not uncommon, and similarly do your classmates delight in things you've accomplished. >> And actually, toward that end, we have a start-of-term event, as well. If things like this appeal to you, or you're at least curious as to what this, know that a new tradition of the course is called CS50 Puzzle Day. And this was instituted a couple of years back to really signal to campus that computer science is not about programming, and it's certainly not about embracing only those students who have prior experience. It's really about problem-solving more generally. >> And so Puzzle Day, over the past few years now, has evolved into a nice partnership with our friends at Facebook, whereby there'll be fabulous prizes and pizza across the river at the i-lab this coming Saturday. Head to that URL with two or three friends if you would like to partake in this new tradition. >> So I'd like to ask that you keep one thing in mind, and we've got just a two minute clip on which to close today. 73% is the number to remember. Cake, too, will await you outside this transept as we adjourn in just a couple of moments, which is a tradition of the course, as well. But this is the key quote from the course's syllabus to keep in mind. What ultimately matters in this course is not so much where you end up relative to your classmates but where you, in Week 12, end up relative to yourself in Week 0. >> But the glimpse that we will leave you with here today is this last one here by our same Daniel, who did the wrdly video just a moment ago. I leave you with this glimpse of what lies ahead. And as we do this, if we could have CS50 staff from the front of the room to come on up to the stage to paint all the more of a visual picture as to what awaits you this year-- getting awkward. We'll conclude with this here on the screen. >> [MUSIC PLAYING] >> DAVID MALAN: This is CS50. >> [MUSIC - MATT & KIM, "IT'S ALRIGHT"] >> SPEAKER 1: I love CS50 more than cats. >> SPEAKER 2: Whoaaaa! >> [LAUGHTER] >> DAVID MALAN: This, then, is CS50. We will see you on Friday. >> [APPLAUSE AND CHEERING] >> NARRATOR: At the next CS50, an onstage demo doesn't go as planned. >> DAVID MALAN: We want to find Mike Smith in this phone book. Well, what are your instincts? I might jump roughly to the middle of the phone book, glance down, see that I'm at M, and I know now that Mike Smith isn't to the left. He must be to the right. And so at this point, we can literally tear-- at this point, we can literally tear-- at this point, we can figuratively tear the phone book in half. >> [UKELELE STRUMMING]



A DOI is a type of Handle System handle, which takes the form of a character string divided into two parts, a prefix and a suffix, separated by a slash. The prefix identifies the registrant of the identifier, and the suffix is chosen by the registrant and identifies the specific object associated with that DOI. Most legal Unicode characters are allowed in these strings, which are interpreted in a case-insensitive manner. The prefix usually takes the form 10.NNNN, where NNNN is a series of at least 4 numbers greater than or equal to 1000, whose limit depends only on the total number of registrants.[11][12] The prefix may be further subdivided with periods, like 10.NNNN.N.[13]

For example, in the DOI name 10.1000/182, the prefix is 10.1000 and the suffix is 182. The "10." part of the prefix distinguishes the handle as part of the DOI namespace, as opposed to some other Handle System namespace,[A] and the characters 1000 in the prefix identify the registrant; in this case the registrant is the International DOI Foundation itself. 182 is the suffix, or item ID, identifying a single object (in this case, the latest version of the DOI Handbook).

DOI names can identify creative works (such as texts, images, audio or video items, and software) in both electronic and physical forms, performances, and abstract works[14] such as licenses, parties to a transaction, etc.

The names can refer to objects at varying levels of detail: thus DOI names can identify a journal, an individual issue of a journal, an individual article in the journal, or a single table in that article. The choice of level of detail is left to the assigner, but in the DOI system it must be declared as part of the metadata that is associated with a DOI name, using a data dictionary based on the indecs Content Model.


The official DOI Handbook explicitly states that DOIs should display on screens and in print in the format "doi:10.1000/182".[15] Contrary to the DOI Handbook, CrossRef, a major DOI registration agency, recommends displaying a URL (for example, instead of the officially specified format (for example, doi:10.1000/182)[16][17] This URL provides the location of an HTTP proxy server which will redirect web accesses to the correct online location of the linked item.[8][18] This recommendation is primarily based on the assumption that the DOI is being displayed without being hyper-linked to its appropriate URL – the argument being that without the hyperlink it is not as easy to copy-and-paste the full URL to actually bring up the page for the DOI, thus the entire URL should be displayed, allowing people viewing the page containing the DOI to copy-and-paste the URL, by hand, into a new window/tab in their browser in order to go to the appropriate page for the document the DOI represents.


Major applications of the DOI system currently include:

  • scholarly materials (journal articles, books, ebooks,etc.) through CrossRef, a consortium of around 3,000 publishers; Airiti, a leading provider of electronic academic journals in Chinese and Taiwanese; and the Japan Link Center (JaLC) an organization providing link management and DOI assignment for electronic academic journals in Japanese.
  • research datasets through DataCite, a consortium of leading research libraries, technical information providers, and scientific data centers;
  • European Union official publications through the EU publications office;
  • the Chinese National Knowledge Infrastructure project at Tsinghua University and the Institute of Scientific and Technical Information of China (ISTIC), two initiatives sponsored by the Chinese government.
  • Permanent global identifiers for commercial video content through the Entertainment ID Registry, commonly known as EIDR.

In the Organisation for Economic Co-operation and Development's publication service OECD iLibrary, each table or graph in an OECD publication is shown with a DOI name that leads to an Excel file of data underlying the tables and graphs. Further development of such services is planned.[19]

A multilingual European DOI registration agency activity, mEDRA, and a Chinese registration agency, Wanfang Data, are active in non-English language markets.

Features and benefits

The IDF designed the DOI system to provide a form of persistent identification, in which each DOI name permanently and unambiguously identifies the object to which it is associated. It also associates metadata with objects, allowing it to provide users with relevant pieces of information about the objects and their relationships. Included as part of this metadata are network actions that allow DOI names to be resolved to web locations where the objects they describe can be found. To achieve its goals, the DOI system combines the Handle System and the indecs Content Model with a social infrastructure.

The Handle System ensures that the DOI name for an object is not based on any changeable attributes of the object such as its physical location or ownership, that the attributes of the object are encoded in its metadata rather than in its DOI name, and that no two objects are assigned the same DOI name. Because DOI names are short character strings, they are human-readable, may be copied and pasted as text, and fit into the URI specification. The DOI name-resolution mechanism acts behind the scenes, so that users communicate with it in the same way as with any other web service; it is built on open architectures, incorporates trust mechanisms, and is engineered to operate reliably and flexibly so that it can be adapted to changing demands and new applications of the DOI system.[20] DOI name-resolution may be used with OpenURL to select the most appropriate among multiple locations for a given object, according to the location of the user making the request.[21] However, despite this ability, the DOI system has drawn criticism from librarians for directing users to non-free copies of documents that would have been available for no additional fee from alternative locations.[22]

The indecs Content Model as used within the DOI system associates metadata with objects. A small kernel of common metadata is shared by all DOI names and can be optionally extended with other relevant data, which may be public or restricted. Registrants may update the metadata for their DOI names at any time, such as when publication information changes or when an object moves to a different URL.

The International DOI Foundation (IDF) oversees the integration of these technologies and operation of the system through a technical and social infrastructure. The social infrastructure of a federation of independent registration agencies offering DOI services was modelled on existing successful federated deployments of identifiers such as GS1 and ISBN.

Comparison with other identifier schemes

A DOI name differs from commonly used Internet pointers to material, such as the Uniform Resource Locator (URL), in that it identifies an object itself as a first-class entity, rather than the specific place where the object is located at a certain time. It implements the Uniform Resource Identifier (Uniform Resource Name) concept and adds to it a data model and social infrastructure.[23]

A DOI name also differs from standard identifier registries such as the ISBN, ISRC, etc. The purpose of an identifier registry is to manage a given collection of identifiers, whereas the primary purpose of the DOI system is to make a collection of identifiers actionable and interoperable, where that collection can include identifiers from many other controlled collections.[24]

The DOI system offers persistent, semantically-interoperable resolution to related current data and is best suited to material that will be used in services outside the direct control of the issuing assigner (e.g., public citation or managing content of value). It uses a managed registry (providing social and technical infrastructure). It does not assume any specific business model for the provision of identifiers or services and enables other existing services to link to it in defined ways. Several approaches for making identifiers persistent have been proposed. The comparison of persistent identifier approaches is difficult because they are not all doing the same thing. Imprecisely referring to a set of schemes as "identifiers" doesn't mean that they can be compared easily. Other "identifier systems" may be enabling technologies with low barriers to entry, providing an easy to use labeling mechanism that allows anyone to set up a new instance (examples include Persistent Uniform Resource Locator (PURL), URLs, Globally Unique Identifiers (GUIDs), etc.), but may lack some of the functionality of a registry-controlled scheme and will usually lack accompanying metadata in a controlled scheme. The DOI system does not have this approach and should not be compared directly to such identifier schemes. Various applications using such enabling technologies with added features have been devised that meet some of the features offered by the DOI system for specific sectors (e.g., ARK).

A DOI name does not depend on the object's location and, in this way, is similar to a Uniform Resource Name (URN) or PURL but differs from an ordinary URL. URLs are often used as substitute identifiers for documents on the Internet (better characterised as Uniform Resource Identifiers) although the same document at two different locations has two URLs. By contrast, persistent identifiers such as DOI names identify objects as first class entities: two instances of the same object would have the same DOI name.


DOI name resolution is provided through the Handle System, developed by Corporation for National Research Initiatives, and is freely available to any user encountering a DOI name. Resolution redirects the user from a DOI name to one or more pieces of typed data: URLs representing instances of the object, services such as e-mail, or one or more items of metadata. To the Handle System, a DOI name is a handle, and so has a set of values assigned to it and may be thought of as a record that consists of a group of fields. Each handle value must have a data type specified in its <type> field, which defines the syntax and semantics of its data. While a DOI persistently and uniquely identifies the object to which it is assigned, DOI resolution may not be persistent, due to technical and administrative issues.

To resolve a DOI name, it may be input to a DOI resolver, such as

Another approach, which avoids typing or cutting-and-pasting into a resolver is to include the DOI in a document as a URL which uses the resolver as an HTTP proxy, such as (preferred)[25] or, both of which support HTTPS. For example, the DOI 10.1000/182 can be included in a reference or hyperlink as This approach allows users to click on the DOI as a normal hyperlink. Indeed, as previously mentioned, this is how CrossRef recommends that DOIs always be represented (preferring HTTPS over HTTP), so that if they are cut-and-pasted into other documents, emails, etc., they will be actionable.

Other DOI resolvers and HTTP Proxies include,, At the beginning of the year 2016, a new class of alternative DOI resolvers was started by This service is unusual in that it tries to find a non-paywalled version of a title and redirects you to that instead of the publisher's version.[26][27] Since then, other open-access favoring DOI resolvers have been created, notably in October 2016.[28] While traditional DOI resolvers solely rely on the Handle System, alternative DOI resolvers first consult open access resources such as BASE (Bielefeld Academic Search Engine).[26][28]

An alternative to HTTP proxies is to use one of a number of add-ons and plug-ins for browsers, thereby avoiding the conversion of the DOIs to URLs,[29] which depend on domain names and may be subject to change, while still allowing the DOI to be treated as a normal hyperlink. For example. the CNRI Handle Extension for Firefox, enables the browser to access Handle System handles or DOIs like hdl:4263537/4000 or doi:10.1000/1 directly in the Firefox browser, using the native Handle System protocol. This plug-in can also replace references to web-to-handle proxy servers with native resolution. A disadvantage of this approach for publishers is that, at least at present, most users will be encountering the DOIs in a browser, mail reader, or other software which does not have one of these plug-ins installed.

IDF organizational structure

The International DOI Foundation (IDF), a non-profit organisation created in 1998, is the governance body of the DOI system.[30] It safeguards all intellectual property rights relating to the DOI system, manages common operational features, and supports the development and promotion of the DOI system. The IDF ensures that any improvements made to the DOI system (including creation, maintenance, registration, resolution and policymaking of DOI names) are available to any DOI registrant. It also prevents third parties from imposing additional licensing requirements beyond those of the IDF on users of the DOI system.

The IDF is controlled by a Board elected by the members of the Foundation, with an appointed Managing Agent who is responsible for co-ordinating and planning its activities. Membership is open to all organizations with an interest in electronic publishing and related enabling technologies. The IDF holds annual open meetings on the topics of DOI and related issues.

Registration agencies, appointed by the IDF, provide services to DOI registrants: they allocate DOI prefixes, register DOI names, and provide the necessary infrastructure to allow registrants to declare and maintain metadata and state data. Registration agencies are also expected to actively promote the widespread adoption of the DOI system, to cooperate with the IDF in the development of the DOI system as a whole, and to provide services on behalf of their specific user community. A list of current RAs is maintained by the International DOI Foundation. The IDF is recognized as one of the federated registrars for the Handle System by the DONA Foundation (of which the IDF is a board member), and is responsible for assigning Handle System prefixes under the top-level 10 prefix.[31]

Registration agencies generally charge a fee to assign a new DOI name; parts of these fees are used to support the IDF. The DOI system overall, through the IDF, operates on a not-for-profit cost recovery basis.


The DOI system is an international standard developed by the International Organization for Standardization in its technical committee on identification and description, TC46/SC9.[32] The Draft International Standard ISO/DIS 26324, Information and documentation – Digital Object Identifier System met the ISO requirements for approval. The relevant ISO Working Group later submitted an edited version to ISO for distribution as an FDIS (Final Draft International Standard) ballot,[33] which was approved by 100% of those voting in a ballot closing on 15 November 2010.[34] The final standard was published on 23 April 2012.[1]

DOI is a registered URI under the info URI scheme specified by IETF RFC 4452. info:doi/ is the infoURI Namespace of Digital Object Identifiers.[35]

The DOI syntax is a NISO standard, first standardised in 2000, ANSI/NISO Z39.84-2005 Syntax for the Digital Object Identifier.[36]

The maintainers of the DOI system have deliberately not registered a DOI namespace for URNs, stating that:

URN architecture assumes a DNS-based Resolution Discovery Service (RDS) to find the service appropriate to the given URN scheme. However no such widely deployed RDS schemes currently exist.... DOI is not registered as a URN namespace, despite fulfilling all the functional requirements, since URN registration appears to offer no advantage to the DOI System. It requires an additional layer of administration for defining DOI as a URN namespace (the string urn:doi:10.1000/1 rather than the simpler doi:10.1000/1) and an additional step of unnecessary redirection to access the resolution service, already achieved through either http proxy or native resolution. If RDS mechanisms supporting URN specifications become widely available, DOI will be registered as a URN.

— International DOI Foundation, Factsheet: DOI System and Internet Identifier Specifications[37]

See also


  1. ^ Other registries are identified by other strings at the start of the prefix. Handle names that begin with "100." are also in use, as for example in the following citation: Hammond, Joseph L., Jr.; Brown, James E.; Liu, Shyan-Shiang S. (May 1975). "Development of a Transmission Error Model and an Error Control Model l". Technical Report RADC-TR-75-138. Rome Air Development Center. Bibcode:1975STIN...7615344H. hdl:100.2/ADA013939. 


  1. ^ a b "ISO 26324:2012(en), Information and documentation — Digital object identifier system". ISO. Retrieved 2016-04-20. 
  2. ^ "The Handle System". 
  3. ^ "Factsheets". 
  4. ^ Witten, Ian H.; David Bainbridge & David M. Nichols (2010). How to Build a Digital Library (2nd ed.). Amsterdam; Boston: Morgan Kaufmann. pp. 352–253. ISBN 978-0-12-374857-7. 
  5. ^ Langston, Marc; Tyler, James (2004). "Linking to journal articles in an online teaching environment: The persistent link, DOI, and OpenURL". The Internet and Higher Education. 7 (1): 51–58. doi:10.1016/j.iheduc.2003.11.004. 
  6. ^ "How the 'Digital Object Identifier' works". BusinessWeek. BusinessWeek. 23 July 2001. Retrieved 20 April 2010. Assuming the publishers do their job of maintaining the databases, these centralized references, unlike current Web links, should never become outdated or broken. 
  7. ^ Paskin, Norman (2010), "Digital Object Identifier (DOI®) System", Encyclopedia of Library and Information Sciences (3rd ed.), Taylor and Francis, pp. 1586–1592 
  8. ^ a b Davidson, Lloyd A.; Douglas, Kimberly (December 1998). "Digital Object Identifiers: Promise and problems for scholarly publishing". Journal of Electronic Publishing. 4 (2). doi:10.3998/3336451.0004.203. 
  9. ^ "Welcome to the DOI System". 28 June 2010. Retrieved 7 August 2010. 
  10. ^ "DOI® News, April 2011: 1. DOI System exceeds 50 million assigned identifiers". 20 April 2011. Retrieved 3 July 2011. 
  11. ^ "doi info & guidelines". Publishers International Linking Association, Inc. 2013. Retrieved 10 June 2016. All DOI prefixes begin with "10" to distinguish the DOI from other implementations of the Handle System followed by a four-digit number or string (the prefix can be longer if necessary). 
  12. ^ "Factsheet—Key Facts on Digital Object Identifier System". International DOI Foundation. June 6, 2016. Retrieved 10 June 2016. Over 18,000 DOI name prefixes within the DOI System 
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