From September 3 to 5, I will be attending STI 2014, the 19th International Conference on Science and Technology Indicators. There, I will present a paper entitled “Altmetrics-based Visualizations Depicting the Evolution of a Knowledge Domain” that I co-authored with Philipp Weißensteiner and Peter Brusilovsky (download the PDF here). In this work-in-progress paper, we present an approach to visualizing the topical evolution of a scientific conference over time.

Below you can see the results: a topical overview of the 19th and 20th iteration of UMAP, representing the conference years of 2011 and 2012, as well as the evolution of the domain.An interactive prototype can be found on http://stellar.know-center.tugraz.at/umap/.

Data Source and Method

co-bookmarking

Relationship between two documents established by co-bookmarking

The data source for these visualization is quite unique: it is the conference scheduling system Conference Navigator 3 (CN3). CN3 allows conference attendees to create a personal schedule by bookmarking talks from the program that they intend to follow. And it is exactly this scheduling data that we have employed to create the above visualizations: we used co-bookmarking as a measure of subject similarity, meaning that two documents are related when they are bookmarked by the same user in the system (see example to the right). The more often two documents are bookmarked together, the more similar they are subject-wise.

On top of this co-bookmarking data, we performed the knowledge domain visualization process from the open source visualization Head Start to create individual representations of the field (please refer to the paper for details). This resulted in the first two visualizations pictured earlier on. The blue bubbles represent research areas. The size of an area is determined by number of bookmarks that the papers related to this area have received. Spatial closeness implies topical similarities. In 2011, “User modeling” is the area with most papers and most bookmarks. It is closely connected to several other larger areas, including “Recommender system”. A second cluster of areas can be found on the right hand side of the visualization, involving “Intelligent tutoring system”, “Adaptive system”, and “Problem solving”.

Overview of UMAP 2011

Overview of UMAP 2011

Timeline Visualization

The next question was how to visualize the evolution of the conference. As far as time series visualization goes, there are many types of visualizations, most prominently index charts and stacked graphs. In the case of knowledge domain visualizations, simple visualizations are unfortunately not able to convey all necessary dimensions of the data (in terms of ordination, size of research areas and closeness). One possibility would have been to use animation, as shown in the video below with Hans Rosling.



In the end, we did not choose to use animation. Why? The reason for that is a psychological phenomenon called change blindness (Simons and Rensink, 2005). It means that people are bad at recognizing change in an object or scence. In the next video, the phenomenon is explained and illustrated with an astonishing example.



Animation seems to be especially prone to change blindness; in the video below by Suchow and Alvarez (2011), the colored dots making up the ring are constantly changing. This changing of color seems to stop when the circle itself starts to move – except that it does not. If you concentrate on individual dots, you can see that they keep changing color.

Surely, this is an extreme example, but think about it: if Hans Rosling were not to be talking you through the video above, would you have recognized all the changes taking place and would you have been able to interpret them correctly? If you concentrate on one country specifically, could you remember the movement of the other countries as well? Chances are, you would have to watch the animation many times to come up with the same interpretation as Prof. Rosling.

All of these considerations led us to choose a different visualization concept popularized by Edward Tufte: small multiples. In small multiples, a graph is drawn for each of the steps in a time series. Then the graphs are positioned next to each other. This approach thus allows for direct visual comparison between different representations.

Evolution of UMAP

Evolution of UMAP

To aid the user in detecting changes between the representations, we introduced two visual helpers. First, a grid is drawn to help with comparing size and position of the research areas. Second, whenever users hover over an area, the corresponding area is highlighted in the other representation, and a line is drawn between the two entities. There are three areas that are present in both years: “User modelling”, “Recommender system” and “Intelligent tutoring system”. While the relative position of the areas to each other has not changed much, the area with the most papers and bookmarks is now “Recommender system”.

Future Work

As you can see from the examples above, this is just a first prototype, albeit a promising one. Using small multiples allows for a comparison of knowledge domain visualizations over various years.

Nevertheless, there are certain weaknesses in the current approach: first, the topology of the visualizations is not ideal, as many areas may overlap each other. Second, the usefulness of the method depends on the usage of the system by conference participants. Therefore, we are looking into supplementing bookmarking data with content-based measures when there was lower participation. Third, the continuity between the two years is very low. This could be improved by introducing moving time windows of two years. Finally, it will be important to evaluate the method and the interface.

Any comments on the issues mentioned above and the paper in general are of course very welcome!

ResearchBlogging.orgKraker, P., Weißensteiner, P., & Brusilovsky, P. (2014). Altmetrics-based Visualizations Depicting the Evolution of a Knowledge Domain 19th International Conference on Science and Technology Indicators (STI 2014), 330-333

Note: This is a reblog from the OKFN Science Blog. As part of my duties as a Panton Fellow, I will be regularly blogging there about my activities concerning open data and open science.

barometer2

by Leo Reynolds

Altmetrics, web-based metrics for measuring research output, have recently received a lot of attention. Started only in 2010, altmetrics have become a phenomenon both in the scientific community and in the publishing world. This year alone, EBSCO acquired PLUM Analytics, Springer included Altmetric info into SpringerLink, and Scopus augmented articles with Mendeley readership statistics.

Altmetrics have a lot of potential. They are usually earlier available than citation-based metrics, allowing for an early evaluation of articles. With altmetrics, it also becomes possible to assess the many outcomes of research besides just the paper – meaning data, source code, presentations, blog posts etc.

One of the problems with the recent hype surrounding altmetrics, however, is that it leads some people to believe that altmetrics are somehow intrinsically better than citation-based metrics. They are, of course, not. In fact, if we just replace the impact factor with the some aggregate of altmetrics then we have gained nothing. Let me explain why.

The problem with metrics for evaluation

You might know this famous quote:

“All models are wrong, but some are useful” (George Box)

It refers to the fact that all models are a simplified view of the world. In order to be able to generalize phenomena, we must leave out some of the details. Thus, we can never explain a phenomenon in full with a model, but we might be able to explain the main characteristics of many phenomena that fall in the same category. The models that can do that are the useful ones.

Example of a scientific model, explaining atmospheric composition based on chemical process and transport processes.  Source: Strategic Plan for the U.S. Climate Change Science Program (Image by  Phillipe Rekacewicz)

Example of a scientific model, explaining atmospheric composition based on chemical process and transport processes. Source: Strategic Plan for the U.S. Climate Change Science Program (Image by Phillipe Rekacewicz)

The very same can be said about metrics – with the grave addition that metrics have a lot less explanatory power than a model. Metrics might tell you something about the world in a quantified way, but for the how and why we need models and theories. Matters become even worse when we are talking about metrics that are generated in the social world rather than the physical world. Humans are notoriously unreliable and it is hard to pinpoint the motives behind their actions. A paper may be cited for example to confirm or refute a result, or simply to acknowledge it. A paper may be tweeted to showcase good or to condemn bad research.

In addtion, all of these measures are susceptible to gaming. According to ImpactStory, an article with just 54 Mendeley readers is already in the 94-99 percentile (thanks to Juan Gorraiz for the example). Getting your paper in the top ranks is therefore easy. And even indicators like downloads or views that go into the hundreds of thousands can probably be easily gamed with a simple script deployed on a couple of university servers around the country. This makes the old citation cartel look pretty labor-intensive, doesn’t it?

Why we still need metrics and how we can better utilize them

Don’t get me wrong: I do not think that we can come by without metrics. Science is still growing exponentially, and therefore we cannot rely on qualitative evaluation alone. There are just too many papers published, too many applications for tenure track positions submitted and too many journals and conferences launched each day. In order to address the concerns raised above, however, we need to get away from a single number determining the worth of an article, a publication, or a researcher.

One way to do this would be a more sophisticated evaluation system that is based on many different metrics, and that gives context to these metrics. This would require that we work towards getting a better understanding of how and why measures are generated and how they relate to each other. In analogy to the models, we have to find those numbers that give us a good picture of the many facets of a paper – the useful ones.

As I have argued before, visualization would be a good way to represent the different dimensions of a paper and its context. Furthermore, the way the metrics are generated must be open and transparent to make gaming of the system more difficult, and to expose the biases that are inherent in humanly created data. Last, and probably most crucial, we, the researchers and the research evaluators must critically review the metrics that are served to us.

Altmetrics do not only give us new tools for evaluation, their introduction also presents us with the opportunity to revisit academic evaluation as such – let’s seize this opportunity!

Note: This is a reblog from the OKFN Science Blog. As part of my duties as a Panton Fellow, I will be regularly blogging there about my activities concerning open data and open science.

In July last year, I released the first version of a knowledge domain visualization called Head Start. Head Start is intended for scholars who want to get an overview of a research field. They could be young PhDs getting into a new field, or established scholars who venture into a neighboring field. The idea is that you can see the main areas and papers in a field at a glance without having to do weeks of searching and reading.

 

Interface of Head Start

You can find an application for the field of educational technology on Mendeley Labs. Papers are grouped by research area, and you can zoom into each area to see the individual papers’ metadata and a preview (or the full text in case of open access publications). The closer two areas are, the more related they are subject-wise. The prototye is based on readership data from the online reference management system Mendeley. The idea is that the more often two papers are read together, the closer they are subject-wise. More information on this approach can be found in my dissertation (see chapter 5), or if you like it a bit shorter, in this paper and in this paper.

Head Start is a web application built with D3.js. The first version worked very well in terms of user interaction, but it was a nightmare to extend and maintain. Luckily, Philipp Weißensteiner, a student at Graz University of Technology became interested in the project. Philipp worked on the visualization as part of his bachelor’s thesis at the Know-Center. Not only did he modularize the source code, he also introduced Javascript Finite State Machine that lets you easily describe different states of the visualization. To setup a new instance of Head Start is now only a matter of a couple of lines. Philipp developed a cool proof of concept for his approach: a visualization that shows the evolution of a research field over time using small multiples. You can find his excellent bachelor’s thesis in the repository (German).

 

Head Start Timeline View

In addition, I cleaned up the pre-processing scripts that do all the clustering, ordination and naming. The only thing that you need to get started is a list of publications and their metadata as well as a file containing similarity values between papers. Originally, the similarity values were based on readership co-occurrence, but there are many other measures that you can use (e.g. the number of keywords or tags that two papers have in common).

So without further ado, here is the link to the Github repository. Any questions or comments, please send them to me or leave a comment below.

 

Note: This is a reblog from the OKFN Science Blog. As part of my duties as a Panton Fellow, I will be regularly blogging there about my activities concerning open data and open science.

6795008004_8046829553

by AG Cann

Altmetrics are a hot topic in scientific community right now. Classic citation-based indicators such as the impact factor are amended by alternative metrics generated from online platforms. Usage statistics (downloads, readership) are often employed, but links, likes and shares on the web and in social media are considered as well. The altmetrics promise, as laid out in the excellent manifesto, is that they assess impact quicker and on a broader scale.

The main focus of altmetrics at the moment is evaluation of scientific output. Examples are the article-level metrics in PLOS journals, and the Altmetric donut. ImpactStory has a slightly different focus, as it aims to evaluate the oeuvre of an author rather than an individual paper.

This is all good and well, but in my opinion, altmetrics have a huge potential for discovery that goes beyond rankings of top papers and researchers. A potential that is largely untapped so far.

How so? To answer this question, it is helpful to shed a little light on the history of citation indices.

Pathways through science

In 1955, Eugene Garfield created the Science Citation Index (SCI) which later went on to become the Web of Knowledge. His initial idea – next to measuring impact – was to record citations in a large index to create pathways through science. Thus one can link papers that are not linked by shared keywords. It makes a lot of sense: you can talk about the same thing using totally different terminology, especially when you are not in the same field. Furthermore, terminology has proven to be very fluent even in the same domain (Leydesdorff 1997). In 1973, Small and Marshakova realized – independently from each other – that co-citation is a measure of subject similarity and therefore can be used to map a scientific field.

Due to the fact that citations are considerably delayed, however, co-citation maps are often a look into the past and not a timely overview of a scientific field.

Altmetrics for discovery

In come altmetrics. Similarly to citations, they can create pathways through science. After all, a citation is nothing else but a link to another paper. With altmetrics, it is not so much which papers are often referenced together, but rather which papers are often accessed, read, or linked together. The main advantage of altmetrics, as with impact, is that they are much earlier available.

clickstream_map

Bollen et al. (2009): Clickstream Data Yields High-Resolution Maps of Science. PLOS One. DOI: 10.1371/journal.pone.0004803.

One of the efforts in this direction is the work of Bollen et al. (2009) on click-streams. Using the sequences of clicks to different journals, they create a map of science (see above).

In my PhD, I looked at the potential of readership statistics for knowledge domain visualizations. It turns out that co-readership is a good indicator for subject similarity. This allowed me to visualize the field of educational technology based on Mendeley readership data (see below). You can find the web visualization called Head Start here and the code here (username: anonymous, leave password blank).

Why we need open and transparent altmetrics

The evaluation of Head Start showed that the overview is indeed more timely than maps based on citations. It, however, also provided further evidence that altmetrics are prone to sample biases. In the visualization of educational technology, the computer science driven areas such as adaptive hypermedia are largely missing. Bollen and Van de Sompel (2008) reported the same problem when they compared rankings based on usage data to rankings based on the impact factor.

It is therefore important that altmetrics are transparent and reproducible, and that the underlying data is openly available. This is the only way to ensure that all possible biases can be understood.

As part of my Panton Fellowship, I will try to find datasets that satisfy these criteria. There are several examples of open bibliometric data, such as the Mendeley API, and figshare API that have adopted CC BY, but most of the usage data is not available publicly or cannot be redistributed. In my fellowship, I want to evaluate the goodness of fit of different open altmetrics data. Furthermore, I plan to create more knowledge domain visualizations such as the one above.

So if you know any good datasets please leave a comment below. Of course any other comments on the idea are much appreciated as well.

Note: This is a reblog from the OKFN Science Blog. As part of my duties as a Panton Fellow, I will be regularly blogging there about my activities concerning open data and open science.

Peer review is one of the oldest and most respected instruments of quality control in science and research. Peer review means that a paper is evaluated by a number of experts on the topic of the article (the peers). The criteria may vary, but most of the time they include methodological and technical soundness, scientific relevance, and presentation.

“Peer-reviewed” is a widely accepted sign of quality of a scientific paper. Peer review has its problems, but you won’t find many researchers that favour a non peer-reviewed paper over a peer-reviewed one. As a result, if you want your paper to be scientifically acknowledged, you most likely have to submit it to a peer-reviewed journal.

Even though it will take more time and effort to get it published than in a non peer-reviewed publication outlet.

Peer review helps to weed out bad science and pseudo-science, but it also has serious limitations. One of these limitations is that the primary data and other supplementary material such as documentation source code are usually not available. The results of the paper are thus not reproducible. When I review such a paper, I usually have to trust the authors on a number of issues: that they have described the process of achieving the results as accurate as possible, that they have not left out any crucial pre-processing steps and so on. When I suspect a certain bias in a survey for example, I can only note that in the review, but I cannot test for that bias in the data myself. When the results of an experiment seem to be too good to be true, I cannot inspect the data pre-processing to see if the authors left out any important steps.

As a result, later efforts in reproducing research results can lead to devastating outcomes. Wang et al. (2010) for example found that they could not reproduce almost all of the literature on a certain topic in computer science.

“Reproducible”: a new quality criterion

Needless to say this is not a very desirable state. Therefore, I argue that we should start promoting a new quality criterion: “reproducible”. Reproducible means that the results achieved in the paper can be reproduced by anyone because all of the necessary supplementary resources have been openly provided along with the paper.

It is easy to see why a peer-reviewed and reproducible paper is of higher quality than just a peer-reviewed one. You do not have to take the researchers’ word of how they calculated their results – you can reconstruct them yourself. As a welcome side-effect, this would make more datasets and source code openly available. Thus, we could start building on each others’ work and aggregate data from different sources to gain new insights.

In my opinion, reproducible papers could be published alongside non-reproducible papers, just like peer-reviewed articles are usually published alongside editorials, letters, and other non peer-reviewed content. I would think, however, that over time, reproducible would become the overall quality standard of choice – just like peer-reviewed is the preferred standard right now. To help this process, journals and conferences could designate a certain share of their space to reproducible papers. I would imagine that they would not have to do that for too long though. Researchers will aim for a higher quality standard, even if it takes more time and effort.

I do not claim that reproducibility solves all of the problems that we see in science and research right now. For example, it will still be possible to manipulate the data to a certain degree. I do, however, believe that reproducibility as an additional quality criterion would be an important step for open and reproducible science and research.

So that you can say to your colleague one day: “Let’s go with the method described in this paper. It’s not only peer-reviewed, it’s reproducible!”

Note: This is a reblog from the OKFN Science Blog. To my excitment and delight, I was recently awarded a Panton Fellowship. As part of my duties, I will be regularly blogging there about my activities concerning open data and open science.

Peter Kraker at Barcamp Graz 2012. Photo by Rene Kaiser

Photo by Rene Kaiser

Hi, my name is Peter Kraker and I am one of the new Panton Fellows. After an exciting week at OKCon, I was asked to introduce myself and what I want to achieve during my fellowship, which I am very happy to do. I am a research assistant at Know-Center of Graz University of Technology and a late-stage PhD student at University of Graz. Like many others, I believe that an open approach is essential for science and research to making progress. Open science to me is about reproducibility and comparability of scientific output. Research data should therefore be put into the public domain, as called for in the Panton Principles.

In my PhD, I am concerning myself with research practices on the web and how academic literature search can be improved with overview visualizations. I have developed and open-sourced a knowledge domain visualization called Head Start. Head Start is based on altmetrics data rather than citation data. Altmetrics are indicators of scholarly activity and impact on the web. Have a look at the altmetrics manifesto for a thorough introduction.

In my evaluation of Head Start, I noticed that altmetrics are prone to sample biases. It is therefore important that analyses based on altmetrics are transparent and reproducible, and that the underlying data is openly available. Contributing to open and transparent altmetrics will be my first objective as a Panton Fellow. I will establish an altmetrics data repository for the upcoming open access journal European Information Science. This will allow the information science community to analyse the field based on this data, and add an additional data source for the growing altmetrics community. My vision is that in the long run, altmetrics will not only help us to evaluate science, but also to connect researchers around the world.

My second objective as a Panton Fellow is to promote open science based on an inclusive approach. The case of the Bermuda Rules, which state that DNA sequences should be rapidly released into the public domain, has shown that open practices can be established, if the community stands together. In my opinion, it is therefore necessary to get as many researchers aboard as possible. From a community perspective, it is the commitment to openness that matters, and the willingness to promote this openness. The inclusive approach puts the researcher in his or her many roles at the center of attention. This approach is not intended to replace existing initiatives but to make researchers aware of these initiatives and helping them with choosing their approach to open science. You can find more on that in on my blog.

Locally, I will be working with the Austrian Chapter of the Open Knowledge Foundation to promote open science based on this inclusive approach. Together with the Austrian Student’s Union, we will be having workshops with students, faculty, and librarians. I will also make the case for open science in the research communities that I am involved in. For the International Journal on Technology Enhanced Learning for example, I will develop an open data policy.

I am very honored to be selected as a Panton Fellow, and I am excited to get started. If you want to work with me on one or the other objective, please do not hesitate to contact me. You can also follow my work on Twitter and on my blog. Looking forward to furthering the cause of open data and open science with you!

Open Science Logo v2

by gemmerich

Update: There is a OKFN pad devoted to discussing this idea. Please add your comments and critique there!

When Derick Leony, Wolfgang Reinhardt, Günter Beham and myself made the case for an open science in technology-enhanced learning back in late 2011, we discussed how open science could become a reality. We finally concluded that this was first and foremost a matter of consensus in the community:

Open Science is first and foremost a community effort. In fact we are arguing that reproducibility and comparability should become two of the standard criteria that every reviewer has to judge when assessing a paper.  [..] These two criteria should be of equal importance as the established criteria, giving incentive to the authors to actually apply the instruments of Open Science.

In addition, journals and conferences ought to make the submission of source code, data, and methodological descriptions together with the paper mandatory for them to be published. Conferences and journals themselves should in turn commit to making the papers openly accessible. The case of the genetic sequence database GenBank, which stores DNA sequences and makes them available to the public, has shown that if publishers and conference organisers adopt new standards, they can be propagated quickly within the community. The huge success of GenBank is due to the fact that many journals adopted the Bermuda principles (Marshall 2001), which state among other things that DNA sequences should be rapidly released into the public domain.

There is a crucial interplay at work between individual researchers and other actors within a field such as funding agencies, journals, and conferences. On the one hand, individual researchers are often bound by the rules that are made by those institutions because they depend on them as sources of funding and as publication outlets. On the other hand, the boards and committees steering these institutions are (at least partly) made up of the same researchers. Many researchers are sitting on conference committees, editorial boards, and policy advisory boards. They are thus shaping the community and commonly defining what is shared pratice among its participants. In their role, they can advocate open practices and propose rules that help establishing an open science.

In my perception, the discourse in open science often runs along the lines of open vs. closed approaches. A lot of effort is put into determining what is truly open and what is actually still closed. In open access for example, there is a heated debate whether to choose the green or the gold road with advocates on both sides ferociously arguing why only one of the two can only be considered as true open access. Whereas this discussion surely has some merit, most researchers have to worry more about whether their efforts are recognized by the community than what constitutes true openness. As Antonella Esposito writes in her insightful study on digital research practices:

Nonetheless their digital identities and online activities constituted a ‘parallel’ academic life that developed as a self–legitimating approach within a traditional mode of knowledge production and distribution. These tentative efforts were not acknowledged in their respective communities, struggling to become identifiable open research practices. Indeed, some interviewees called for clear institutional rules enabling sharing practices — especially in teaching and learning — that might slowly produce a general change of attitude and overcome current isolated initiatives by a few pioneers of open scholarship.

Most researchers are neither completely open nor completely closed. There is no black and white, but different shades of grey. Nonetheless, there are many researchers out there who make their publications available or put their source code online. In my opinion, it is necessary to get these reseachers aboard, not to drive them away with endless debates whether their research is “truly” open. Don’t get me wrong: it is important to have discussions about the optimal characteristics of open science, but not at the expense of making open science an elitist club where only a small minority can enter that satisfies all criteria. From a community perspective, it is the commitment to openness that matters, and the willingness to promote this openness on editorial boards and program committees.

It seems that such a holistic view is gaining some traction: in a recent Web Science paper, R. Fyson, J. Simon and L. Carr discuss the interplay between actors regarding open access publications. Another good example of an inclusive approach is the Open Science Project here in Graz. The Open Science Project is a group of students led by Stefan Kasberger that tries to do all of their study-related work according to open science practices. This means that they try to use open source software for their homework assignments and make the results publicly available. They go to great lengths in their effort as they also try to persuade lecturers to follow their example and make their scripts openly accessible.

Draft Petition

At a recent meeting of the Austrian chapter of the OKFN Open Science, we started discussing an inclusive  approach to open science. This motivated me to write a first draft for a petition which you can find below. So my question is: would you sign such a petition? Do you think it is engaging/far going/well worded enough? Let me know what you think in the comments or join us at the OKFN Pad where you can help us to collaboratively edit the text:

Science is one of the greatest endeavours of mankind. It has enjoyed  enormous growth since its inception more than 400 years ago. Science has  not only produced an incredible amount of knowledge, it has also created  tools for communication and quality control. Journals, conferences, peer  review to name just a few. Lately, serious shortcomings of these  established instruments have surfaced. Scientific results are often irreproducible and lead to ill-guided decisions. Retraction rates are on  the rise. There have been many cases of high profile scientific fraud.

In our view, all of these problems can be addressed by a more open approach to science. We see Open Science as making the scientific  process and all of its outcomes openly accessible to the general public. Open Science would benefit science, because it would make results more  reproducible, and quality control more transparent. Open Science would also benefit the society by including more people in the process and sparking open innovation.

Besides the greater good, open science also benefits individual scientists. Research has shown that papers that are openly accessible are cited more  often. If you share source code and data, you could get credited for  these parts of your research as well. If you talk about your methodology and share it with others, this will bring attention to your work. The internet provides us with the technology to make Open Science possible. In our view, it is time to embrace these possibilities and innovate in the scientific process.

It is very important to note that we see Open Science as a community effort that can only work if we include as many people as possible. We know that it is not possible to open up entire work processes  overnight. In our view, this is not necessary to contribute to an Open Science. The idea is to open everything up that you already can and work towards establishing open practices in your work and your  community. You might already have papers that you are allowed to share in a personal and institutional repository. You might have source code or data that you can easily publish under a permissive license. And you might be sitting on a board and committee where you can bring open practices into the discussion.

If you agree with this point of view, you are encouraged to sign the  declaration below.

  • I will open up resources that I have the legal right to
  • I will work towards establishing open practices in my research
  • I will promote Open Science in my institution and my research community

If you would like to comment on the manifesto, or add your own ideas, please go to this OKFN Pad.

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