Note: This is a reblog from the OKFN Science Blog.

It’s hard to believe that it has been over a year since Peter Murray-Rust announced the new Panton fellows at OKCon 2013. I am immensly proud that I was one of the 2013/14 Panton Fellows and the first non UK-based fellow. In this post, I will recap my activities during the last year and give an outlook of things to come after the end of the fellowship. At the end of the post, you can find all outputs of my fellowship at a glance. My fellowship had two focal points: the work on open and transparent altmetrics and the promotion of open science in Austria and beyond.

Open and transparent altmetrics

On stage at the Open Science Panel Vienna (Photo by FWF/APA-Fotoservice/Thomas Preiss)

On stage at the Open Science Panel Vienna (Photo by FWF/APA-Fotoservice/Thomas Preiss)

The blog post entitled “All metrics are wrong, but some are useful” sums up my views on (alt)metrics: I argue that no single number can determine the worth of an article, a journal, or a researcher. Instead, we have to find those numbers that give us a good picture of the many facets of these entities and put them into context. Openness and transparency are two necessary properties of such an (alt)metrics system, as this is the only sustainable way to uncover inherent biases and to detect attempts of gaming. In my comment to the NISO whitepaper on altmetrics standards, I therefore maintained that openness and transparency should be strongly considered for altmetrics standards.

In another post on “Open and transparent altmetrics for discovery”, I laid out that altmetrics have a largely untapped potential for visualizaton and discovery that goes beyond rankings of top papers and researchers. In order to help uncover this potential, I released the open source visualization Head Start that I developed as part of my PhD project. Head Start gives scholars an overview of a research field based on relational information derived from altmetrics. In two blog posts, “New version of open source visualization Head Start released” and “What’s new in Head Start?” I chronicled the development of a server component, the introdcution of the timeline visualization created by Philipp Weißensteiner, and the integration of Head Start with Conference Navigator 3, a nifty conference scheduling system. With Chris Kittel and Fabian Dablander, I took first steps towards automatic visualizations of PLOS papers. Recently, Head Start also became part of the Open Knowledge Labs. In order to make the maps created with Head Start openly available to all, I will set up a server and website for the project in the months to come. The ultimate goal would be to have an environment where everybody can create their own maps based on open knowledge and share them with the world. If you are interested in contributing to the project, please get in touch with me, or have a look at the open feature requests.

Evolution of the UMAP conference visualized in Head Start. More information in  Kraker, 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.

Evolution of the UMAP conference visualized in Head Start. More information in Kraker, 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.

Promotion of open science and open data

Regarding the promotion of open science, I teamed up with Stefan Kasberger and Chris Kittel of openscienceasap.org and the Austrian chapter of Open Knowledge for a series of events that were intended to generate more awareness in the local community. In October 2013, I was a panelist at the openscienceASAP kick-off event at University of Graz entitled “The Changing Face of Science: Is Open Science the Future?”. In December, I helped organizing an OKFN Open Science Meetup in Vienna on altmetrics. I also gave an introductory talk on this occasion that got more than 1000 views on Slideshare. In February 2014, I was interviewed for the openscienceASAP podcast on my Panton Fellowship and the need for an inclusive approach to open science.

In June, Panton Fellowship mentors Peter Murray-Rust and Michelle Brook visited Vienna. The three-day visit, made possible by the Austrian Science Fund (FWF), kicked off with a lecture by Peter and Michelle at the FWF. On the next day, the two lead a well-attended workshop on content mining at the Institute of Science and Technology Austria.The visit ended with a hackday organized by openscienceASAP, and an OKFN-AT meetup on content mining. Finally, last month, I gave a talk on open data at the “Open Science Panel” on board of the MS Wissenschaft in Vienna.

I also became active in the Open Access Network Austria (OANA) of the Austrian Science Fund. Specifically, I am contributing to the working group “Involvment of researchers in open access”. There, I am responsible for a visibility concept for open access researchers. Throughout the year, I have also contributed to a monthly sum-up of open science activities in order to make these activities more visible within the local community. You can find the sum-ups (only available in German) on the openscienceASAP stream.

I also went to a lot of events outside Austria where I argued for more openness and transparency in science: OKCon 2013 in Geneva, SpotOn 2013 in London, and Science Online Together 2014 in Raleigh (NC). At the Open Knowledge Festival in Berlin, I was session facilitator for “Open Data and the Panton Principles for the Humanities. How do we go about that?”. The goal of this session is to devise a set of clear principles which describe what we mean by Open Data in the humanities, what these should contain and how to use them. In my role as an advocate for reproducibility I wrote a blog post on why reproducibility should become a quality criterion in science. The post sparked a lot of discussion, and was widely linked and tweeted.

by Martin Clavey

by Martin Clavey

What’s next?

The Panton Fellowship was a unique opportunity for me to work on open science, to visit open knowledge events around the world, and to meet many new people who are passionate about the topic. Naturally, the end of the fellowship does not mark the end of my involvement with the open science community. In my new role as a scientific project developer for Science 2.0 and open science at Know-Center, I will continue to advocate openness and transparency. As part of my research on altmetrics-driven discovery, I will also pursue my open source work on the Head Start framework. With regards to outreach work, I am currently busy drafting a visibility concept for open access researchers in the Open Access Network Austria (OANA). Furthermore, I am involved in efforts to establish a German-speaking open science group

I had a great year, and I would like to thank everyone who got involved. Special thanks go to Peter Murray-Rust and Michelle Brook for administering the program and for their continued support. As always, if you are interested in helping out with one or the other project, please get in touch with me. If you have comments or questions, please leave them in the comments field below.

All outputs at a glance

Head Start – open source research overview visualization
Blog Posts
Audio and Video
Slides
Reports
Open Science Sum-Ups (contributions) [German]

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!

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