This year, there will be some small updates regarding how Open Access VIS works.
It’s great that you have a website or a github repositry, but link rot has been a serious problem on OAVIS with about 5% of PDFs disappearing each year. Reliable archives keep papers in a freely accessible, persistent, immutable, and uniquely identifiable way. Archives that meet these criteria include: Continue reading
Last week, with great disappointment, I resigned from the position of open practice co-chair for the IEEE VIS conference. I delayed this blog post by several days to avoid an uncourteous surprise public announcement. Here is my open letter: Continue reading
I recently gave a talk on Open Practices in Visualization Research at the workshop on Methodological Approaches for Visualization (BELIV). Unfortunately, with only a 10 minute talk, I had to leave out many important details, which has resulted in some confusion.
A few people have brought up concerns that repositories for open data and materials do not have long term viability. “What happens if the site shuts down in 5 years?” As an alternative, people have proposed storing data and materials in a pay-walled IEEE repository. While it’s good to hear that open access is being discussed, being informed is important for the discussion to be fruitful. So I’ll highlight some critical information about the Open Science Framework (OSF).
The Center for Open Science (COS) has a fund devoted specifically to preserving and maintaining the repository in case the organization ever shuts down. This fund would make a read-only form of the repository accessible for 50+ years. Here is a quote from the sustainability supplement in the COS’s strategic plan (page 24):
In the event of COS’s closing, the preservation fund guarantees long-term hosting and preservation of all the data and content stored on the OSF (50+ years based on present costs and use)
Content posted to OSF can choose from a variety of open licenses. Any future work that builds upon the content, incorporates it into a meta-analysis, or scrutinizes it can freely access and link to the material. Openness facilitates research without needing to rely on an expensive subscription to the publisher. Furthermore, an open license means that future work will not require the original author give permission or even reply to emails.
On the other hand, some people want the content to be stored in IEEE’s digital library. That is exactly the opposite of open science. It would be behind a pay-wall (that’s not open). Also, IEEE would own the copyright of the data and material. Either IEEE or an obnoxious original author in fear of scrutiny could obstruct any attempt to publish work that reuses the content on licensing grounds (that’s not science).
The openness of OSF allows people to copy their content elsewhere in the future. So there is little risk of being “stuck” with OSF if you don’t like it. If someone creates a better site, they could even mirror OSF’s content, so future open science systems could start with all of the information already on OSF.
Like in version control, most open science repositories allow for updating content such that previous versions are always accessible. That approach allows for further updates such as added documentation or fixing typos without erasing the peer-reviewed version. In contrast, making a change to the IEEE digital library is a nightmare.
There have been some attempts by individuals and organizations such as ACM to “reinvent the wheel” by creating their own policies for open practice requirements and badges. These attempts often fail to consider flexibility and transparency in reporting.
Alternatively, the Transparency and Openness Promotion (TOP) guidelines have pre-written templates for modular policies that with various levels of strictness (from simply reporting whether it is available to mandatory submission) and for various artifacts (materials, collected data, analysis code, etc.). A table (artifact x sternness) summarizing the different policies is available on the last page here.
One final note: I’m not especially attached to OSF. There are alternatives such as zenodo and figshare, but OSF has the most full-featured set of services and has the most well-thought-out policies.
Open data allows people to independently check a paper’s analysis or perform an altogether new analysis. It’s also a way of allowing future work to perform meta-analyses and ask questions that may not have been asked in the original paper. Therefor, it’s important to make experiment data public, provide it completely, and make it accessible for it to be useful to others.
But many missteps can happen that reduce the value of open data. These tips should help ensure that your data is indeed open, useful, and accessible.
Sharing experiment data and materials is a key component of open science and is becoming increasingly common (Kidwell et al. 2016). But some in Visualization and HCI have expressed concern that this practice may not be compatible with anonymous submissions. Not true! Open data and open materials can easily be shared anonymously.
Continue reading
Previous years: 2013, 2014, 2015, 2016
The goal of this guide is to highlight vis papers that demonstrate evidence of a user performance benefit. I used two criteria:
I did not discriminate beyond those two criteria. However, I am using a gold star ★ to highlight one property that only a few papers have: a generalizable explanation for why the results occurred. You can read more about explanatory hypotheses here.
Previous years: 2013, 2014, 2015
The goal of this guide is to highlight vis papers that demonstrate evidence of a user performance benefit. I used two criteria:
I did not discriminate beyond those two criteria. However, I am using a gold star ★ to highlight one property that only a few papers have: a generalizable explanation for why the results occurred. You can read more about explanatory hypotheses here.
The goal of this guide is to highlight vis papers that demonstrate evidence of a user performance benefit. I used two criteria:
I did not discriminate beyond those two criteria. However, I am using a gold star ★ to highlight one property that only a few papers have: a generalizable explanation for why the results occurred. You can read more about explanatory hypotheses here.
As we all know, the most important part of publishing research is making sure that no one ever reads or cites it. After all, it’d be awful if anyone actually saw the end result of months or even years of your effort. So keep these tips in mind the next time you author a publication.
Continue reading
Imagine someone explaining a complex topic, like how to improve the fuel efficiency of a boat. But shortly after starting the explanation, they go off on a series of tangents about pretty boats they’ve seen, big boats, rubber ducks, submarines, and other transportation vehicles such as the new 787 by Boeing et al. Then they return to the explanation of boat efficiency without ever referencing why they brought up those strange tangents.
Tangents are confusing, and they hurt clarity. The related work section is often just a string of unrelated tangents, which is a waste of the reader’s time.
Now let me make something clear: I am not necessarily saying that papers should cite fewer sources. Instead, each citation should serve an obvious, specific purpose. And if that purpose is so tangential to the structure of your argument that you need to put it in what amounts to a citation dumping ground, then it isn’t needed.