I gave a talk on e-lab notebooks (ELNs) at UW-Madison yesterday. I cover the reasons for making (or not making) the switch to an ELN, what to look for in an ELN, and some things that Madison has done in this area. If you are unfamiliar with e-lab notebooks, this talk should provide you with a nice background in the technology.
In addition to the slides below, you can also watch a video of the talk here.
There is a saying about storage in the library world: lots of copies keep stuff safe. The abbreviation, LOCKSS, not only defines this principle but also provides the name of two storage systems, LOCKSS and CLOCKSS, which libraries buy into to add redundancy to their data storage. The idea behind the principle is that even if your local storage system fails, you still have access to your data.
LOCKSS is a great concept, but for everyday storage I boil it down to the ‘Rule of 3’. This rule of thumb says that you should keep 3 copies of your data, 2 onsite copies and 1 offsite copy. This is not only a good level of redundancy, but also a very achievable level of redundancy.
The third offsite copy is actually critical to the success of the Rule of 3. Many people keep their data and a backup copy on-site, but this doesn’t factor in scenarios where the building floods or burns down or a natural disaster occurs. One only has to look at universities recovering after hurricane Katrina or the Japan tsunami to see how devastating a natural disaster can be to research (among other things). Storing a copy of your data off-site can make the recovery process a bit easier if everything local is lost.
While the Rule of 3 speaks mainly to redundancy, I also see it as a recommendation for variety; mainly, that each copy should be on a different type of hardware. Usually, the first copy is on your computer, so options for the other copies include external hard drives, cloud storage, local server, CDs/DVDs, tape backup, etc. Each of these technologies has its own strengths and weaknesses, so you spread out your risk by not relying on one storage type.
For example, if you keep your data backed up off-site on commercial cloud storage, keeping an extra copy on a hard drive on-site means that the safety of your data is not based solely on the success of a business. Alternatively, tape backup is high quality but slow to recover from, but it’s a great option for the ‘if all else fails’ backup copy. The exact configuration of your backups will depend on the technology options available to you, but variety should be a factor when you choose your systems.
I personally love the Rule of 3 and follow it for my work information. For my data, I keep:
The shared drive is the weak link in this chain, as I transfer files manually, but setting a weekly reminder in my calendar makes sure that I stay on top of things. Additionally, I would not use the office shared drive if I had security or privacy concerns with my data. Besides keeping my data in these 3 locations, I have practiced retrieving information from both backups so I know that they are working and how to restore my information if disaster strikes.
In the end, the Rule of 3 is simply an interpretation of the old expression, ‘don’t put all of your eggs in one basket.’ This applies not only to the number of copies of your data but also the technology upon which they are stored. With a little bit of planning, it is very easy to ensure that your data are backed up in way that dramatically reduces the risk of total loss.
This week is Open Access week, a celebration that promotes and raises awareness for the growing Open Access movement. There are a lot of great reasons to publish open access, including making research openly available and shifting away from an unsustainable journal pricing model, but I want to focus my celebration of Open Access week on Open Data.
Open Access and Open Data are very different but they share common values: accessibility, transparency, ease of information reuse, a return on investment for public funding, and advancing research. While Open Access publishing has taken off in the last few years, especially with the success of open journals like PLOS ONE and faculty-led mandates like the one from Harvard, the efforts to open up our research data are still developing. For this reason, I think it’s important to take a moment during Open Access week to talk about Open Data
What is Open Data?
Open Data is the idea that research data should be made available upon the publication of a paper and as part of peer review. Data sheds light onto the research process in a way that can’t be done with an article alone. With stories of fraud and irreproducible research increasingly in the news, we need methods like Open Data for detecting these issues earlier.
Another reason for Open Data is that the value of data is increasing in the current funding climate. Between more access to data and new tools for analysis and mining, we are able to conduct research that simply wasn’t possible before. With shrinking research budgets, data are valuable research products that we can no longer afford to ignore.
Why should I make my data open?
A good reason for Open Data comes from a recent study in PeerJ that found a 9% average increase in citation rates for papers that had open datasets as compared to papers without shared data. The citation increase was upwards of 30% for the older papers sampled, suggesting that this citation effect increases over time.
Opening up research data also benefits us by being able to work with data that we did not have access to before. Not having to produce all of the data ourselves is great thing, but that data has to come from somewhere. We must be willing to provide useful data to others if we want access to useful data for ourselves.
What can I do about Open Data?
The first step is simply to understand why there is movement toward Open Data, even if you personally choose not to share data. The way we conduct research is changing and we need to know how to navigate those changes in order to be successful. Open Data is not going to universally happen overnight, but the ever increasing momentum in this direction means we need to stay informed of the why’s and where’s.
For those a little more comfortable with the idea of Open Data, consider sharing an old dataset or a negative/unpublishable study. This is a great way to get credit for information that you are not actively using and it will familiarize you with the data sharing systems. From there, you can share more datasets as you choose or as requested by funders/journals/readers.
As a librarian, I’m also spending this week letting people know about Open Data. This blog post is one of the ways I’m doing that but I have also hung up a poster in my library:
In keeping with the open data theme, the files are openly available (both PDF and Adobe Illustrator files) for you to use and remix. One person has already used the files to make a poster for their library and I would love to see more versions!
Happy Open Access week!
I’m surprised that I haven’t discussed this on the blog yet, but there is a pretty fundamental question that needs to be addressed in order to discuss data management: what does “data” even mean?
Coming from a scientific background, it’s easy to imagine large tables of numbers as data or files filled with the repetitive A, T, G, and C’s of genetic code, but data regularly defies these stereotypes (particularly in non-science disciplines). Data can be videos, large collections of text, images, tweets, geospatial information, etc. What can be used as data is only limited by the research question and the creativity of the researcher.
Though research data can be a lot of things, it is still useful to define the term so we know what information needs management. So here is my working definition of data: anything that you can perform analysis upon. It’s a wide definition, but there are so many types of research out there that anything narrower won’t apply. Despite the broad definition it is still possible to break the diversity of data into four general types.
What Data Are
Data is often categorized into the four following groups: observational data, experimental data, simulation data, and derived/compiled data. Not only are the data in each group different, but the way that you should manage each type differs. Let’s go through each group now.
Observational data are tied to a time and place and are a record of something that occurred there. This type of data includes everything from bird counts, to polling data, to weather sensor data, to recordings of dance performances. The proper management of this data is critical because this information is not reproducible.
Experimental data are created under a particular set of conditions that are (hopefully) reproducible. This type of data covers everything from gene sequences, to chromatography data, to measurements from the Large Hadron Collider, to psychology studies. Good data management is important here too because, depending on the experiment, it can be very expensive to reproduce data. Experimental data also requires good documentation so that, should the need arise, the data can be accurately reproduced.
Simulation data are created using models and code. This type of data covers everything from climate models, to economic models, to simulations of experiments/experimental data. In this group, it is more important to preserve the code that created the data than the data themselves, as the data can be recreated from the code.
Finally, derived/compiled data are compilations of other datasets that can be used for new types of analysis. This type of data covers databases, large corpora used for text mining, collections of images, etc. Standard data management applies, but you’re also more likely to run up against data size concerns and licensing/copyright issues with this data type than the others.
What Data Aren’t
I defined what data are but I think it’s also important to talk about what data aren’t. The OMB Circular A-110 contains a nice round-up of what the government does not consider to be research data.
Research data is defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings, but not any of the following: preliminary analyses, drafts of scientific papers, plans for future research, peer reviews, or communications with colleagues. This “recorded” material excludes physical objects (e.g., laboratory samples). Research data also do not include:
(A) Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and
(B) Personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.
That doesn’t mean that these things aren’t important to manage and preserve, just that funders don’t consider them to be data for the purposes of sharing and other policies. I will also point out that while lab notebooks are not technically data nor do they need to be shared, they contain important information that gives context to data and should therefore be preserved alongside any data that they describe. The ultimate point is that research is built on multiple information sources, each with its own information management need, but not all of these sources fall under the umbrella of “data”.
Final Thoughts
It’s important to recognize that the term “data” is more broadly applicable than you may think. Something you would not consider to be data can be the critical foundation for research in another field. But that doesn’t mean that the term “data” applies to all research materials.
The broad definition of data goes hand in hand with the realization that not all data should be managed in the same way. Understanding the diversity and nuances of data allows us to make good management decisions to better preserve data and make research more reproducible.
I wrote about the best licenses for datasets in my previous post and I want to add to that information by pointing out two potentially problematic Creative Commons licenses for research products, data and publications alike: CC Attribution-NonCommercial (CC BY-NC) and CC Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)*. These are the two Creative Commons noncommercial licenses.
There are a couple reasons to think twice before using this class of licenses. The first is that the meaning of noncommercial is unclear. You are excluding anyone from freely using CC-NC content for profit, which obviously covers corporations but might also include groups like nonprofits. For example, a nonprofit may use your content for promotional material intended to increase their membership. An increase in member dues can be considered a financial gain that is not allowed under a CC-NC license. There are a lot of ambiguities here and they are better laid out in this article on noncommercial licensing in biology. The important thing is to be aware that you are excluding more uses than you may realize using a noncommercial license.
The other reason to hesitate before applying a noncommercial license is that publishers can still make profit on this content even though it is “open access”. Licensing something under a CC-NC licence doesn’t mean that it can’t be used commercially, only that it can’t be used commercially for free. This content can still be used in a commercial setting if you pay for permission, just like with traditional content. At least one publisher is guiding its authors toward this “open access” license while simultaneously charging others to commercially use this content; the profit rarely goes back to the author.
Noncommercial licenses are not recommended for data, for reasons expressed in my previous post, but are probably not ideal for your other research products either. I’m not saying that you shouldn’t use a noncommercial license, only that you should be aware of the limitations of these licenses before consciously applying them to your research products.
* “Share Alike” licenses require any derivative products to be similarly licensed. This is a “copyleft” style license, meant to make the content and all its derivatives free in perpetuity.
As scientists, we aren’t necessarily trained in copyright. For a long time this hasn’t been a problem, as practices for distributing our scholarly work have been fairly standardized. Open access publishing and data sharing are changing things and providing researchers with a multitude of copyright options beyond just signing over our rights in order to be published. This post looks at some of those options for data.
Data and Copyright
Copyright is confusing, but it becomes even weirder when you apply it to research data. That’s because data are often considered facts, which do not fall under copyright in many countries. Unless you create a creative compilation of those facts, a situation in which copyright then applies in countries like the US. Such variations in copyright law from country to country make it difficult to determine if you need to worry about copyright on your research data.
In the US, the distinction between facts and a creative compilation of facts was laid out in the case “Feist Publications, Inc. v. Rural Telephone Service Company, Inc.” This case applied to the compilation of telephone numbers (ie. a phone book), which was not deemed a creative arrangement. There must be some original selection and rejection in the compilation in order to justify copyright. So a curated database containing research data could be considered a creative arrangement, even though individual facts are not eligible for copyright.
The heterogeneous nature of data adds to the copyright confusion. It’s unclear if original research data that aren’t a stereotypical set of numbers (like image data, video data, etc.) are eligible for copyright, though my inclination is that in some situations they might be.
Confused yet?
Let’s take a step back from this muddle and talk about the two things I think you should know about copyright on datasets (caveat: I am not a copyright expert, so this does not constitute legal advice).
I recommend using a Creative Commons license because these licenses are easy to apply, legally enforceable, and becoming popular in scholarly publishing. Creative Commons (CC) itself is a nonprofit organization founded in 2001. They took the idea of the GNU GPL license for open source software and applied it to creative works. They offer several licenses, but I want to look at the two most often discussed for datasets.
Creative Commons Attribution (CC BY)
The Creative Commons Attribution license is the most basic of the CC licenses and the one that is often used on open access articles. If you license something under CC BY, you allow anyone to use and modify your content for any purpose, so long as you are given attribution. Because of the freedom to mine content, CC BY is often considered the best license for open access journal articles and for that reason is required by some funding agencies.
On the surface, CC BY seems like a great license for data because it enables data reuse while still requiring citation, which is always important in research. The problem with this license appears when you aggregate datasets. For example, if you are analyzing a group of 100 datasets to find patterns, under a CC-BY license you would need to cite every last dataset in your published article. If you have a particularly large database, citation becomes even more difficult because you need to sort through which parts of the database were actually included in the analysis. Using CC BY datasets in aggregate is obviously problematic.
The limitations of CC BY licensed data are becoming more apparent as data mining emerges as an important research tool. Ironically, data mining is one of the reasons to want openly licensed data in the first place. So in order to enable easier data mining and reuse, Creative Commons does not recommend the use of CC BY for data.
Creative Commons Zero (CC0)
The Creative Commons Zero license is the only Creative Commons license intended for data. Using a CC0 license means that you revoke all of your rights over a dataset, including the attribution requirement which hinders data mining. This may seem counter-intuitive but recognize that you probably didn’t have those rights to begin with in countries like the US.
The strength of CC0 is that it is explicitly intended for content that is copyrightable in some jurisdictions and not others. It does this by removing all copyright claims universally. In the words of Creative Commons:
CC0 should not be used to mark works already free of known copyright and database restrictions and in the public domain throughout the world. However, it can be used to waive copyright and database rights to the extent you may have these rights in your work under the laws of at least one jurisdiction, even if your work is free of restrictions in others. Doing so clarifies the status of your work unambiguously worldwide and facilitates reuse.
CC0 clears away the confusion on whether a dataset is copyrightable, noncopyrightable, or copyrightable in some countries by applying an open license that is unambiguous and usable worldwide. It also allows for data mining and reuse, which makes it the best license for research datasets.
The other big consideration when using a CC0 license is attribution. Attribution is not required with this license, but that doesn’t mean that you should not cite a dataset. Data Dryad addresses this issue nicely in their FAQ:
CC0 does not exempt those who reuse the data from following community norms for scholarly communication, in particular from citation of the original data authors. On the contrary, by removing unenforceable legal barriers, CC0 facilitates the discovery, reuse, and citation of that data. Any publication that makes substantive reuse of the data is expected to cite both the data package and the original publication from which it was derived.
So while CC0 does not require attribution, community norms do. Community norms and the corresponding ethics of doing research are powerful motivators even when there are no comparable legal requirements in place. All this means is that despite not being required to attribute a dataset, you will still be expected to.
Finally, I will note that CC0 fits into a broader idea that scientific data should be open to encourage the scholarly process, an idea which is outlined by the Panton Principles. The Panton Principles identify CC0 and the Public Domain Dedication and License (PDDL) license as the two acceptable options for licensing datasets.
Final Thoughts
Many data repositories are already using the CC0 license: Dryad, figshare (which licenses data under CC0 and all other materials under CC BY), and, just announced this week, BioMed Central, among others. There is definitely growing consensus within the scientific community that CC0 is the preferred license for shared datasets.
Using a CC0 license removes any potential copyright ambiguity and makes it clear that someone else can freely use the licensed dataset. For US-based researchers, it is likely you never had copyright over your data to begin with, but it’s still best to be as explicit as possible that you are not exerting these rights. It makes data that much easier to share and reuse. Just remember that if you come across a CC0-licensed dataset you would like to use, you should cite the data creator even if it is not technically required.
Resources:
Elliott, R. (2005). Who owns scientific data? The impact of intellectual property rights on the scientific publication chain. Learned Publishing, 18(2), 91-94.
Murray-Rust, P. (2008). Open Data in Science. Serials Review, 34(1), 52-64.
My name is Kristin Briney and I love research data. Follow my adventures with data here or contact me with questions.
My third book, "The Data Management Workbook".
My second book, "Managing Data for Patron Privacy," co-authored with Becky Yoose.
My first book, "Data Management for Researchers".