Dennis D. McDonald (ddmcd@ddmcd.com) consults from Alexandria Virginia. His services include writing & research, proposal development, and project management. Follow him on Google+. He publishes on CTOvision.com and aNewDomain and volunteers with the Alexandria Film Festival. He is also on Linkedin. To subscribe to emailed updates about additions to this web site click here.

Electronic Lab Notebooks as the Tip of the Research Data Spear

Electronic Lab Notebooks as the Tip of the Research Data Spear

By Dennis D. McDonald

Tip of the spear

I’m no lab scientist but I do appreciate the details that Roberta Kwok presents in here excellent Nature article How to pick an electronic laboratory notebook. I also know enough about data sharing, collaboration technology, and the struggle of moving to “open data” to anticipate possible issues down the road if Kwok’s suggested selection criteria are ignored.

The variety of tools reviewed by Kwak is impressive. There is something here to please a wide range of research styles. Individual researchers most likely will find the value of the tools discussed by Kwok high -- as long as they are working with team members who are using the same or similar tools.

That last point is important. When reviewing research collaboration tools, especially if you are a manager tasked with selecting a tool for institutional or team use, you should also consider at least two related dimensions (besides cost, of course):

  1. Is the focus of the tool the researcher and his or her team, or is the focus the institution and field as a whole?
  2. Is the focus of the tool current research or is it future access to that research?

Team or institution focus?

If you want your team to work together seamlessly all team members should have similar tools that interact no matter where team members are in the research cycle, e.g.,

  • When capturing and recording data and research notes no matter what form they take.
  • When exchanging notes and data in real-time with your team in a secure environment.
  • When protecting privacy related or proprietary data while enabling the necessary exchange of raw or original data.

Any roadblock or delay to capturing and exchanging data may result in team members “voting with their feet” by either (a) selecting tools on their own, (b) by falling back to a “lowest common denominator” such as emailing attached data files, or (c) by not sharing data.

Both (a) and (b) have the potential for slowing down data exchange among team members even though they may initially seem like the simplest and most straightforward approach. Option (c) kills it.

The proliferation of collaboration tools becomes an issue in loosely defined teams where only a subset have a strict necessity to rapidly and efficiently share data. Regarding (c), this would be the ultimate vote against a collaboration tool if by adopting it collaboration is actually discouraged.

Current versus future access?

It’s not uncommon for funding agencies to require researchers to explicitly agree as a condition for funding that they will make their research data available to others. A variety of web and cloud based services are emerging worldwide to make such data exchange possible. The National Academies in the U.S. have also recommended that all research stages be made more “transparent,” not just when the research is complete but before as well.

While it’s normal for researchers to target a defined set of colleagues for access throughout the research cycle, making data accessible regardless of team membership or institutional affiliation will also for some be a concern when selecting a tool. This is where data and metadata standards come into play in order to simplify data exchange among various interacting systems.

In reality there still exist distinctions among researchers about what constitutes career advancing research and what doesn’t. Formal publication metrics have long been used to “grade” researchers in academia by focusing on peer reviewed journals. Such measures have long been the topic of much debate partly because such metrics oversimplify how research operates and can even misrepresent the actual contribution a researcher makes to the field. This is partly because influence can stretch over many years and partly because so much influential exchange of data and information has always occured outside formal channels such as peer reviewed journals and academic meetings.

Add to this already complex mix the increasing emphasis on data exchange and it becomes clear that the tools one selects for gathering and sharing data can also impact the “reach” of one’s research influence.

Moving forward

It’s probably unrealistic for a single tool to emerge to satisfy every requirement for researchers to capture, share, and ultimately access research data in all the varieties and volumes such data can take. The best we can hope for as we continue our increasing reliance on the web and clouds for sharing data are interchange standards that operate at different levels of standardization (e.g., physical as well as semantic) and that can be rapidly implemented to make tools such as lab notebooks – which are intended for use by individual researchers -- interact with each other.

We may never reach a “one tool to rule them all” state outside the closed networks of military or proprietary industrial research. Efficient data interchange will probably be the best we can do. How efficiently this occurs will depend not only funding but on the ability and willingness of institutions to cooperate not only on data interchange standards but also on data governance processes that stretch across organizational and disciplinary boundaries.

Conclusions

Having long promoted the use of tools to promote team collaboration I have become aware of several realities:

  1. Email, phone conversations, and meetings are not going away.
  2. There’s always someone on a team who resists adopting new collaboration tools thus requiring the extra time and expense of supporting workarounds.
  3. Creative people like to experiment with new tools.
  4. Bureaucracy and standardization will automatically be viewed as “bad” by some.
  5. Data governance measures and the costs associated with them have to be documented and justified.

My own belief is that researchers will always have to be masters of multiple tools including lab notebooks, data visualization software, statistical packages, data archiving services, grant application and financial reporting systems, and the wide range of laboratory and remote devices and sensors that generate, record, and store data. It’s always going to be a messy world out there and we all have to live with it.

Copyright © 2018 by Dennis D. McDonald

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