Mr Paul Cremin1, Matt Miller1, Mr Matt Dunie2
2LabArchives LLC, San Diego, United States, email@example.com
The data produced in the practice of the scientific method is not managed in a uniform way by design. Specifically, in Academia, the breadth of mechanisms in use is extreme. Labs, researchers and collaborators are using thumb-drives, isolated PCs, network drives, large commercial storage services, open access blogging sites or hosted blogs. Some labs are handling sensitive information in paper notebooks, not under lock & key, and others are using smart phones. Major issues with compliance, intellectual property protection and data integrity are raised in these environments. The use of secure, online or locally based (data resides in-country or in-institution) systems designed for research data management, research workflow and institutional support facilitate better research data management, researcher & institutional oversight, access, provisioning and reporting. The collaborative nature of scientific research more enabled now than ever before. Scientists of any discipline use sophisticated technology in their workflows and should do the same for research data management.
There has been a growth in research-related data for several reasons. But the advancement and use of sophisticated research technology combined with the advent of immediate and low-cost communications and collaboration technology are enabling the creation of massive amounts of research data. These data must be managed properly in order to facilitate quality research. Research funding agencies make statements regarding data management and publishers provide for the submission of supplemental data. Yet there are research retractions for faulty and falsified data every month. Good data management tools and process can help to limit “bad” research.
According to a report published by the International Association of Scientific, Technical and Medical Publishers, there are more than twenty-eight thousand one hundred English language peer-reviewed journals in publication with an output of an estimated 2.5 million articles . In addition, the report mentions that surveys of researchers suggest as many as 1%–2% of scientists have fabricated or falsified research data. The report states there were more than four million unique authors in 2014. Simple arithmetic then suggests that anywhere from forty thousand to eighty thousand of the named authors of the 2014 sample may have used incomplete or inaccurate data at some point in their career.
Data management and Laboratory notebooks
Data Management Plans have been a requirement of various funding agencies for most of the past decade. Governmental agencies have been issuing policies on data and management for much longer. The basics are the same across all of the plans: preserve data & provide access. There are other components to be considered, however the basic premise is to make the data available for future research and evaluation for the purposes of reproducibility, research integrity, further research, or challenge.
The reasons for keeping a lab notebook are well known. The U.S. Health & Human Services Office of Research Integrity web page sums up the reasons very nicely :
- To establish good work practices.
- To teach people in your lab.
- To meet contractual requirements.
- To avoid fraud.
- To defend patents.
- To allow work to be reproduced by others.
- To facilitate preparation of formal reports, presentations and papers.
- To validate your research.
- To serve as a source for assigning credit to lab members.
The sixth bullet above may be the most important. Among the most basic underpinnings of science is the reproducibility of research. Without strong data management policies, documentation, and data management, reproducibility is at risk. Research labs of all disciplines have varying types of equipment, but there is at least one standard among them: Research is to be documented in accordance with the scientific method. Good data is data that is documented, stored, and accessible.
The scientific method at the highest level, appears quite simple. It is a circular process by which a researcher goes through at least four phases: ideation, research, analysis, and conclusion. The output is typically either a peer reviewed, or “grey” paper or report. However, when evaluated in more detail, the process is much more complex than the four steps just mentioned.
In granularity, good research requires skill, tools, and rigor. One of the more granular components is data management. . . and possibly, the definition of data. To the lay person, the data is limited to the results of an experiment or survey. But in reality, and depending upon the discipline, the data may include documentation of process and procedure. A research notebook may include information on failed experiments and documentation of missteps or unresolved questions. Research notes, environmental observations, lab notes and evaluations are all part of the research data. All of this can be “templated” into an Electronic Laboratory Notebook (ELN), ensuring that the complete research record is preserved and available for future use.
This is especially true when considering that a major element of validation in the research process is the reproducibility of previous work. Many retractions are the result of irreproducible research. According to some in the research community, the community itself has conflicting objectives. The pressure placed on researchers to increase their research output, to win more grant proposals, and to regularly publish their research can be at odds with solid scientific research management. Indeed, a recent article details two peer reviewed articles which “. . . urge scientists to make research more reproducible” .
It appears Research Data Management practices are highly-variable. If you want proof, walk the halls on different floors of a research building in academia and visit a dozen research labs. Depending on institutional policies and the level of independence provided lab heads, you may see as many as a dozen different mechanisms to manage research data in a dozen different labs. But, research labs must be different. . . that is the nature of the business of research, especially in Academia. And those differences are the basic nature and strength of academia.
Electronic Laboratory notebooks
But there are tools in the marketplace that can help researchers maintain their independence, provide researchers and their administrators with the ability to protect their work product and enable scientific reproducibility. They are Electronic Laboratory Notebooks (ELN – a terrible 1980’s-era name describing a product that essentially replaces the analog paper notebook with a much-improved digital version).
When data are entered into an ELN – and data means research data, notes, observations, formulas, equations, sketches, data sets, images. . . any type of data – the platform must provide the ability to support several interested parties: 1) a funding agency’s requirement for a Data Management Plan; 2) the researcher’s need to document their research; 3) the administration’s need to be able to protect IP and prove discovery; and 4) the publisher’s need to review and publish data supporting the research; and 5) the institutes industry partner’s need to easily review and acquire data in a format that improves commercialization efforts and is frankly the standard in the industry.
ELNs can support the Scientific Method in ways traditional paper notebooks cannot. They also support institutional research policies and objectives and provide a platform for institutional data management and research support. A robust ELN supports Data Integrity, Data Lifecycle Management, Data Management, Data Accessibility, Collaboration, and Research Reproducibility. In today’s world of global collaborative research, digital information, and robust, advanced information technology, ELNs are becoming the “must have” tool for researchers and institutions.
 Two manifestos for better science, Discover Magazine January 11 (2017), http://blogs.discovermagazine.com/ neuroskeptic/2017/01/11/manifestos-better-science/#.WT6mvmjyuUk.
 M. Ware and M. Mabe, The STM Report: An Overview of Scientific and Scholarly Journal Publishing, 4th edn, International Association of Scientific, Technical and Medical Publishers, Netherlands, 2015, http://www.stm-assoc.org/ 2015_02_20_STM_Report_2015.pdf.
** Full article published by Matt Dunie, President and Co-Founder, LabArchives in:
Information Services & Use 37 (2017) 355–359
Experienced data management consultant, specialist literature researcher, trainer and reference info specialist.
I’ve had 20 years of high level research experience, across fields such as competitive intelligence, company research, medical literature searching and pharmacovigilance, market research, drug safety literature searching, intellectual property and finance. My significant experience in the corporate, academic and scientific community means I have a working knowledge of the required knowledge resources, the various data management resources available and how these resources can be leveraged to improve research workflows.