Which lessons about research data management and project management can we learn from the experiences of the doctoral candidate Alfred Issendorf, the protagonist of the novel “Beyond Sleep”?
In the novel Beyond Sleep - originally published in Dutch as Nooit meer slapen, by W.F. Hermans in 1966  four doctoral candidates team up for a geological fieldwork trip in the north of Norway. They are hiking and carry all their camping and research equipment with them. Already at the start of the expedition, it becomes clear that the Dutch researcher Alfred is less well prepared than the young Norwegians who know the area and have more camping experience.
On the plane and at the start of the trip, Alfred realizes his lack of planning and preparation which he keeps regretting throughout the rest of his fieldwork.
“I have no idea how much weight I’m capable of carrying on my back, when it comes down to it. (…) Stupid of me not to have done a dry run at home first (…) Subtracting a percentage of the total weight to allow for the fact that I won’t be carrying it in short bursts, but for hours at a time over rough, rock-strewn terrain, up hill [sic] and down dale.” (p. 36). “I wonder what my daily food intake actually weights” (p. 38).
“If only I had done more sport! If only this wasn’t my first visit to Norway” (p. 88).
It is thanks to the other young men that Alfred’s data collection does not fail before it has started. Alfred can hardly carry his load of camping and research equipment (p. 68; 100). He is already struggling before a single stone sample has been collected - and even more so later on during the trip after getting injured in a fall (p. 173). The Norwegians are carrying a bigger share of the equipment and food and arranged for an additional person to help carrying the load at the beginning, as the originally planned horse could not be arranged (p. 77, 88). They have also addressed the practicalities of the expedition, including the food weight issue, planning to “‘(…) catch fish on the way. Otherwise we won’t have enough to eat.’” (p. 67).
Lesson 1: At the beginning of your research - surely before you leave for a data collection trip - be aware of the limiting factors to your project.
A good example would be to have an idea as clear as possible about what type of data you are going to collect or create and what size and/or weight a) the data and b) the equipment you need in order to collect or create it would have. In the basic scenario Alfred is limited in his data collection by the physical size of his luggage and his own strength to carry a heavy load on uneven and unknown territory.
Limiting factors are always depending on the type of data and the context of the research. They could greatly vary per discipline, but good ones to start with could be:
In the example of Alfred’s project, a considerable amount of time during fieldwork has to be invested in travelling as well as taking care of shelter and finding food.
In an example like the fictional fieldwork trip of Alfred, project and data management or the lack of it becomes quite visible. In other types of research, for example of (empirical)legal research, a lot of the researchers’ work is being done at a desk and research data are less visible to others, as they are hidden in databases or on the researcher’s computer. While the researcher’s work often is a lot more safe and dry compared to Alfred’s, this doesn’t mean that these lessons do not apply to other disciplines. Assuming that time and money are limited, the proper planning of a project and management of the data will reduce the need for major changes during the project, as well as the risk of not making the estimated deadline or budget. There are limits to how many texts human beings can read and analyse in a certain amount of time and how fast one can search through a database. Changing one’s research approach, tools or software – or losing files! - when a project is already well on its way, often results in extra work for the researcher(s).
What also influences the amount of data that can be collected/created is when during your research you might have to further describe your data and method of collection as well as taking further processing steps. It could make a great difference whether these steps need to be done as soon as possible and are integrated in the data collection or if the majority of such tasks is scheduled for a later phase of the research. Noting down information about the search and selection strategy that you use in a physical archive or online database would need to be done immediately. When collecting a lot of text documents you might also want to invest part of your time in implementing your file naming system and adding further relevant information while collecting.
Steps that might need to be taken at a later stage of a research project should be part of the preparation for analysis. In the case of analysis of text documents, these might need to be de-identified, the sample refined and relevant parts selected, physical data digitized or digital files changed to different file formats. Even if documentation and processing takes place during a later phase, estimating the time needed for processing steps can still be very helpful. For example, if you collect interview data in the form of audio recordings in order to analyse the transcripts, you might need to know how long it takes to type out a transcript (check out transcribing calculator) or the fees for one hour of transcription in the relevant language(s) if you make use of an external service.
When you have identified factors that are limiting to your research, you can then deliberate on which ones you will have to accept and which ones you might wish to challenge. Depending on your project, you might look into possibilities to raise more funds, change the data collection period, increase the available human resources or use additional equipment. More experienced researchers using a comparable method should have examples of what can realistically be done in a certain amount of time, the context of your intended research.
For some disciplines digital scholarship could be an outcome when dealing with limiting factors. When having to collect and work with a lot of information, using special software can make a huge difference. Did you type and edit the bibliography of your Masters' thesis manually and now the amount of literature for your PhD trajectory seems rather scary? Check out the possibility to use software to manage your citation and switch between styles and create a bibliography with a few clicks.
Do you have to do a systematic literature review? Or do you have to do qualitative research and work with even more documents such as interview transcripts, legal or policy documents? Are you used to working with printed sources or separate files and, for instance, highlighted relevant passages in these text documents with different markers? Computer Assisted Qualitative Data Analysis (CAQDAS) software such as ATLAS.TI or NVivo, offers a lot of possibilities to use it for in-depth analysis and visualization as a result of which the rigor of textual analysis will increase . Depending on your research project, it might also be worthwhile to look into the possibilities of text and data mining - "extract[ing] structured data (in databases) from unstructured data (text)". There are a lot of tools for digital research. Check out this selection of common tools and software on the website of the Centre for Digital Scholarship of Leiden University.
Lesson 2 Research activities can potentially cause harm to the researcher or others.
During the fictional expedition in "Beyond Sleep" Alfred doesn’t only get injured, but his Norwegian friend - sorry to give away this part of the plot - also dies in an accident. It's not possible to prevent all theoretically possible risks like stumbling over your own feet, but depending on the research context, it might be necessary to consider possible harm that could be done to yourself and others and what can be done to mitigate the risks that you have identified.
Consult discipline specific resources and double check if you have all the relevant general information that your own university provides and follow the correct procedures, for example, with regard to ethics, academic integrity and privacy. Data management plan templates are often designed to help you to take relevant legal aspects of your data collection into account. Depending on your role and the research context, some of these steps might be taken by more senior researchers. In such cases, make sure that you follow their instructions and express any questions and doubts that you might have on these issues. Even if you are not collecting data directly from respondents, also data from databases, for example, could contain personal data or be subject to intellectual property rights or certain conditions for use in research.
Lesson 3. When your research includes activities that you are not used to or unfamiliar with, allow in your planning for a learning and training phase - at home and/or at the location of data collection/creation.
In the fictional example Alfred left rather unprepared and directly wants to shoulder the 'full load'. Not all the skills and knowledge needed for data collection will, and can, be taught in PhD lectures and seminars offered at universities or can be adequately mastered in a hectic last-minute preparation. In the case of Alfred, developing a good physical condition, acquire some basic survival and camping skills and specific knowledge of a certain area are the researcher’s own responsibility and require some preparation and training time. Research can be physically trying and data collection might include the development of new skills. While Alfred has to walk a lot, other researchers might have to work in other climates, in a different cultural, linguistic or organisational context while also using new tools and equipment etc. Where necessary, allow time for learning and adjustment to make the most of the data collection period. Where possible, practice in a safe environment with your tools and methods. Depending on your discipline and research method, this could mean to become familiar with database search options, do a mock interview to become comfortable with your recording equipment and try out consent procedures, check how fast you can take notes by hand or learn to use a new software program with a test dataset first.
The next part in this series will continue with lessons learned from the fictional fieldwork trip: "Equipment don’t fail me now".
This blog post is part of the series “Research in fiction through the lens of data management”.
Boom, M.S. (2018) Oops!….I didn’t prepare…. Available at: http://europeanbordercommunities.eu/blog/advising-alfred-applying-the-data-perspective (Accessed [date]).
 For international readers this blog post refers to an English translation Hermans, W.F. (2007). Beyond Sleep. (I. Rilke, Trans.). New York, NY: The Overlook Press. (Original work published in 1966, translation of the 27th impression published in 2003 by De Bezige Bij).
 Cf. for example, Silver, C., & Lewins, A. (2014). Using software in qualitative research. London: SAGE Publications Ltd.