In his 2003 essay ‘Scarcity or Abundance’ Roy Rosenzweig sought to alert historians to what he called ‘the fragility of evidence in the digital era’ (Rosenzweig, 736). And whilst his concerns were focused on sources available on the open web, they can easily be extended to the born-digital materials – or data – historians create during their research.
It is this research data that the present guide will focus upon. But why?
Well, historians are moving toward using computers as the default means of storing all of their research data, their stuff. Their manuscripts have been digital objects for some time and their research is moving accordingly – be that in the form of typed notes, photographs of archives, or tabulated data. Moreover research data held in a digital form has clear advantages over its physical antecedents: it can be browsed and searched, hosted in ways that enable access in many places, and merged with or queried against other research data.
Merely putting research data into digital form does not guarantee it will survive. Here by survival I neither mean survive in a literal sense nor in a survival as readable by the next version of Microsoft Word sense, but rather in a usable by people sense. For if not a problem solved, the nuts and bolts of how to preserve research data for the future is a problem whose potential solutions have already been addressed at length, both with and without historians in mind. So too have data management experts, services and the like talked about scholarly best practice with regards to documenting, structuring and organising research data. In spite of all this, research data generated by an individual historian is at risk of loss if that historian is not able to generate and preserve it in a form they can understand and find meaningful years or decades after the fact, let alone someone else wading through the idiosyncrasies of their research process. In short, there is a risk of loss as a consequence of data being detached from the context of its creation, from the tacit knowledge that made it useful at the time of preparing talk X or manuscript Y. As William Stafford Noble puts it:
The core guiding principle is simple: Someone unfamiliar with your project should be able to look at your computer files and understand in detail what you did and why […]Most commonly, however, that “someone” is you. A few months from now, you may not remember what you were up to when you created a particular set of files, or you may not remember what conclusions you drew. You will either have to then spend time reconstructing your previous experiments or lose whatever insights you gained from those experiments.
William Stafford Noble (2009) A Quick Guide to Organizing Computational Biology Projects. PLoSComputBiol 5(7): e1000424. doi:10.1371/journal.pcbi.1000424
Drawing on the lessons and expertise of research data experts, the present guide will suggest ways in which historians can document and structure their research data so as to ensure it remains useful in the future. The guide is not intended to be prescriptive, rather it is assumed readers will iterate, change, and adapt the ideas presented to best fit their own research.
Documenting research data
Birkwood, Katie (girlinthe). “Victory is mine: while ago I worked out some Clever Stuff ™ in Excel. And I MADE NOTES ON IT. And those notes ENABLED ME TO DO IT AGAIN.” 7 October 2013, 3:46 a.m.. Tweet.
The purpose of documentation is to capture the process of data creation, changes made to data, and tacit knowledge associated with data. Project management methodologies, such as PRINCE2, place great emphasis on precise, structured, and verbose documentation. Whilst there are benefits to this approach, especially for large, complex, multi-partner projects, the average working historian is more likely to benefit from a flexible, bespoke approach to documentation that draws on, but is not yoked to, project management principles. In the case of historical research, the sort of documentation that might be produced to preserve the usefulness of research data includes:
- documentation describing notes taken whilst examining a document in an archive, such as the archival reference for the original document, how representative the notes are (e.g. full transcriptions, partial transcriptions, or summaries), how much of the document was examined, or decisions taken to exclude sections of the document from the research process.
- documentation describing tabulated data, such as how it was generated (e.g. by hand or in an automated manner), archival references for the original sources some data came from, or what attributes of the original sources were retained (and why).
- documentation describing a directory of digital images, such as how each image was created, where those images were downloaded from, or research notes that refer to them.
As the last example suggests, one of the key purposes of documentation is to describe the meaningful links that exist between research data, links that may not remain obvious over time.
When to document is very much up to the individual and the rhythm of their research. The main rule is to get into a habit of writing and updating documentation at regular intervals, ideally every time a batch of work is finished for the morning, afternoon, or day. At the same time it is important not to worry about perfection, rather to aim to write consistent and efficient documentation that will be useful to you, and hopefully someone else using your research data, years after the fact.
Research data and documentation should ideally be saved in platform agnostic formats such as .txt for notes and .csv (comma-separated values) or .tsv (tab-seperated values) for tabulated data. These plain text formats are preferable to the proprietary formats used as defaults by Microsoft Office or iWork because they can be opened by many software packages and have a strong chance of remaining viewable and editable in the future. Most standard office suites include the option to save files in .txt, .csv and .tsv formats, meaning you can continue to work with familiar software and still take appropriate action to make your work accessible. Compared to .doc or .xls these formats have the additional benefit, from a preservation perspective, of containing only machine-readable elements. Whilst using bold, italics, and colouring to signify headings or to make a visual connection between data elements is common practice, these display-orientated annotations are not machine-readable and hence can neither be queried and searched nor are appropriate for large quantities of information. Preferable are simple notation schemes such as using a double-asterisk or three hashes to represent a data feature: in my own notes, for example, three question marks indicate something I need to follow up on, chosen because ‘???’ can easily be found with a CTRL+F search.
It is likely that on many occasions these notation schemes will emerge from existing individual practice (and as a consequence will need to be documented), though existing schema such as Markdown are available (Markdown files are saved as .md). An excellent Markdown cheat sheet is available on GitHub https://github.com/adam-p/markdown-here) for those who wish to follow – or adapt – this existing schema. Notepad++ http://notepad-plus-plus.org/ is recommended for Windows users, though by no means essential, for working with .md files. Mac or Unix users may find Komodo Edit or Text Wrangler helpful.
To recap, the key points about documentation and file formats are:
- Aim for documentation to capture in a precise and consistent manner the tacit knowledge surrounding a research process, be that with relation to note taking, generating tabulated data, or accumulating visual evidence.
- Keep documentation simple by using file formats and notation practices that are platform agnostic and machine-readable.
- Build time for updating and creating documentation into your workflow without allowing documentation work to become a burden.
- Make an investment in leaving a paper trail now to save yourself time attempting to reconstruct it in the future.
Structuring research data
Documenting your research is made easier by structuring your research data in a consistent and predictable manner.
Well, every time we use a library or archive catalogue, we rely upon structured information to help us navigate data (both physical and digital) the library or archive contains. Without that structured information, our research would be much poorer.
Examining URLs is a good way of thinking about why structuring research data in a consistent and predictable manner might be useful in your research. Bad URLs are not reproducible and hence, in a scholarly context, not citable. On the contrary, good URLs represent with clarity the content of the page they identify, either by containing semantic elements or by using a single data element found across a set or majority of pages.
A typical example of the former are the URLs used by news websites or blogging services. WordPress URLs follow the format:
- website name/year(4 digits)/month (2 digits)/day (2 digits)/words-of-title-separated-by-hyphens
A similar style is used by news agencies such as a The Guardian newspaper:
- website name/section subdivision/year (4 digits)/month (3 characters)/day (2 digits)/words-describing-content-separated-by-hyphens
- http://www.theguardian.com/uk-news/2014/feb/20/rebekah-brooks-rupert-murdoch-phone-hacking-trial .
In archival catalogues, URLs structured by a single data element are often used. The British Cartoon Archive structures its online archive using the format:
- website name/record/reference number
And the Old Bailey Online uses the format:
- website name/browse.jsp?ref=reference number
What we learn from these examples is that a combination of semantic description and data elements make consistent and predictable data structures readable both by humans and machines. Transferring this to digital data accumulated during the course of historical research makes research data easier to browse, to search and to query using the standard tools provided by our operating systems (and, as we shall see in a future lesson, by more advanced tools).
In practice (for OS X and Linux users, replace all backslashes hereafter with forward slash), the structure of a good research data archive might look something like this:
A base or root directory, perhaps called ‘work’.
A series of sub-directories.
\work\events\ \research\ \teaching\ \writing\
Within these directories are series of directories for each event, research project, module, or piece of writing. Introducing a naming convention that includes a date elements keeps the information organised without the need for subdirectories by, say, year or month.
Finally, further sub-directories can be used to separate out information as the project grows.
\work\research\2014_Journal_Articles\analysis \data \notes
Obviously not all information will fit neatly within any given structure and as new projects arise taxonomies will need to be revisited. Either way, idiosyncrasy is fine so long as the overall directory structure is consistent and predictable, and so long as anything that isn’t is clearly documented: for example, the ‘writing’ sub-directory in the above structure might include a .txt file stating what it contained (drafts and final version of written work) and what it didn’t contain (research pertaining to that written work).
The name of this .txt file, indeed any documentation and research data, is important to ensuring it and its contents are easy to identify. ‘Notes about this folder.docx’ is not a name that fulfils this purpose, whilst ‘2014-01-31_Writing_readme.txt’ is as it replicates the title of the directory and included some date information (North American readers should note that I’ve chosen the structure year_month_date). A readme file I made for a recent project https://www.dropbox.com/s/i12cv5rdnfbdoz3/network_analysis_of_Isaac_Cruikshank_and_his_publishers_readme.txt contains the sort of information that you and other users of your data might find useful.
An cautionary tale should be sufficient to confirm the value of this approach. During the course of a previous research project, I collected some 2,000 digital images of Georgian satirical prints from a number of online sources, retaining the file names upon download. Had I applied a naming convention to these from the outset (say ‘PUBLICATION YEAR_ARTIST SURNAME_TITLE OF WORK.FORMAT’) I would be able to search and query these images. Indeed starting each filename with some version of YYYYMMDD would have meant that the files could be sorted in chronological order on Windows, OS X and Linux. And ensuring that all spaces or punctuation (except dash, dot and underscore) were removed from the filenames in the process of making them consistent and predictable, would have made command line work with the files possible. But I did not, and as it stands I would need to set aside a large amount of time to amend every filename individually so as to make the data usable in this way.
Further, applying such naming conventions to all research data in a consistent and predictable manner assists with the readability and comprehension of the data structure. For example for a project on journal articles we might choose the directory…
…where the year-month elements captures when the project started.
Within this directory we include a \data\ directory where the original data used in the project is kept.
Alongside this data is documentation that describes 2014-01-31_Journal_Articles.tsv.
Going back a directory level to \2014-01_Journal_Articles\ we create the \analysis\ directory in which we place:
Note the different month and date attributes here. These reflect the dates on which data analysis took place, a convention described briefly in 2014-02-02_Journal_Articles_analysis_readme.txt.
Finally, a directory within \data\ called \derived_data\ contains data derived from the original 2014-01-31_Journal_Articles.tsv. In this case, each derived .tsv file contains lines including the keywords, ‘africa’, ‘america’, ‘art’ et cetera, and are named accordingly.
2014-01-31_Journal_Articles_KW_africa.tsv 2014-01-31_Journal_Articles_KW_america.tsv 2014-02-01_Journal_Articles_KW_art .tsv 2014-02-02_Journal_Articles_KW_britain.tsv
To recap, the key points about structuring research data are:
- Data structures should be consistent and predictable.
- Consider using semantic elements or data identifiers to structure research data directories.
- Fit and adapt your research data structure to your research.
- Apply naming conventions to directories and file names to identify them, to create associations between data elements, and to assist with the long term readability and comprehension of your data structure.
This lesson has suggested ways for documenting and structuring research data, the purpose of which is to ensure that data is preserved by capturing tacit knowledge gained during the research process and thus making the information easy to use in the future. It has recommended the use of platform agnostic and machine-readable formats for documentation and research data. It has suggested that URLs offer a practice example of both good and bad data structures that can be replicated for the purposes of a historian’s research data.
These suggestions are intended merely as guides; it is expected that researchers will adapt them to suit their purposes. In doing so, it is recommended that researchers keep digital preservation strategies and project management best practice in mind, whilst ensuring that time spent documenting and structuring research does not become a burden. After all, the purpose of this guide is to make more not less efficient historical research that generates data. That is, your research.
Ashton, Neil, ‘Seven deadly sins of data publication’, School of Data blog (17 October 2013) http://schoolofdata.org/2013/10/17/seven-deadly-sins-of-data-publication/
Hitchcock, Tim, ‘Judging a book by its URLs’, Historyonics blog (3 January 2014) http://historyonics.blogspot.co.uk/2014/01/judging-book-by-its-url.html
Howard, Sharon, ‘Unclean, unclean! What historians can do about sharing our messy research data’, Early Modern Notes blog (18 May 2013) http://earlymodernnotes.wordpress.com/2013/05/18/unclean-unclean-what-historians-can-do-about-sharing-our-messy-research-data/
Noble, William Stafford, A Quick Guide to Organizing Computational Biology Projects.PLoSComputBiol 5(7): e1000424 (2009) http://dx.doi.org/10.1371/journal.pcbi.1000424
Oxford University Computing Services, ‘Sudamih Project. Research Information Management: Organising Humanities Material’ (2011) http://doi.org/10.5281/zenodo.28329
Pennock, Maureen, ‘The Twelve Principles of Digital Preservation (and a cartridge in a repository…)’, British Library Collection Care blog (3 September 2013) http://britishlibrary.typepad.co.uk/collectioncare/2013/09/the-twelve-principles-of-digital-preservation.html
Pritchard, Adam, ‘Markdown Cheatsheet’ (2013) https://github.com/adam-p/markdown-here
Rosenzweig, Roy, ‘Scarcity or Abundance? Preserving the Past in a Digital Era’, The American Historical Review 108:3 (2003), 735-762.
UK Data Archive, ‘Documenting your Data’ http://data-archive.ac.uk/create-manage/document
James Baker , "Preserving Your Research Data," Programming Historian, (2014-04-30), http://programminghistorian.org/lessons/preserving-your-research-data