Low-barrier-to-entry data tools: creating and sharing humanities data
| DOI | https://doi.org/10.1108/LHT-07-2015-0073 |
| Published date | 20 June 2016 |
| Date | 20 June 2016 |
| Pages | 268-285 |
| Author | Linda L. Rath |
| Subject Matter | Library & information science,Librarianship/library management,Library technology |
Low-barrier-to-entry data
tools: creating and sharing
humanities data
Linda L. Rath
Newman Library, Baruch College, The City University of New York (CUNY),
New York, New York, USA
Abstract
Purpose –The purpose of this paper is to determine whether TAMS Analyzer and Viewshare are
viable free and open source software data sharing and creation tools for those with limited funding and
technological skills.
Design/methodology/approach –The participant observer method was used to collect experiential
evidence whileapplying the tools to a collectionof text-, image-, and video-based digitalcultural records.
Findings –TAMS Analyzer was found to be a low barrier to entry tool for those with coding and
qualitative data analysis experience. Those with general experience will be able to create datasets with
the support of manuals and tutorials, while those with limited experience may find it difficult to
use. Viewshare was found to be a low barrier to entry tool for sharing data online, and accessible for
all skill levels.
Research limitations/implications –TAMS Analyzer supports Mac and Linux platforms only, so
a low-cost software recommendation was made for those in Windows environments.
Practical implications –Librarians can use these tools to address data access gaps while promoting
library digital collections.
Social implications –With a greater understanding of data tools, librarians can be advisors,
collaborators, agents for data culture, and relevant participants in digital humanities scholarship.
Originality/value –The research evaluates both the capabilities of the tools and the barriers to using
or accessing them, which are often neglected. The paper addresses a need in the literature for
greater scrutiny of tools that are a critical component of the data ecology, and will further assist
librarians when connecting scholars to tools of inquiry in an environment with limited funding and
technical support.
Keywords Digital libraries, Academic libraries, Data, Qualitative research, Digital humanities,
Data services
Paper type Technical paper
Introduction
Data and analysis tools are critical for scholarly inquiry. The growth of digital data and
the proliferation of tools enable new approaches of exploration and the emergence of
new fields of study. Collectively, this data-intensive approach is known as the fourth
paradigm of scientific discovery and scholarship, following the empirical, theoretical,
and computational models (Lynch, 2009). Within humanities scholarship, the field of
digital humanities has emerged. Digital humanists engage in text mining and cultura l
analytics to examine digitized and encoded texts, images, audio, videos, and other
objects available for humanistic inquiry. Computational processing tools are used to
reveal details of source materials, uncover patterns and anomalies, quantify attributes,
and create visualizations to assist with knowledge creation. Unfortunately, access gaps
to resources and skills exist and they impede scholarly pursuits. The National Science
Board’s (2005) long-lived digital data collections report found, “Not all researchers have
equal access to the resources and expertise necessary to create and operate a digital
Library Hi Tech
Vol. 34 No. 2, 2016
pp. 268-285
©Emerald Group Publishing Limited
0737-8831
DOI 10.1108/LHT-07-2015-0073
Received 15 July 2015
Revised 16 October 2015
12 December 2015
Accepted 10 January 2016
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0737-8831.htm
268
LHT
34,2
data collection. The need is especially apparent at the level of an individual investigator
developing a research collection”(p. 23). While data and tools are more prevalent today,
access gaps continue to impede both researchers and librarians due to lack of funding,
skills, training, technical support, and common or shared data infrastructure (Borgman,
2015; Posner, 2013). The barriers encountered when addressing the access gaps differ
by discipline. Librarians and humanists must be strategic and resourceful to address
the economic, technological, and cultural barriers hampering humanities research.
One solution is the use of low barrier to entry tools, affordable tools requiring minimal
training or cost, to create and share data. For librarians advising or collaborating with
self-funded or minimally funded researchers in the humanities, free and open source
software (FOSS) data tools are attractive. The online digital research tools (DiRT)
directory is useful for identifying tools, but the many choices can be overwhelming or
confusing. Investing time with trial and error and disappointing results can be avoide d
if library literature included more evaluations of data tools and their uses in various,
domain-specific contexts. Tools are a vital component in the data ecology and need to
be examined and scrutinized (Zuiderwijk et al., 2014). Beyond listing software features,
evaluations can determine whether a tool’s barrier to entry is worthwhile, and an
achievable and practical solution for librarians and scholars with limited funding or
technical skills.
Using a case study approach, this paper explores TAMS Analyzer, a qualitative
data analysis tool, and Viewshare, an online digital collections display platform, as
complementary FOSS tools to create and share humanities data. A review of the
literature discussing the academic data landscape, humanities data, and library data
services provides context and six criteria for evaluating the tools. The process of
turning non-structured, qualitative data from text, image, and video files into
structured data and shared datasets will determine the degree to which TAMS
Analyzer and Viewshare are low barrier to entry data exploration and sharing tools
supporting humanities scholarship.
Literature
The academic data landscape
While the volume and variety of digital data created from the scholarly process is
growing, the contexts of data creation impact how frequently or easily it can be
accessed, shared, discovered, reused, and preserved. Anderson’s (2004) “long tail,”a
concept describing the internet market’s demand curve to illustrate the availability and
distribution of a few, high-demand and popular items versus the cumulative variety
and low-demand of obscure items, is regularly applied in library literature to describe
variables of scholarly data distribution and the variety of data available in terms of big
data sciences, such as astronomy and physics, and small sciences data, such as arts and
humanities (Borgman, 2012, 2015; Fitzpatrick, 2012; Heidorn, 2008; Wallis et al., 2013).
The data of big sciences are well funded, consistently structured, shared among large
teams for long-term projects, created for longevity, reusability, and transferability, and
are easier to find since datasets are placed in established domain- specific or university
repositories (Borgman, 2015; Wallis et al., 2013). Often, knowledge creation in big
sciences is funded by grants requiring data to be publicly available. This results in a
large volume of demand for these datasets, even though they represent a fraction of the
total data output. Small sciences data tends to have nominal funding or is self-funded,
and is structured for local projects with small teams or for individuals (Wallis et al.,
2013). Discovering small sciences data can be difficult since there are few, well
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Low-barrier-
to-entry
data tools
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