The digitization of cultural heritage, increased datafication in all areas of society, and new infrastructures for sharing data within research and with the wider public are likely to change our views of the production of history. Participatory practices such as citizen science are, for example, enabled and further developed in projects that includes a broad public in crowdsourcing projects (Borowiecki, Forbes, & Fresa, 2016; Cameron, Kenderdine, 2007; Cerratto Pargman, Joshi and Wehn, 2019; Manzo, Kaufman, Punjasthitkul, & Flanagan, 2015). Digitization entails an increased datafication and development of data-driven practices in diverse societal sectors, which results in more and more human activities being monitored, analyzed, quantified, archived, classified, and linked to other data (Boyd & Crawford, 2012; Lyon, 2014).
Easier access to large-scale and linked datasets have opened up new possibilities to explore and create value from data and have enabled the development of more evidence-based quantitative scientific practices. This quantitative turn has influenced new research fields such as digital humanities, digital social science, e-learning, e-business, learning analytics, etc., where access, storage, and analysis of data drives the development of new infrastructures and methodologies. This creates changes not only at the level of data techniques and methods but also for researchers’ practices. For instance, some of these changes include increased standardization, development of shared infrastructures, and direct publication of research data, which changes the preconditions for research not only in the natural sciences but also in the humanities (Borgman et al., 2012; Carusi & Jirotka, 2009; De La Flor et al., 2010; Ribes & Lee, 2010; Schroeder, 2008).
Increasing datafication of research practices and their infrastructure create opportunities to nurture an open research culture, enabling researchers to share their results through open access (Kidwell et al., 2016; Nosek et al., 2015; Roche et al., 2014). Datafication and increased measurement practices are also reflected in areas such as education (Biesta, 2015), journalism (Coddington, 2015), and politics (Milan & Velden, 2016). Such practices are not only possible through the access, storage, and analysis of people’s data but via the tools for gathering and analyzing data. The proliferation of results is democratized through easily accessible infrastructures of people, survey tools, opinion polls and petitions, visualization, and dissemination to the public. This scientification, in which research methods are increasingly developed and integrated with everyday working practices, also creates expanded demands for a digital literacy. Not only is it necessary to understand how information is created and disseminated (boyd, 2011), but it is also necessary to foster deeper insights into research methodologies and archiving processes.
The critics of datafication claim that the belief in quantification poses dangers. For example, transparency decreases (it is often unclear on what grounds the data have been curated) (Andrejevic, 2014; Boyd, 2012; Bunnik et al., 2016; Driscoll & Walker, 2014), important values are lost if they are not easy to compile as numbers, and threats to personal integrity increase as data collections are disseminated, linked, and combined (Schradie, 2011). Efforts to generate, collect, identify, and classify data and data collections risk obscuring the multifaced work practices around history production, including reward structures, authority structures, formalization of knowledge, interdependencies among groups, trust mechanisms, and the transitional quality of data collections (Borgman, 2012). Datafication can also be seen as an increased commodification of various aspects of human practices, especially due to the datafication of our life-worlds. People’s opportunities to express themselves and organize themselves through the use of social media also contributes to new forms of surveillance and sources for consumer research (Hansson et al., 2018).
In the context of the humanities, there are critics who think that the qualitatively founded criticality that is at the core of contemporary research in the humanities is threatened and downplayed by, for example, politically controlled funding (Belfiore & Upchurch, 2013). Also, the majority of crowdsourcing and open science projects are within the realm of the natural sciences and areas where data is easy to quantify (Burke, 2012; De León, 2015). In the humanities, scientific processes are often different, and they demand other considerations. Research in the humanities is more about creating heterogeneity and differences than collaborating around one shared common goal (Anderson & Blanke, 2015). Furthermore, critics have pointed out that when archiving practices are distributed and maintained broadly over diverse sectors and groups in society, enabling a multifaced and fragmented notion of history, cultural heritage institutions might need to reevaluated their role in society (Fredheim, 2018).
However, the developers of infrastructure and the critics rarely meet. Few academic studies or commercial design processes take criticism seriously by developing practices and tools that combine qualitative and quantitative approaches in a self-reflexive way. Furthermore, while areas such as e-research, cyberinfrastructure, and crowdsourcing, are generally well covered by the ECSCW community, the intersection between these areas and the increasing digitalization processes and datafication in the humanities is less explored.
- Anderson, S., & Blanke, T. (2015). Infrastructure as intermeditation – from archives to research infrastructures. Journal of Documentation, 71(6), 1183–1202. doi:10.1108/JD-07-2014-0095
- Andrejevic, M. (2014). The big data divide. International Journal of Communication, 8(1), 1673–1689.
- Belfiore, E., & Upchurch, A. (2013). Humanities in the twenty-first century : beyond utility and markets.
- Biesta, G. J. J. (2015). Good Education in an Age of Measurement. Routledge. doi:10.4324/9781315634319
- Borgman, C. L., Wallis, J. C., & Mayernik, M. S. (2012). Who’s got the data? Interdependencies in science and technology collaborations. Computer Supported Cooperative Work, 21(6), 485–523. doi:10.1007/s10606-012-9169-z
- Borowiecki, K. J., Forbes, N., & Fresa, A. (Eds.). (2016). Cultural Heritage in a Changing World. Cham: Springer International Publishing. doi:10.1007/978-3-319-29544-2
- boyd, danah. (2011). Social network sites as networked publics: Affordances, dynamics, and implications. In Z. Papacharissi (Ed.), A Networked Self (pp. 39–58). Routledge.
- Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society, 15(5), 662–679. doi:10.1080/1369118X.2012.678878
- Bunnik, A., Cawley, A., Mulqueen, M., & Zwitter, A. (Eds.). (2016). Big Data Challenges. Society, Security, Innovation and Ethics. Palgrave MacMillan.
- Burke, K. L. (2012). Interface Facts. American Scientist, 100(6), 463–465.
- Cameron, Fiona. Kenderdine, S. (2007). Theorizing Digital Cultural Heritage. Cambridge, Massachussetts: MIT Press.
- Carusi, A., & Jirotka, M. (2009). From data archive to ethical labyrinth. Qualitative Research, 9(3), 285–298. doi:10.1177/1468794109105032
- Cerratto Pargman, T., Joshi, S., & Wehn, U. (2019, June). Experimenting with Novel Forms of Computing: The case of the Swedish Citizen Observatory for Water Quality Conservation. In Proceedings of the Fifth Workshop on Computing within Limits (pp. 1-10).
- Coddington, M. (2015). Clarifying Journalism’s Quantitative Turn. Digital Journalism, 3(3), 331–348. doi:10.1080/21670811.2014.976400
- De La Flor, G., Jirotka, M., Luff, P., Pybus, J., & Kirkham, R. (2010). Transforming scholarly practice: Embedding technological interventions to support the collaborative analysis of ancient texts. Computer Supported Cooperative Work, 19(3–4), 309–334. doi:10.1007/s10606-010-9111-1
- De León, M. A. P. (2015). Crowdsourcing in the memory institutions: Mass transcriptions. BiD, 35(35). doi:https://dx.doi.org/10.1344/BiD2015.35.26#sthash.DdLXRxNe.dpuf
- Driscoll, K., & Walker, S. (2014). Big Data, Big Questions| Working Within a Black Box: Transparency in the Collection and Production of Big Twitter Data. International Journal of Communication, 8(1243170), 20.
- Fredheim, L. H. (2018). Endangerment-driven heritage volunteering: democratisation or ‘Changeless Change.’ International Journal of Heritage Studies, 24(6), 619–633. doi:10.1080/13527258.2017.1399285
- Hansson, K., Ludwig, T., & Aitamurto, T. (2018). Capitalizing Relationships: Modes of Participation in Crowdsourcing. Journal Computer Supported Cooperative Work (JCSCW) Special Issue: Crowd Dynamics: Conflicts, Contradictions, and Cooperation Issues in Crowdsourcing.
- Kidwell, M. C., Lazarević, L. B., Baranski, E., Hardwicke, T. E., Piechowski, S., Falkenberg, L.-S., … Nosek, B. A. (2016). Badges to Acknowledge Open Practices: A Simple, Low-Cost, Effective Method for Increasing Transparency. (M. R. Macleod, Ed.)PLOS Biology, 14(5), e1002456. doi:10.1371/journal.pbio.1002456
- Lyon, D. (2014). Surveillance, Snowden, and Big Data: Capacities, consequences, critique. Big Data & Society, 1(2), 2053951714541861. doi:10.1177/2053951714541861
- Manzo, C., Kaufman, G., Punjasthitkul, S., & Flanagan, M. (2015). “By the People , For the People “: Assessing the Value of Crowdsourced , User Generated Metadata. Digital Humanities Quarterly, 9(1), 1–21.
- Milan, S., & Velden, L. van der. (2016). The Alternative Epistemologies of Data Activism. Digital Culture & Society, 2(2), 1–11. doi:10.14361/dcs-2016-0205
- Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., Yarkoni, T. (2015). Scientific standards. Promoting an open research culture. Science (New York, N.Y.), 348(6242), 1422–5. doi:10.1126/science.aab2374
- Ribes, D., & Lee, C. P. (2010). Sociotechnical studies of cyberinfrastructure and e-research: Current themes and future trajectories. Computer Supported Cooperative Work, 19(3–4), 231–244. doi:10.1007/s10606-010-9120-0
- Roche, D. G., Lanfear, R., Binning, S. A., Haff, T. M., Schwanz, L. E., Cain, K. E., … Kruuk, L. E. B. (2014). Troubleshooting Public Data Archiving: Suggestions to Increase Participation. (J. A. Eisen, Ed.)PLoS Biology, 12(1), e1001779. doi:10.1371/journal.pbio.1001779
- Schradie, J. (2011). The digital production gap: The digital divide and Web 2.0 collide. Poetics, 39(2), 145–168. doi:10.1016/j.poetic.2011.02.003
- Schroeder, R. (2008). e-Sciences as research technologies: reconfiguring disciplines, globalizing knowledge. Social Science Information, 47(2), 131–157. doi:10.1177/0539018408089075