Cite this as: Seaton, K-L., Laužikas, R., McKeague, P., Moitinho de Almeida, V., May, K. and Wright, H. 2023 Understanding Data Reuse and Barriers to Reuse of Archaeological Data. A quality-in-use methodological approach, Internet Archaeology 63. https://doi.org/10.11141/ia.63.8
Over the last decade, innovation has centred on making archaeological data more interoperable, increasing the discoverability of data through integrated cross-search and facilitating knowledge creation by combining data in new ways. An emerging research challenge for the next decade is optimising archaeological data for reuse and defining what constitutes good practice around reuse. Critical to this research is understanding the current state-of-the-art regarding both existing best practices and barriers to using and reusing archaeological data. This research aimed to understand how to optimise archives and interfaces to maximise the discovery, use and reuse of archaeological data and explore how archaeological archives can better respond to user needs.
The study was bound by (i) the reuse of digital archaeological archives; (ii) orientation to content usability and reusability; (iii) maintaining a user-orientated approach; (iv) collecting data from professionals in archaeology and heritage. The research group members adopted the quality-in-use conceptual approach for this study. Quality in use is 'the degree to which a product or system can be used by specific users to meet their needs to achieve specific goals with effectiveness, efficiency, satisfaction, and freedom from risk in specific contexts of use'. The research methodology is based on the SQuaRE (System and Software Quality Requirements and Evaluation) model, represented in the ISO/IEC 25000 standards series. In addition, the quality-in-use metric for investigation of reuse and barriers to reuse of archaeological data were adopted from the standardised measurement functions and methods of ISO/IEC 25022:2016. The result was a methodological model composed of 5 characteristics (Effectiveness, Efficiency, Satisfaction, Context coverage and Usability) with 14 measures (Task completeness, Objectives achievement, Task time, Cost-effectiveness, Overall satisfaction, Satisfaction with features, User trust in the system, data and paradata, User pleasure, Physical comfort, Context completeness, Flexible context of use and User guidance completeness). The methodology was tested with specific Contexts of use (use cases), orientated to a distinct user with the specific professional goal of data reuse. Three use cases relating to 3D Pottery, radiocarbon, and GIS data were created. The pilot study has proven that the methodology works and could be applied in future research. This article discusses the application of the quality-in-use approach for evaluating the quality of digital archaeological archives, as well as presenting the methodology and the results of the pilot study.
Corresponding author: Kristy-Lee Seaton
kjs553@york.ac.uk
University of York
Rimvydas Laužikas
rimvydas.lauzikas@kf.vu.lt
Vilnius University Faculty of Communication
Peter McKeague
peter.mckeague@hes.scot
Historic Environment Scotland
Vera Moitinho de Almeida
vmoitinho@letras.up.pt
Centre for Digital Culture & Innovation (CODA), Faculty of Arts and Humanities of University of Porto
Keith May
Keith.May@HistoricEngland.org.uk
Historic England
Holly Wright
holly.wright@york.ac.uk
Archaeology Data Service
Figure 1: Map of countries participating in COST (European Cooperation in Science and Technology)
Figure 2: 'What is your area of research?', a list of responses regarding participants' occupations
Figure 3: Effectiveness evaluates the respondents' ability to complete the tasks in a given scenario. The measurement is a ratio between the tasks supplied and the number of tasks respondents could complete. This graph displays the effectiveness of scenarios with five tasks
Figure 4: Effectiveness evaluates the respondents' ability to complete the tasks in a given scenario. The measurement is a ratio between the tasks supplied and the number of tasks respondents could complete. This graph displays the effectiveness of scenarios with four tasks
Figure 5: Efficiency determines the resources required to complete a given scenario. The first efficiency measure was the average time taken to complete a scenario
Figure 6: Efficiency determines the resources required to complete a given scenario. The second measure of efficiency is the cost-effectiveness of the user, where the cost of completing each task is considered in relation to the time taken to complete individual tasks
Figure 7: 'Was the platform easy to use?' assessed the respondents' satisfaction when using their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform
Figure 8:: 'Were you satisfied with the features offered?' assessed the respondents' satisfaction with the features offered on their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform
Figure 9: 'Did you enjoy using the platform?' assessed the respondents' enjoyment when using their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform
Figure 10: 'Did the platform have the data you were looking for?' assessed the respondents' ability to find relevant data when using their chosen platform. Satisfaction was used to determine the degree to which the user's expectations were met when using their chosen platform
Figure 11: 'Did the platform help you better engage with the data?' assessed whether the respondents' chosen platform improved their engagement with the data offered. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform
Figure 12: 'What was your overall satisfaction with using your chosen platform?' assessed the respondents' overall satisfaction when using their chosen platform. Satisfaction was used to determine the degree to which users' expectations were met when using their chosen platform
Figure 13: 'Was there enough metadata for you to understand the dataset?' assessed the extent to which the respondents' trusted their chosen platform. Trust was a sub-characteristic of satisfaction that determined the degree to which the user's expectations were met when using their chosen platform
Figure 14: 'Was the metadata sufficient for your research needs?' assessed the extent to which the respondents' trusted the data used for the survey. Trust was a sub-characteristic of satisfaction that determined the degree to which the user's expectations were met when using their chosen platform
Figure 15: 'Are you confident that you could use the metadata in your own research?' assessed the extent to which the respondents' trusted the metadata or paradata found on their chosen platform. Trust was a sub-characteristic of satisfaction that determined the degree to which the user's expectations were met when using their chosen platform
Figure 16: Flexibility measured the extent to which the data could be used in additional contexts, in this instance, whether the data could be exported for use with different software. The question provided a text box, and the responses are summarised here. Respondents who failed to answer the question were marked as 'undefined'
Figure 17: Flexibility measured the extent to which the data could be used in additional contexts, in this instance, by considering whether the respondent was satisfied with the file formats available for download. Respondents who failed to answer the question were marked as 'undefined'
Figure 18: Usability determined whether sufficient documentation was provided to use their chosen platform. The question posed whether licensing information was available. Respondents who failed to answer the question were marked as 'undefined'
Figure 19: Usability determined whether sufficient documentation was provided to use their chosen platform. The question was whether user documentation was made available to assist the respondent in navigating the platform. Respondents who failed to answer the question were marked as 'undefined'
Archaeology Data Service and Digital Antiquity 2013 Caring for Digital Data in Archaeology: A Guide to Good Practice, Oxford: Oxbow Books.
The British Standards Institution 2016 ISO/IEC 25022:2016: Systems and Software Engineering - Systems and Software Quality Requirements and Evaluation (SQuaRE) - Measure of Quality in Use, BSI Standards Limited. https://www.iso.org/standard/35746.html#:~:text=ISO%2FIEC%2025022%3A2016%20defines,to%20product%20or%20system%20quality
CoreTrustSeal 2017 Data Repositories Requirements. https://www.coretrustseal.org/why-certification/requirements/ [Last accessed: 20 October 2022].
Faniel, I., Kansa, E., Kansa, S.W., Barrera-Gomez, J. and Yakel, E. 2013 'The challenges of digging data: a study of context in archaeological data reuse' in J.S. Downie et al. (eds) Proceedings of the 13th ACM/IEEE-CS joint conference on digital libraries, 22 July 2013, New York: Association for Computing Machinery. 295–304. https://doi.org/10.1145/2467696.2467712
Garvin, D.A. 1984 'What does 'Product Quality' really mean?', Sloan Management Review 26(1), 25–43.
Geser, G., Richards, J.D., Massara, F. and Wright, H. 2022 'Data management policies and practices of digital archaeological repositories', Internet Archaeology 59. https://doi.org/10.11141/ia.59.2.
Huggett, J. 2018 'Reuse remix recycle: repurposing archaeological digital data', Advances in Archaeological Practice 6(2), 93–104. https://doi.org/10.1017/aap.2018.1
Kansa, E.C., Kansa, S.W. and Benjamin, A. 2014 'Publishing and pushing: mixing models for communicating research data in archaeology', International Journal of Digital Curation 9(1), 57–70. http://www.ijdc.net/article/view/9.1.57/341
International Organisation for Standardisation 2016 ISO/IEC 25022:2016 Systems and software engineering — Systems and software quality requirements and evaluation (SQuaRE) — Measurement of quality in use. https://www.iso.org/standard/35746.html [Last accessed: 21 February 2023].
International Organisation for Standardisation 2017 ISO/IEC 25010:2011 Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — System and software quality models. https://www.iso.org/standard/35733.html [Last accessed: 21 February 2023].
International Organisation for Standardisation 2019 ISO/IEC 25020:2019 Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality measurement framework. https://www.iso.org/standard/72117.html [Last accessed: 21 February 2023].
Lee, J. W., Jones, P.S., Mineyama, Y. and Zhang, X.E. 2002 'Cultural differences in responses to a Likert scale', Research in Nursing & Health 25(4), 295–306. https://doi.org/10.1002/nur.10041
Marttila, S. and Botero, A. 2017 'Infrastructuring for Cultural Commons', Computer Supported Cooperative Work: CSCW: An International Journal 26(1-2), 97–133. https://doi.org/10.1007/s10606-017-9273-1
Marwick, B. and Pilaar Birch, S.E. 2018 'A standard for the scholarly citation of archaeological data as an incentive to data sharing', Advances in Archaeological Practice, 6(2), 125–43.
Richards, J.D., Jakobsson, U., Novák, D., Štular, B. and Wright, H. 2021 'Digital archiving in archaeology: the state of the art. Introduction', Internet Archaeology 58. https://doi.org/10.11141/ia.58.23
Sheehan, B. 2015 'Comparing digital archaeological repositories: tDAR versus Open Context', Behavioural and Social Sciences Librarian 34(4), 173–213. https://doi.org/10.1080/01639269.2015.1096155
Sobotkova, A. 2018 'Sociotechnical obstacles to archaeological data reuse', Advances in Archaeological Practice 6(2), 117–121. https://doi.org/10.1017/aap.2017.37
Wilkinson, M.D., Dumontier M., Aalbersberg I.J., Appleton G., Axton, M., Baak, A., Blomberg, N., Boiten, J.W., Bonino da Silva Santos, L., Bourne, P.E., Bouwman, J., Brookes, J. A., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C.T., Finkers, R., Gonzalez-Beltran, A., Gray, A.J.G., Groth, P., Goble, C., Grethe, J.S., Heringa, J., 't Hoen, P.A.C., Hooft, R., Kuhn, T., Kok, R., Kok, J., Lusher, S.J., Martone, M.E., Mons, A., Packer, A.L., Persson, B., Rocca-Serra, P., Roos, M., van Schaik, R., Sansone, S.A., Schultes, E., Sengstag, T., Slater, T., Strawn, G., Swertz, M.A., Thompson, M., van der Lei, J., van Mulligen, E., Velterop, J., Waagmeester, A., Wittenburg, P., Wolstencroft, K., Zhao, J. and Mons, B. 2016 'The FAIR guiding principles for scientific data management and stewardship', Scientific Data 3, 160018. https://doi.org/10.1038/sdata.2016.18.
Wright, H. 2020 WG1 Exploratory Workshop Results!. https://www.seadda.eu/?p=1417 [Last accessed: 26 September 2022].
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