Activity 220.127.116.11 “Post-doctoral Research Aid” of the Specific Objective 1.1.1 “To increase the research and innovative capacity of scientific institutions of Latvia and the ability to attract external financing, investing in human resources and infrastructure” of the Operational Programme “Growth and Employment”
Project name: Fundamental research “Digitalization of Social Memory: Digitalization Practices and their Affect on Nationalization and Transnationalization in National Museums”
Project implementer: Maija Spuriņa
Contract No.: 18.104.22.168/VIAA/2/18/252
Project implementation period: 01.01.2019. – 31.12.2021.
Project funding: EUR 133 805,88, including:
Project aim: The project will examine digitalization practices in three national museums in three different countries, and will inquire how digitalization is related to transnationalization of memory. Advancement of digital technologies and connective media provides, at least in theory, an opportunity to get rid of institutional, disciplinary, and also national boundaries. This connectivity potentially might result in narratives that challenge conventional national frameworks and broaden the range of imaginable. Yet, how this potentiality is realized is an empirical question. The project will address this question by a close look at digitalization as it is practiced by museums.
In this stage of the project the researcher continued analysis of the data available on nmkk.lv as well as started inquiry into digital colection of Finnland available on FINNA.fi and Estonia – MUIS.ee.
The researcher took par tin two conferences – an international conference “Culture Crossroads” and Annual Conference of Nordic Digital Humanities. Both conference were hosted in Riga and took place via ZOOM. The researcher presented findings of her project focusing on the potential of and barriers to data aggregation in digital museum collections. Both presentations are available on academi.edu.
Even though digitization of cultural heritage provides an opportunity to aggregate a huge amount of data and get a real burd-view on national and even trans-national cultural resources, in reality there are numerous digital barriers that hinder such aggregation. For example, Latvian museum database NMKK contains information on more than 1,3 million objects. Its infrustructure constitutes 438 information spaces for each objects, thus providing a potential space for more than 600 million units of information. In reallyty only about 31 million spaces are filled with actual information and a vast majority of this information cannot be aggregated due to discrepancies in formats. Only 20 fields from, the total of 438, are used by multiple museums. The fragmentation of data can be explained by several factors. First, museums themselves and the objects they store are very diverse and cannot be described in uniform terms. For example, the museums of natural history hold objects that do not have “Date of Production”, “Date of Use” or an “Author”, while majority of other museums use these fields to describe their collections. Second, each museum has its own tradition of organizing and describing its collections. Finally, data agrggation system in Latvia so far has been vey democratic – museums have been allowed to use any fields upon their choice and even to introduce new fields to describe their collections. The resulting data base is a collection of huge amount of information with almost no structure and with very weak possibilities of aggregation and overview.
The researcher analysed possibilities to aggregate NMKK data using several sociologically significant parameters, such as time, space, material, object type, and image. She consluded that the two, from the sociologial view point probably most significant, parameters – time and space – cannot be used for aggregation. Information on date of object is found across 47 fiels, information on space – across 13 fields. Even if compiled, the information on time appear in vastly diverse formats that do not allow computerized processings. Some of the formats used are as follows:
19th century 50ies
19th century 50ies – 70ies
18th – 19th century AC
the end of 19th century (middle, beginning)
the last quarter of 19the century
Even though this information is intelligable from a human point of view and can be manually processed, it cannot be processed via algoritm.
The researcher also started inquiry in to FINNA.fi – got familiar with the structure of the databse, the documentation and potential ways to access it via API. She learned webscraping with API using information available and consulkting with collegues at the National Library of Finnland. She also developed a Python algorythm for webscraping images available in FINNA and started data downloading process.