Tag: research

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Lab News

Funding success for the Mapping Museums team

Wooden packing cases of different sizes with stencilled text and labels stand in front of a blank wall

We are pleased to announce that the Arts and Humanities Research Council has awarded £1million to the Mapping Museums research team for their project ‘Museum Closure in the UK 2000-2025’.  

The new research will use trans-disciplinary methods to analyse closure and collections dispersal within the UK museums sector. Its aim is to examine the geographic distribution of closure, to better understand types of closure (e.g., whether museums are mothballed or disbanded), and to document the flows of objects and knowledge from museums in the aftermath of closure. We will investigate the afterlife of collections, find out if museum exhibits are scrapped, sold, stored, or re-used, and examine ‘outreach’ and temporary museums. A Knowledge Base will be designed to model and store the collected data, and visualisations and analyses of the data will be developed. Above all, we aim at critically reassessing notions of permanence and loss within the museums sector.  

‘Museum Closure’ is based at Birkbeck, University of London and at King’s College London, and will run for two years, beginning in October 2023. It is led by Fiona Candlin, Professor of Museology, who will be working with co-investigators, Dr Andrea Ballatore (King’s College London), a specialist in cultural data science, Alexandra Poulovassilis, Emeritus Professor in Computer Science, and Peter Wood, Professor in Computer Science. The post-doctoral researcher is Dr Mark Liebenrood (museum history) and we will be recruiting a second post-doctoral researcher in data science.

(Image modified from original, by elston on Flickr)

Categories
Research Process

A week in the life

Illustration of a man sitting in an attic room at a desk. The desk is piled high with books and he is reading one of them. A window is open behind him.

What does the Mapping Museums research assistant do all day? I sometimes wonder where all the time goes. Although the vast majority of the four thousand-odd museums listed in the database were added before I really began work on the project, I’ve added well over a hundred new museums and made corrections to the entries for hundreds more. But how do we find out about museums that were not already in the database, and where do all the amendments come from? Here I offer a peek into a ‘typical’ week.

Monday

A friend of the project reports on Twitter a possible new museum she’s spotted while on a bike ride. It turns out that it is not new, but the small private museum has slipped under the Mapping Museums radar, so I add it to the database. Another contact has suggested we check a directory of railway preservation sites to make sure we haven’t missed any railway museums during our searches. I order it from the British Library for my next visit.

Tuesday

I have Google news alerts set up in the hope of spotting museums closing and opening, and I open my email this morning to find an alert for a new museum. All too often these alerts don’t produce anything useful, but on this occasion they have. A new private museum dedicated to the footballer Duncan Edwards has opened above a shop in Dudley, in the West Midlands, so I make a note to add it to the database.

The Mapping Museums database is constantly being updated. When we receive new information for museums currently open, we update our records accordingly. Today I find that a curator has supplied updated details for their museum using the form for editing data, and process the update so that the details are added to the database.

Wednesday

At the British Library for my own PhD research, I also look at the railway preservation directory. At first sight it looks somewhat daunting, as it lists hundreds of railway preservation sites in Britain opened from the 1950s onwards, classified into thirteen types. Each one of these will potentially need to be checked against the database to see whether museums need to be added. I copy the pages I need for processing later.

Looking through copies of Museums Journal I see mention of another museum that I’m not familiar with. It’s in the database, but the news item gives extra information about the museum’s governance that we didn’t have, so I make a note for later.

Sometimes we need to contact museums directly to confirm information, and recently I have been trying to get hold of the administrator of a small military museum in Scotland (the museum came to our attention as part of a list supplied by a liaison officer for regimental museums). The administrator is only on site occasionally, and so far I have missed him each time I’ve called. I miss a call while sitting in the library’s reading room, and when I return it later I have just missed him, but his colleague supplies his email address. By email he confirms the nature of the collection, but does not know when the museum was first opened – he has been in the post for less than two years. One thing I’ve discovered doing this research is that it is quite common for the opening date of museum not to be known by those who run it. A museum’s foundation date is often tacit knowledge, which can easily be lost as staff change. The database currently contains almost five hundred museums for which we do not have a certain opening date, and we record them as date ranges instead based on the best information available.

Thursday

I resume work on a list of museums that another contact has provided us with. They are all in North East England. Not all of them qualify as museums in the way that the project defines them but many do, and for whatever reason some have been overlooked. Small private museums are easily missed, and it would not be possible for the project to have compiled as comprehensive a list as it has without the benefit of local knowledge. One example is the Ferryman’s Hut Museum in Alnmouth, which I add to the database.

Friday

The opening date of a museum is proving elusive. My enquiry to the owners remains unanswered, so I resume searching online. Eventually I track it down in the Gloucestershire volumes of the Victoria County History, an incredibly valuable local history resource.

It’s fortunate that that museum was recorded, but what do you do when a museum has long closed and there are no references to be found online, no matter how hard you search? Well, you might descend the archive.org rabbit hole. As anyone who has followed references in Wikipedia may have noticed, website links stop working all the time – a phenomenon colloquially known as ‘link rot’. The Wayback Machine preserves websites for posterity, keeping copies of those still online as well as many that have long since vanished. In this case we knew that the museum had closed thanks to an estate agent’s website, but when did it open? The website for the tower in the Scottish Borders had fortunately been captured by the wayback machine, and while there was no definitive information about the museum, there was enough to allow a range of dates for the museum’s opening to be recorded.

It’s the end of another week of data collection and checking. That list of hundreds of railway preservation sites will have to wait until another time …

Mark Liebenrood

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Lab News

Mapping Museums Database: New Developments

Since our blog entry on building the database, we have held a series of user trials of the Mapping Museums database and the Web Application through which the database is accessed. These trials have given us much useful feedback for improving the system as well as a positive endorsement of the overall development approach. For example, museums experts told us that the system is “useful to anyone wanting to understand the museum sector as this is the closest we’ve ever been to getting a full picture of it”, “intuitive to use”, “the Museum equivalent of YouTube”.

Following the user trials, we have made some improvements and extensions to the user interface, have incorporated data relating to some 50 additional museums, and have added three new attributes for all of the 4000+ museums in our database. The new attributes relate to the location of each museum and are Geodemographic Group and Geodemographic Subgroup and Deprivation indices (English indices of deprivation 2015, Welsh Index of Multiple Deprivation, and Northern Ireland Multiple Deprivation Measure 2017).

The figure on the left shows the architecture of our system. It has a three-tier architecture comprising a Web Browser-based client served by a Web Server connecting to a Database Server.  The database is implemented as a triple store, using Virtuoso, and it supports a SPARQL endpoint for communicating with the Web Server. The system currently comprises some 28,600 lines of Python code, as well as additional scripts consisting of 25,800 lines of JavaScript, HTML pages, and other source files.

Usage of the database and Web Application by the project’s researchers has already led to insights about periods and regions that show high numbers of museum openings or closings, changes in museums’ accreditation and governance status over the past 60 years, and popular subject areas. There will be two more years of detailed research, both qualitative and quantitative, building on this first phase of research.

The qualitative research is comprising both archival and interview-based work. The quantitative research is investigating correlations between high rates of openings or closings of museums and attributes such as accreditation, governance, location, size, and subject matter. The new attributes Geodemographic Group/Subgroup and Deprivation Index are enabling new analyses into the demographic context of museums’ openings/closing, including cross-correlation of these aspects with the other museum attributes, and hence the charting of new geographies of museums.

Ongoing development work is extending the Web Application into a full Website to showcase the outcomes and findings of the project.  We are also developing a new web service to allow the capture of data updates relating to existing museums and the insertion of data about new museums. There will be forms allowing the public upload of such data which will be subsequently validated by the project’s domain experts before being inserted into the database.

© Alexandra Poulovassilis, Nick Larsson, Val Katerinchuk

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Lab News

Two Years On: An Update

2 years spelled out in children's blocks

The Mapping Museums project is coming to the end of its second year. To mark the half way point of the research, this blog provides a brief update on some of the work so far.

Finalising the data
Early this year, Dr Jamie Larkin, the researcher, completed the main phase of data collection. We are continuing to make changes to the dataset as new museums open and existing museums close, and we’re still trying to hunt down some missing opening and closing dates, so it remains work in progress. Nonetheless, we now have information on almost 4,000 museums that have been open at some point between 1960 and the present day.

Evaluating the knowledge base

Alongside data gathering, we have designed a knowledge base that allows users to browse, search, and visualise the data in nuanced and precise ways, and which we described in our last blog (See: Managing Patchy Data). As part of the design process, Professor Alex Poulovassilis, the co-investigator on the project, and Nick Larsson, the Computer Science researcher and developer of the knowledge base, organised a series of trials to evaluate the knowledge base. These provided us with valuable feedback and we responded by making changes to how the material is presented and navigated.

We got enormously positive responses at the most recent user trial in Manchester in July. Having lived and breathed the research for the last two years it was very encouraging to hear Emma Chaplin, director of the Association for Independent Museums, call it the “museums equivalent to YouTube” and say that she could while away hours browsing the material; to know that staff from Arts Council England thought that it was “intuitive  to use”; and generally that the trial participants assessed it as a being a useful resource for them in their roles and for others in the sector.

Analysing and publishing the findings

Having finished the data collection and the main phase of developing the knowledge base, we have been able to start analysing the data. These initial analyses will be the basis of a series of articles, and over the summer, the team has been working on four publications:

  • ‘Mapping Museums and managing patchy data’ examines the reasons why data on the museum sector is so incoherent, how the project sought to remedy that situation, in part by building a system that acknowledges uncertain information.
  • ‘Where was the Museum Boom?’ looks at the massive expansion of museums in the late twentieth century and asks whether or not the boom took place across the UK, or if there were regional variations.
  • ‘Creating a Knowledge Base to research the history of UK independent museums: a Rapid Prototyping approach’, covers the computer science research that underpins the conceptualisation and construction of the knowledge base.
  • ‘Missing Museums’ deals with the recent history of museum surveys, considers the focus on professionalised museums, and asks what the sector looks like when we factor in unaccredited museums.

Brief versions of the first and second of these papers were also presented at conferences: Digital Humanities Congress at Sheffield University and Spatial Humanities at Lancaster University, both of which took place in September, and we hope that the full versions of all four articles will be published within the next academic year. We’ll let you know when that happens.

The process of analysing data has been greatly helped by having Dr Andrea Ballatore join the team early in 2018. Andrea is a Lecturer in GIS and Big Data Analytics in the Department of Geography at Birkbeck and he is leading the statistical analysis within the project. He has also made invaluable contribution to developing the knowledge base, particularly with respect to mapping the data.

The Mapping Museums Team

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Research Process

Modelling Patchy Data

Repairs anchor thread

How do researchers manage when they have missing data? One of the initial aims of the Mapping Museums project was to establish an authoritative dataset of all the museums open between 1960 and 2020, and to record information on their location, governance, accreditation status, subject matter, opening and closing dates, and visitor numbers. Having this material would provide the first step in constructing a nuanced, evidence-based history of the development of the museum sector during the period, and so the research team began to compile information from numerous sources: surveys conducted by government bodies, by the Association of Independent Museums, and the Museums Association; lists of museums held by the national organisations for the arts; guidebooks; and websites. The researchers also got in touch with dozens of tourist boards and local history groups, and hundreds of curators and volunteers to follow up leads or information. All this material was cross-checked within the team, and then reviewed by experts from the Museum Development Network.

We now have a rigorously researched list of museums in the UK from 1960-2020. Even so, there is still a considerable amount of missing data. When the first phase of data collection was finished we had identified almost 4,000 museums and had established the following coverage of their key attributes:

  • Museum opening dates: 88%
  • Museum closing dates: 6%
  • Governance: 92%
  • Visitor numbers: 67%

The question then was, how were we to represent and model the missing dates, governance, and visitor numbers within our analysis?

At the same time as collecting data, we started to build a knowledge base that allows users to explore. The system is designed so that users can browse in a structured way through the categories of accreditation, governance, location, size, subject classification, year of opening and year of closing, and see the results on a map or in a list view. Alternatively, they can submit a detailed search that allows them to filter results by combinations of the categories above, or they can generate visualisations of how the different types of museums have emerged over time and create tables showing how the various categories inter-relate. At any point, it is possible to scrutinise the details of individual venues.

One option for dealing with missing information was to exclude museums with missing data from the relevant searches. The problem with that approach is that incomplete data tends to be associated with small, unaccredited museums or with museums that have since closed and so excluding them on this basis would bias our analysis in favour of extant established museums., which would be counter to the purposes of the project as a whole. Thus, when we could not identify a museum’s governance, we assigned it a value of Unknown. The advantage of an explicit Unknown category is that the missing data is made apparent, and the problem of data patchiness is exposed rather than hidden.

We took a different approach to opening and closing dates because we often had rough information about these rather than no information at all – for example, we might know that a museum had closed at some point in the 1990s. This approximate information would be lost if we just categorised a date as ‘unknown’. Therefore, we decided to use a date range of the form (earliest possible year, latest possible year) to capture imprecise knowledge about museum opening/closing dates. These date ranges are used in different ways across the different facilities provided by our system:

  • In the Browse facility, we take museums’ opening/closing dates to be the mid point of the specified date range.
  • In the Visualise facility, event occurrences are ‘spread’ equally over a date range. For example, if a museum is known to have opened between 1965 and 1969, then the count of one museum opening is spread over that time period (i.e. a count of 0.2 is assigned to each of the five years 1965, 1966, 1967, 1968, 1969).
  • In the Search facility, the user has the option of searching by definite dates so that the results exclude all the museums with date ranges attached, or by possible dates, in which case the results include museums where the date range intersects with the specified period. This allows for a much more nuanced analysis.

Looking in more detail at how Search works, opening and closing dates are stored as a pair of years (f,t) in our database, where f and t may be the same year if we know the year of opening/closing for certain.  So, for example, the pair (1965,1969) would be stored for a museum known to have opened between 1965 and 1969; and the pair (2011,2011) would be stored for a museum known to have closed in 2011. Modal Logic operators are supported by our system’s Search facility that allow the user to query whether a particular museum definitely or possibly opened/closed in a given year.  In particular, suppose a given museum ‘m’ is recorded as having opened in year ‘f’ at the earliest and year ‘t’ at the latest.  Suppose a researcher wishes to find out whether museum m opened before, on, or after a specified year ‘d’.  Then the following comparison operators are supported by our system to allow the researcher to determine whether this is definitely the case:

Comparison operator Implementation logic
(f,t) = d  DEFINITELY ON A SPECIFIC YEAR f = d  and t = d
(f,t) < d  DEFINITELY BEFORE t < d
(f,t) <= d DEFINITELY BEFORE OR INCLUDING t <= d
(f,t) > d  DEFINITELY AFTER f > d
(f,t) >= d DEFINITELY AFTER OR INCLUDING f >= d
(f,t) != d DEFINITELY APART FROM t < d OR f > d

 

And the following comparison operators are supported to allow the researcher to determine whether this is possibly the case:

 

Comparison operator Implementation logic
(f,t) = d POSSIBLY ON A SPECIFIC YEAR f <= d AND d <= t
(f,t) < d  POSSIBLY BEFORE f < d
(f,t) <= d POSSIBLY BEFORE OR INCLUDING f <= d
(f,t) > d POSSIBLY AFTER t > d
(f,t) >= d POSSIBLY AFTER OR INCLUDING t >= d
(f,t) != d POSSIBLY APART FROM not (f=d and t=d)

 

The same comparison operators are available for interrogating closing dates.

We employed a further strategy for visitor numbers, which is the least complete category and has discontinuities that make it difficult to compare like with like.  Our primary objective was to use visitor number data to provide an indication of the size of the museum and, given the patchiness of the information, we decided to have a category of Unknown and also to gross numbers into size categories of Large, Medium and Small, where large and small also have sub-categories. This approach enabled us to include data from the Association of Independent Museums and Arts Council England who generally provide visitor number ranges rather than precise figures, and to use predicative analysis to establish broad size ranges. It also allowed us to circumvent some of the methodological problems of having figures collected by different means and from across the decades. Users can browse or search according to these size categories, and in addition, they can search according to precise date-stamped visitor numbers where available.

In conclusion, in the Mapping Museums project we have managed data patchiness in a variety of ways: designing a flexible knowledge base that can be modified and added to as required; representing absence rather than ignoring unknown information; using date ranges and providing users with the option of searching by definite or possible dates; and apportioning the probability of an opening/closing event occurrence over the estimated time interval for statistical analysis. Rather than implying that all visitor numbers data are of equal reliability, we created size categories for a large number of museums, and provided the means to search the definite but incomplete data that was available.

Fiona Candlin, Alex Poulovassilis
September 2018

Categories
Research Process

Historic Buildings: In or Out of the Surveys?

Ballindalloch Castle
Ballindalloch Castle

In the process of compiling a list of all museums in the UK from 1960 until 2020, the Mapping Museums team have collected data from previous surveys and sources. As I discussed in previous blog posts, we found that surveys had excluded art galleries without collections, and to a large extent, unaccredited museums. Historic buildings are different in that their inclusion is uneven. They are listed in some circumstances, and not in others.

When the Standing Commission for Museum reviewed the sector in 1963 they included historic buildings that were managed by the Ministry of Public Buildings and Works (later English Heritage), but they only listed National Trust buildings if they contained a stand-alone museum. By contrast, when the Museums Association launched the massive Museums.UK Database in 1987, they included historic buildings and stated that the content of the building, and arguably the building itself, could be considered a collection: a venue did not need to have a stand-alone exhibition in order to qualify as a museum. The Museums Libraries Archives Council later reinforced this position when they clarified the 1998 definition of a museum by noting that ‘a collection is an organised assemblage of selected material evidence of human activity or the natural environment, accompanied by associated information. As well as objects … held within a museum building, a collection may include buildings or sites’.

From the early 1980s onwards there has been a broad consensus that historic buildings can be counted as museums. The difficulties or the unevenness around their inclusion in museum surveys and lists has two sources. The first is that historic buildings drop out of the data for the same reasons as small independent museums, which is that they do not always comply with the standards outlined by the accreditation scheme (I discussed this point in my previous post). Some of these venues might be owned or managed by major organisations including the National Trust, others and particularly stately homes, might be owned by individuals or families and thus do not meet the requirement that museums must be held in trust.

The second and more complex reason for the uneven data on historic buildings concerns the different organisations involved in their preservation, and the variety of process. Each of the four countries has a national development agency for museums, or in England, for the arts, this also having responsibility for museums. In addition, each nation has organisations that oversee the preservation of national heritage or environment or buildings: English Heritage Trust, Historic Environment Scotland, Cadw in Wales, and Historic Environment Division in Wales.

The four sister organisations have relatively similar holdings in that they look after a mixture of stately and historic homes and buildings, monuments, and sites. Crucially, however, each of these organisations takes a different approach to accreditation, which has an impact on which venues appear on lists of museums and thus on which sites are recognised as museums. In consequence, English Heritage runs thirty ‘museums’, Historic Environment Scotland seemingly operates three, while Cadw and the Historic Environment Division in Northern Ireland have only one museum apiece and in both cases that remains unofficial in that it does not appear in the records kept by that nation’s museums council. In each case, the organisational structures and histories of the departments responsible for historic buildings and environment result in different outcomes as to whether a historic building constitutes a museum.

There is no particular merit in a building being considered a museum or a heritage site. The issue is not whether something ought to be counted as a museum or a historic building. Rather it is the effect of that designation on how the museum sector is depicted. The different attribution potentially produces a skewed picture of which nations have more or less museums of a particular kind, and it potentially underplays the number of historic buildings that function as museums.

Again, this situation leaves the Mapping Museums research team with questions. Do we include those historic houses and buildings that are owned by the national bodies, although officially they are not designated as museums? Do we include those historic houses that remain in private hands? What do you think?

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Events

Participate in our web application evaluation sessions!

Can you help us to evaluate the Mapping Museums web application?

Mapping Museums is a large-scale research project that is based at Birkbeck, University of London. It aims at documenting at analysing the development of the museum sector between 1960 and 2020. So far, the team has collected a rich range of data on approximately 4000 UK museums, and we have developed a web application that enables users to browse, search, and visualise that information.

The evaluation session is an important means for us to gain feedback about the usefulness of the web application. With your input we can further improve the system before it is made publicly available.

We’d like a wide variety of people to participate in the evaluation trial especially those that are involved in the museum and archive sectors, in academia, or in digital media

The trial session will be based on three activities:

  • A hands-on introduction to the web application.
  • Using the application to undertake a small number of information searches. This will allow us to gauge how easy the system is to use.
  • Group discussion about your experience of using the system and the ways that it could be improved and extended.

The session will take no more than two hours.

There is more information about the project available at http://mappingmuseums.wpengine.com/about/.

Locations and dates:

2 – 4.30pm | October 3rd 2018 | Room 407, Birkbeck Main Building, Malet Street, London WC1E 7HX (the entrance is on Torrington Square)

We would be very grateful for your assistance. If you would like to participate, please email the project director at f.candlin@bbk.ac.uk briefly explaining why you would like to attend. We can cover travel expenses up to £20.

Researchers involved in the trial session

Researchers and contact details:

  1. Professor Fiona Candlin (candlin@bbk.ac.uk)
  2. Professor Alex Poulovassilis (ap@dcs.bbk.ac.uk)
  3. Nick Larsson (nick@dcs.bbk.ac.uk)

How the data will be handled in the study

Information obtained from you through the session will be used to inform the research work of the project and subsequent research publications. All personal data collected from this study including your name and contact details will be kept confidential. No reference will be made in oral or written form that could link any participant to information they have provided to us as part of this the study.

Your participation in this study is voluntary, and you may withdraw from the study at any time.

Categories
Research Process

One Year On: The Principal Investigator’s View

The Mapping Museums project has just reached its first birthday. One year in, and Dr Jamie Larkin, the researcher, has almost completed the data collection. We now have an extremely long list of museums that are or were open in the UK at some point in the last sixty years. My co-investigator Professor Alex Poulovassilis and the Computer Science researcher Nick Larson have made good inroads on designing a database that will allow us search and visualise that information in complex ways. For me, it has been a pleasure to collaborate with other academics rather than to work as a solitary scholar as is usually the case for those working within the arts and humanities, and the process of conducting the research has been both fascinating and demanding. In this post I’m going to outline the three issues that have most preoccupied me over the last twelve months. They concern the definition of museums, their classification, and the structure of the database.

 Challenge No. 1: Defining a museum

One of the central aims of the Mapping Museums project is to analyse the emergence of independent museums in the UK from 1960 until 2020. In order to accomplish this task, we have had to compile the list mentioned above, and to do that we have had to decide what counts as a museum. This has not been straightforward. While the Museum Association and the International Council for Museums both publish definitions of museums, there have been seven different definitions in use during the time period covered by our study. If we were going to use a definition, we would have to decide which one.

More importantly, the use of definitions of museums only became common in the early 1990s and was closely connected to the accreditation process. In consequence, professional definitions of museums are usually aspirational and prescriptive, and they set standards that cannot be matched by many small amateur and community museums. The Mapping Museum project has a strong focus on such grass roots museums, and if we used established definitions, then we would exclude the less professionalised venues from the outset. We needed to find a different way of deciding which venues would be included in our dataset, and thus my first challenge was: how could we identify a museum as such?

Challenge No. 2 Classification

One of our research questions concerns the possible correlations between the date on which a museum opens, its location, and its subject matter. I want to know whether there are historical trends in subject matter: whether museums of rural life tended to open in the 1970s, military museums in the 1980s, and food museums in the twenty-first century. Similarly, I want to consider the relationship between subject matter and place: it’s likely that fishing museums will be located on the coast, but are there other, less obvious, regional differences? Do local history museums cluster in parts of the UK that have been subject to gentrification, or the opposite – are they predominately found in areas of low economic growth? Do transport museums prevail in the West Midlands and personality museums in the East of Scotland? Or are there no noticeable trends?

In order to answer these questions, we need to categorise each museum according to its subject matter. The last time this happened was in the DOMUS survey that ran between 1994 and 1998. They used a relatively traditional classification system that was suitable for documenting conventional public-sector museums, but was much less useful with respect to small independent venues. Many museums, such as those of Witchcraft, Bakelite, Fairground Organs or Romany life, take non-academic subjects as their focus and they do not neatly fit into academic categories. DOMUS did have the category of ‘social history’, but if we used that for all small non-academic museums, it would be so extensive as to be meaningless, and besides, social history is a methodology rather than subject matter. My second challenge, then, has been to write a classification system that could encompass the diverse subject matter of small independent museums alongside that of the more traditional institutions.

Challenge No. 3: Designing a database

While it was undoubtedly a challenge to find criteria for identifying museums and to devise a new system for classifying them, both these tasks related to my areas of expertise, namely museums. The third major challenge was a long way outside of my comfort zone and concerned the database design. This task was utterly anxiety inducing because it is something I’d never done before and, admittedly, never even thought about, and yet, despite my inexperience, I recognised that it is an extremely important part of the project. Although Dr Larkin has been collecting data on museums, and I have been working on definitions and classifications, that labour will be of little use unless we can search and model it in such a way that it produces information. The design of the database has a direct impact on the possibility of my answering the research questions and on the production of knowledge more generally. It has therefore been imperative that I learn to think about and help develop its structure.

How I responded to these three challenges, and worked with other members of the research team to resolve them will be an ongoing theme in this blog and the subject of scholarly publications. Do keep a look out for more posts.

©Fiona Candlin October 2017

Categories
Research Process

Mapping Museums: Why bother?

Readers who have followed our blogs to date may have realised how much work, time, and money is involved in mapping museums across the UK. The team currently comprises of two professors, and two full time researchers, one in computer science and one collecting and analysing data. By the end of its four-year life span, the project will have cost over a million pounds. On a more personal note, I spent well over a year planning the project and writing a proposal and it now dominates a good part of my waking life, all of which begs the question: why bother? Why does this subject merit such personal, economic, and intellectual investment?

There are pragmatic reasons for the research. The lack of data and of historical research means that museum professionals and policy makers do not have a clear idea of when or where the independent museum sector emerged in the UK, or how it has changed. There is no long-term information on patterns of museums opening and closing, or of their subject matter. Museum professionals who have spent their working lives in a particular region, have been involved with the Area Museums Councils, or with a special interest group, may have a good grasp of the museums in their locale or remit, but their knowledge is not always documented or relayed. In consequence, younger staff charged with supporting museums or staff who are responsible for making decisions about funding may not always have a clear overview of the sector. By compiling a dataset of museums, and modelling trends, this project has the potential to inform museum policy and funding at a national level.

There are also historical reasons for mapping museums in the UK. The museums boom of the 1970s and 1980s (or possibly 1990s) is generally considered to be one of the most significant cultural phenomena of the late twentieth century and yet we know very little about it. Commentators of the time generally linked the rising number of museums to the conservative administration led by Margaret Thatcher, to the economic policy of the time, and to consequent de-industrialisation. This led to the wave of new museums being characterised as entrepreneurial, nostalgic, and often as politically reactionary, but there is very little evidence to substantiate those claims. It might be that many of the new museums were dedicated to rural life and were coterminous with the industrialisation of farming, or they may have focused on religion, or writers, or teddy bears. The Mapping Museums research will enable researchers to revisit the museums boom, and potentially to recast the museums of that period.

For me, though, the main point of the project is linked to who established independent museums and to the people still running them. Museums are generally discussed in relation to the national or public sector, while curation and other forms of museum work are understood to be specialised professional roles. And yet, in 1983 the Museums and Libraries Council commented that most of these new, small enterprises had ‘been set up in an initial wave of enthusiasm and volunteer effort’, and my initial research suggested that the vast majority were founded by private individuals, families, businesses, special interest and community groups. It is likely that amateurs drove the expansion of the museum sector. In identifying these venues and in documenting the work of the founders and volunteers, the Mapping Museums project will show how the recent history of museums was a grass-roots endeavour, or as Raphael Samuels put it, ‘the work of a thousand hands’.

©Fiona Candlin September 2017

 

 

Categories
Research Process

Getting Started: Compiling the Data

The Mapping Museums project aims to identify trends in the growth of independent museums from 1960 to 2020. In order to conduct our analysis we need to be able to interrogate longitudinal data for a number of museum variables, including years of opening and closure, size, and status change. At present, no such database exists that would allow us to do so. Ironically, for a sector committed to the preservation of cultural memory, documenting the institutions that participate in these activities is seemingly much less of a priority (see ‘Problems with the Data’ post). Thus, the first objective of the project was to create a functional database that catalogued all of the museums that have existed in the UK since 1960.

Before we began building this database we first considered the logistics of the process, namely the point during our timeframe when it would be best to begin to collect the data. Should we put together a snapshot of the nation’s museums as of 2016 (estimated at 2,500 at the outset of the project) and work backwards, or begin with a baseline of around 900 museums that existed in 1960 and work forwards? The former would give us a solid foundation but might require tortuous weaving back through name changes and amalgamations; the latter would give us fewer museums to start with, but might be easier as we attempted to record individual museum trajectories.

The solution was a compromise based on time and the availability of data. Between 1994 and 1999 the Museums and Galleries Commission ran a programme that produced the Digest of Museum Statistics (DOMUS). It involved annual reporting from museums that participated in the scheme in the form of  lengthy postal surveys. The information captured included address, registration status, visitor numbers and many other characteristics. While some limitations with the data have been highlighted in retrospective analyses (specifically by Sara Selwood in 2001), the baseline data that DOMUS provided was sufficient for our needs.

Using this as a starting point enabled us to begin with detailed information on nearly 2,000 museums. This snapshot of the museum sector in the late 1990s provided us with the flexibility to work both forwards and backwards in time. In particular, having records of museums at an interstitial stage of their development has been helpful in tracking (often frequent) changes of name, status, location and amalgamations.

The major problem with the DOMUS survey was accessing the data and formatting it for our use. After the project was wound up in 1999 the mass of information it had generated was deposited at the National Archives. However, given the complex nature of the data, there was no way of hosting a functional (i.e. searchable) version of the database. Consequently, it was archived as a succession of data sheets – in a way, flat-packed, with instructions as to how the sheets related to one another.

The first task was to reassemble DOMUS from its constituent parts. This meant trying to interpret what the multiple layers of documents deposited in the archive actually referred to. While the archival notes helped, there was still a great deal of deductive work to do.

Once we had identified the datasheet with the greatest number of museums to use as our foundation, the next step was to matchup associated data types held in auxiliary sheets into one single Excel master sheet. To do so we used the internal DOMUS numbers (present within each document) to connect the various data to create single cell data lines for each individual museum. We slowly re-built the dataset in this way.

In some instances the splitting of the data – while presumably logical from an archival perspective – was frustrating from a practical standpoint. A particularly exasperating example was that museum addresses were stored in a separate sheet from their museum, and had to be reconnected using a unique numerical reference termed ADDRID. While the process was relatively straight-forward, there was always a degree of anxiety concerning the integrity of the data during the transfers, and so regular quality checks were carried out during the work.

The next step was to clean-up the reassembled sheet. Firstly, we removed anything from the data that was not a single museum (e.g. references to overarching bodies such as Science Museum Group). Second, we reviewed the amassed data columns to assess their usefulness and determine what could be cut and what should be retained. Thus, old data codes, fax numbers and company numbers were deleted, while any information that could potentially be of use, like membership of Area Museum Councils, was retained. We also ensured that the column headings, written in concise programming terminology, reverted back to more intelligible wording.

This formatting helped shape the data into a usable form, but the final step was to put our own mark on it. Thus we devised specific project codes for the museums, which was useful for recording the source of the data and managing it effectively moving forwards. To tag the museums we decided on a formula that indicated the project name, the original data source, and the museum’s number in that data source (e.g. mm.DOMUS.001). Once our database is finalised, each entry will be ascribed a unique, standardised survey code.

Ultimately, the DOMUS data has acted as the bedrock of our database. It provided a starting point of 1848 museums and thus the majority of our entries have their basis as DOMUS records (which have been updated where applicable). One of our initial achievements is that the DOMUS data is now re-usable in some form, and this may be an output of the project at a later date.

A wider lesson from this process is the importance not only of collecting data, but ensuring that it is documented in a way that allows researchers to easily access it in the future. When our data comes to be archived in the course of time, the detailed notes that we have kept about this process – of which this blog will form a part – aim to provide a useful guide so that our methods and outputs can be clearly understood. Hopefully this will allow the history of the sector that we are helping build to be used, revisited, and revised for years to come.

© Jamie Larkin June 2017