Categories
Research Process

A week in the life

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

Categories
Research Process

How big is that museum?

What is a small museum? Or for that matter a medium or large museum? In the museum sector, size is generally measured in relation to visitor numbers, and in cases where several criteria are used, such as income or staff numbers, they are still taken into account. The Mapping Museums research team has followed suit in this respect. We decided to group the museums within our dataset into size categories that are based on visitor numbers. Thus the question for us was: how should we establish the thresholds for these categories? How many visitors equate to small, medium, and large? And should we just use those three categories? What about very tiny or really massive museums?

Arts organisations define size in slightly different ways, and in some cases, single organisations may use a variety of measures. For example, the Association of Independent Museums (AIM) uses the following categories in their ‘toolkit’:

  • Small = visitor numbers of up to 10,000
  • Medium = visitor numbers of 10,001 to 50,000
  • Large = visitor numbers of 50,001+

However, AIM uses slightly different bands when museums are applying for membership. In this case, the smallest category is defined as being up to 20,000, not 10,000, and there is an additional category of ‘largest museums’, which attract over 100,000 visitors. Arts Council England (ACE) data uses the same measures as the AIM toolkit, but only in relation to independent museums. When they assess the size of local authority museums, they use a different yardstick:

  • Band One: 30,000 visitors
  • Band Two 30-100,000 visitors
  • Band Three 100, 000+ visitors

These differences are sensitive to the realities of museum practice. By categorising museums that have less than 20,000 visitors as being small for the purposes of membership, AIM enables more organisations to pay the lower rates of subscription than if it had put the bar at 10,000 visitors. Similarly, ACE recognise that local authority and independent museums operate under different conditions. For the Mapping Museums team, however, the use of different size bands was problematic because it would be difficult to know how to categorise museums that have hybrid forms of governance, for instance, when local authorities retain ownership of museum buildings and collections but outsource its management. Using different size categories according to governance, also meant we would have to change size designations when museums changed status, and it also prevented any direct comparisons across categories of governance.

In the absence of an established rubric for museum size, we needed to decide what size bands to use in the Mapping Museums research. In order to first make that decision we looked to the data and at the overall spread of museums according to visitor numbers.

Figure 1- Distribution of Museum Yearly Visits
Figure 1

At this point, we had no visitor numbers for 45% of the museums in our dataset. However, when we plotted the information that was available to us (Figure 1), we could see that there was a clear peak in the data between 10,000 and 32,000 visits per year (with median about 13,000), but that there were no obvious points where the distribution of museums divided into bands. Thus the data did not suggest any clear categories for allocating size.

We then divided the distribution into quartiles, which showed that 50% of museums had between about 4,000 and 40,000 visitors per year (Figure 2).

Figure 2 - Distribution of Museum Yearly Visits with Quartiles
Figure 2

One option was to create a band that covered the broad group of museums that gain between 4,000 and 40,000 visitors. The problem was that approach would elide the significant differences in scale. A museum that gains 4,000 visitors per year is likely to be run solely by volunteers or by a private individual, with limited opening, and to operate in a relatively ad-hoc fashion. A museum that attracts 40,000 visitors is reasonably well established and likely to have a professional orientation. Thus, grouping these museums did not make sense in an analytic context.

First categorisation

Our next step was to consider how various size categories would support our research. One of the problems of using three bands for sizing is that it lacks nuance. A museum that has 100,000 visitors is clearly very popular and well established but it is not in the same league as one that has visitors in the millions, yet both would normally be classified as ‘large’. Thus we initially decided to introduce more categories (see Table 1).

 

 

Size category

Yearly visitor number range Number of museums (%)
Tiny 0-1,000 4.1
Very small 1,001-5,000 10.8
Small 5,001- 20,000 16.6
Medium 20,001 – 50,000 11.2
Large 50,001 – 100,000 5.6
Very Large 100,001 – 1million 6.1
Huge 1 million + 0.3
Unknown 45.3

Table 1

This approach initially seemed to work. However, when we began detailed analysis of the data the researchers found that they were constantly aggregating the three smallest categories. We did not need that degree of nuance for our work. However, we regularly used the category of ‘huge’ as a way of filtering out the very largest of institutions. Thus we decided to revert to a single category for small museums but to keep ‘huge’.

Second (and final) categorisation

Our second set of categories, which we are now using, reads: small, medium, large, and huge. Yet the question remained of where the thresholds would be set for each category. Again we turned to the data, and looked at how the distribution of museums would change if we used 10,000 or 20,000 visitors as the top limit for the category of small, and what difference it would make if 50,000 or 100,000 were used as the upper limit for ‘medium’.

  Museum counts
Category thresholds small medium large huge
0; 20k; 100k; 1m

 

1318 677 250 13
0; 10k; 100k; 1m 930 1065 250 13
0; 10k; 50k; 1m 930 839 476 13

Table 2

As Table 2 shows, splitting small and medium at 20,000 means that the former category is significantly larger than the later. Splitting the categories at 10,000 produces a more balanced distribution between the two. In both these figures the category of large has relatively few museums because it only includes organisations with over 100,000 visitors. When that threshold is dropped to 50,000, then the size of that category almost doubles.

Importantly, the different size categorisations give a very different impression of the UK museum sector. If small museums predominate then we might assume that the sector is dominated by museums that attract few visitors, are volunteer-run museums or have few paid staff, and that possibly are struggling to survive. In contrast, if there are larger numbers of museums of a medium size, then the sector seems to be more comfortably established, and, if there are high numbers of large museums, then onlookers may conclude that it is flourishing. Thus size categorisations can have a strong impact on perceptions of the sector, even if the actual visitor numbers and lived realities of museum practice remain the same throughout.

After considerable discussion the Mapping Museums team decided to set the size categories as follows (Table 3):

Size category Yearly visitor number range Number of museums (%)
Small 0–10,000 22.5
Medium 10,001–50,000 20.3
Large 50,001–1 million 11.5
Huge 1 million+ 0.3
Unknown 45.3

Table 3

For us, these categories chimed reasonably closely with norms of thinking about museum size, and are similar to those used by the AIM toolkit, which has the advantage of making them familiar within the sector. They lack nuance in the category of ‘large’, but this is not a particular issue for our research, as the focus of Mapping Museums is on smaller museums. Setting the bar at 10,000 also means that small museums do not merge into medium-sized, more established organisations, and we can examine them as a distinct group. For us, this is important because the smallest museums are often sidelined both in research and in professional discussions.

Copyright: Fiona Candlin and Andrea Ballatore, 2018

Image via S. Faric on Flickr

Categories
Research Process

Missing, massaged, and just wrong: Problems with visitor numbers

Visitor numbers provide some sense of the scale of a museum’s operations. If a museum has a large collection of priceless artefacts, occupies an impressive building, has professional curators and conservators, a nice café, and offers activities to its audiences, then it is unlikely to attract a mere 2,000 visitors per year. Conversely, if a museum is housed in a defunct railway station, with one retired locomotive on exhibition, and is staffed entirely by volunteers, then it would be surprising to discover that it gained millions of visitors. There is a link between a museum’s provision, and its visitor numbers. Thus by listing visitor numbers for the museums in our dataset, the Mapping Museums team intended to provide researchers with some guide as to the organisations’ size and character. However, this process was not as straightforward as it initially seemed.

One problem is that visitor numbers are not always available. Figures for larger institutions are reported in the national monitor Visit Britain. Information on attendance at accredited museums is published by Arts Council England, and the Museums Association usually includes visitor numbers on the Find-A-Museum service listings. Obviously, museums that are not accredited or are not members of the Museums Association do not appear on those sites. Unaccredited, unaffiliated museums may sometimes note their visitor numbers on their own website or annual report, but more often, that information cannot easily be found. Moreover, visitor numbers may not exist as such. Collecting that information requires staff capacity and resources that are beyond the reach of some organisations, and while the lack of documentation or the complete absence of data may indicate low visitor numbers, that correlation cannot be guaranteed.

Problems with visitor numbers are not confined to a lack of information. Even when visitor numbers do exist, they cannot be relied upon. One issue is that there is no accepted methodology for how visitor numbers are collected, and institutions each decide how to accomplish this task. In some instances, museums log everyone who comes through the door. However, if the museum or gallery has conveniently placed toilets, as was the case at Middlesbrough Museum of Art, then people coming to use the facilities raise the footfall. Cafes can similarly boost the total visitor count. Other museums only record the number of visitors who enter into a gallery or look at artwork, although those criteria can be met by putting artwork or displays into the foyer of a museum. It is also unclear whether people who participate in outreach or other activities are included in total numbers. We found one very small museum that reported 42,000 visitors because they organise an annual rally and included all the attendees. Who is doing the counting and how they count has a significant impact on the recorded visitor numbers.

Methodology aside, visitor numbers are sometimes actively massaged. Adrian Babbidge commented in a recent article for Cultural Trends, there are strategic reasons for inflating them and the Mapping Museums team found instances where disparate numbers had been reported. For instance, one museum stated that it had less than 20,000 visitors a year on its AIM membership forms yet claimed 30,000 visitors per year on a fund raising website. If its actual numbers were closer to 30,000 then by tweaking figures, the institution saved a little on membership fees, and if the lower number was more accurate, the upwardly adjusted figure might have improved their chances of raising money. The Mapping Museums team has also encountered cases where visitor numbers were purposefully deflated. At least one small museum had under-reported ticket sales to avoid paying tax on that income. This had the consequence of them appearing to have lower visitor numbers than is in fact the case.

Another set of difficulties obtain when dealing with historic visitor numbers. As we’ve noted before, the Mapping Museums team is documenting UK museums from 1960 until the present day. Where available we have recorded visitor numbers that pertain to that period, and most notably, we have included figures from the massive DOMUS survey that was run between 1994 and 1998. This has the advantage of providing size indicators for museums that have now closed but we have discovered that some of the DOMUS records are anomalous. For example, The Royal Electrical and Mechanical Engineers museum is listed as having the following audiences in successive years.

4,500 in 1994

20,000 in 1995

35,000 in 1996

5,000 in 1997

According to these figures, the number of visits increased eightfold in a two year period, and then reverted to its original numbers. This seemed unlikely so we contacted the museum. The director, Major Rick Henderson, told us that the museum had never attracted such high visitor numbers. Even now, with a dedicated staff and a new building, attendances are in the region of 20,000. It is therefore likely that the inflated figures are due to errors made when the data was entered into the DOMUS system. The problem is that we cannot check all the anomalies, partly because of time but mainly because many of the museums have since closed and the institutional memory lost.

Thus, there are several challenges to using visitor numbers to give a sense of the scale of a museum: it is difficult to find figures for unaccredited museums or they may never have been collected; there is no established methodology for collecting visitor numbers; museums massage audience numbers for strategic purposes; and historic records may be incorrect.

The Mapping Museums team decided to deal with these various issues by using categories for size rather than visitor numbers. Providing precise numbers may give the false impression that the figures all adhere to the same measure and can be compared, whereas categories provider a looser guide to a museums operations. Unfortunately, using size categories also has its complications, which I will outline in the next blog.

Copyright Fiona Candlin 2018

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

Building the Database

The Mapping Museums project is an interdisciplinary one between Arts and Computer Science and as such a challenge in many ways as discussed in the earlier blog on “Interdisciplinarity“. The project is being run using an iterative and collaborative methodology, as the data collection often leads to new knowledge that needs to be modelled and retained. This incremental accumulation of data and knowledge means that flexibility is important so as to be able to respond to frequent changes.

We, therefore, use a Semantic Database to store and describe our data: semantic databases are also known as Triple Stores and they store pieces of information in triplets of the form Subject-Predicate-Object. For example, the fact that the Science Museum is located in London would be stored as the triplet Science Museum-hasLocation-London. The data model that describes entities (such as museums and locations) and the relationships between them (such as hasLocation) is sometimes called an Ontology.

This kind of data model can easily be extended with new triplets as new data and knowledge accrue. It can also easily be integrated with other already existing ontologies, for example relating to geographical regions and types of museums. Equally important, it allows us to describe in fine detail the different relationships between entities.

In our project, the data is first recorded within Excel spreadsheets. It is then converted into a triplets format to load into our database.  We encode the metadata, e.g. the data types and relationships, directly within the spreadsheets as additional header rows, so as to keep the model and the data “in sync”.

In more detail, the processing of the Excel spreadsheets comprises several steps:

  1. The spreadsheet is converted into a CSV (comma separated values) file.
  2. The metadata is converted into a graph, defined in the Graffoo language.
  3. This graph is processed into a number of templates, to be used for converting the data into RDF (Resource Description Framework) and RDFS (RDF Schema).
  4. These templates are used to convert each row of the CSV file into a set of triplets to be loaded into the database (which is stored using Virtuoso).

Once the database has been created, we use it to support a web-based user interface allowing users to explore the data:

 

By using semantic technologies to describe and store the data, we can support a flexible user interface that will allow users to explore spatial and temporal relationships in the data in order to begin to answer the research questions around independent museum development in the UK.

© Nick Larsson, August 2017

Categories
Events

AIM! I’m going to map forever…

Last week the Mapping Museums team attended the Association of Independent Museums (AIM) annual conference hosted at Chatham Historic Dockyard. This year marks the 40th anniversary of the foundation of AIM, which itself gives a good indication as to the moment when the growth of independent museums began to gather pace. As our project is working to map historical trends within the independent museums sector, the conference gave us the perfect opportunity to talk to colleagues with a long and deep involvement with independent museums and to meet those who had recently joined the organisation.

More specifically, we attended the conference for two reasons. The first was to create greater awareness of our project, which we hoped would help forge connections among both professionals and those responsible for running individual sites. The second was more prosaic; we aimed to actively gather data from delegates over the course of the two-day event and put more museums on the map!

Publicising the project

The main method of communicating Mapping Museums was a lecture as part of a session on partnerships between universities and museums. The project’s Principal Investigator, Professor Fiona Candlin, provided an overview of our project, emphasising that the museums sector currently lacks comprehensive data, and that our research would chart the growth of independent museums in relation to a host of cultural, political and economic factors.

Professor Fiona Candlin addressing attendees of the AIM conference

 

The lecture was well attended and this exposure led to both  conversations with sector staff who approached the project team later in the day and a significant increase in activity on our Twitter feed (@museumsmapping). These interactions were helpful for a few reasons. On the one hand we were able to discuss forms of practical help for the project and establish new contacts. But for the most part it was reassuring to exchange stories about the difficulties we face with issues like defining museums and knowing that these are shared problems (and frustrations!) across the sector.

It was also useful to talk to subject specialists about issues particular to their museums. Chatting to a delegate responsible for historic windmills about whether they should be counted as museums, she offered her insight that they should so long as their primary operating revenue came from visitors, rather than auxiliary uses such as producing artisanal flour. Meanwhile, delegates from a historic ship talked to us about whether it should be referred to as a museum or as a visitor attraction, and the difficulties of mapping some vessels that could be moored in different locations.

A highlight of the conference was the opportunity to meet Rob Shorland-Ball, a long-time AIM member and museum consultant who was responsible for depositing the AIM archive at the University of Leicester. By doing so he has been instrumental in helping us to record around 200 (often closed) museums that we have found looking through this material, and which we may not have located otherwise. It was great to inform him about the project and thank him for his efforts. Such interactions, particularly with historical data collection, have helped to humanise the research.

 

Delegates helping with the data collection

In terms of the practical matter of collecting data at the conference, we did this by manning a stall in the exhibition hall. Here delegates could come and talk to the project team, check to see if their museum was in our database and add (or amend) their entry if not. In particular we were eager for delegates to tell us if museums were open or closed, and to give us an idea of their subject matter. The benefit of this was that experts – people ‘on the ground’ working at these museums– could corroborate, and add to, our data.

To make the process as easy as possible we created A3 paper catalogues of our database with entries listed in alphabetical order. This meant that delegates could easily browse entries and had enough space to make additions We also had our computer database on hand in case of any problems in finding museums (for example, if the Barnstaple Museum was recorded as the Museum of Barnstaple).

AIM delegates helping with our data collection

 

In addition to this, we also had on show a prototype of our computer mapping model, demonstrated by Nick Larsson, the project’s computer science researcher. The benefit of bringing the model (and we needed to a substantially reconfigure a laptop to do so!) was that visitors could experience the whole of the research process; once they had checked their entry they were able to see how the data would be visualised and its functionality, and thus think about how they could use such a resource once it is finalised.

The vast majority of the delegates that we spoke to were very enthusiastic about the project and some returned to the stall with their friends to encourage them to participate. As a result, delegates made additions to data over 60 entries and offered suggestions of museums were hadn’t heard of. As a result, we are now aware of the John Lewis Heritage Centre, the Christchurch Tricycle Museum (1984-1999), and the Wigston Folk Museum (1981-1990)! We were also given names of regional experts and offers of help to map museums at a local level. Indeed, despite the cutting-edge technological aspect of the project, our ability to collect (often obscure) information is still largely reliant on traditional forms of networked knowledge; an old fashioned form of crowdsourcing.

New data!

 

Overall, the conference was a success on a number of fronts. Our project is much more visible as a result and we have a trove of data and helpful regional contacts. Beyond these tangible outcomes, the most encouraging aspect of the exercise was to be realise that we are working as part of a sector of professionals who have a great deal of enthusiasm for a project detailing museum history, and who are willing to do as much as they can to help add to this knowledge.

 

© Jamie Larkin          June 2017