Tag: data

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Research Process

Picking the Brains of the Museum Development Network

There is a limit to how much information can be unearthed online or from an archive. Over the last year, the Mapping Museums research team has compiled a mammoth list of museums that were open in the UK between 1960 and 2020. We have used various sources to cross check their details, but there are some particulars that can be hard to find or verify. And so, we asked the Museum Development Network for their assistance.

The Museum Development Network consists of twelve groups, one apiece in Northern Ireland, Wales, and Scotland, and one in each of the nine regions of England. Although the groups all function slightly differently, they all support accredited museums, advise on the accreditation process, and provide relevant information to Arts Council England and other national organisations. They also allocate their own grants, run projects, and help improve services and their members’ skills. In doing so, the museum development officers quickly acquire a fine-grained knowledge of their local museums. We wanted to refine our data by tapping their expertise.

With the support of Claire Browne, the network chair, we arranged to visit staff in each country or region. On each occasion, we arrived with a list of the museums of that area and slowly worked our way through the data, line by line. We had asked the museum development officers to look out for any information that we may have missed and they pointed to a number of instances where the local authority had transferred responsibility for a museum to an independent trust. They also noticed some duplicate entries that had resulted when a museum’s name had been changed, and spotted instances when museums had moved premises, amalgamated with neighbouring venues, or had recently closed. We deleted or edited the entries as appropriate.

The Museum Development Network helped us fine-tune our data and they also contributed to our research by helping us classify museums according to their subject. In most cases, the main topic of a museum is fairly obvious: as one might expect, the Lapworth Museum of Geology concentrates on rocks of varying types, while the Bakelite Museum has a collections of plastic, but the theme of a museum is not always so self-evident. For example, Carnforth Station provided the set for Brief Encounter, and its Heritage Centre focuses on the film, not on railways or trains, while the Deaf Museum and Archive in Warrington is more concerned with the community than with health or medicine. Being familiar with these venues, the museum development officers could make a nuanced judgement as to their overarching subject matter, whereas the research team would have to spend a considerable length of time checking webpages, catalogues, and other sources to make a judgement. Their input saved us weeks of work. It was also good to establish that our new classification system worked smoothly, although the absence of a ‘social history’ category did cause some consternation. For us, the problem with ‘social history’ is that it applies to such a large number of venues that it lacks nuance. In the DOMUS survey, conducted in the 1990s, almost a third of museums were listed under this category, which makes it almost unusable for research purposes.

Holding the meetings served to further refine our data, and it also had benefits for the museum development network. Many of the officers said that they rarely got an opportunity to discuss the museums in their region, and that it was useful to do so. Others thought that going through the list was akin to a quiz on their museums, and had been fun. Almost everyone commented that the Mapping Museums team had identified numerous museums that they had never encountered, and that our data would inform their work, particularly with respect to unaccredited museums.

Ultimately, the experience was incredibly productive. It was a pleasure to meet such a dedicated and knowledgeable group of people. We are very much looking forward to the point when we can provide them, and others, with the completed data.

© Fiona Candlin October 2017

 

 

 

 

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

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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

 

 

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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
Research Process

Interdisciplinarity

When Fiona Candlin and I first met up in 2015 to discuss the possibility of a research project that would create a database and visualisations relating to the UK’s independent museums sector, I was immediately intrigued. I knew from my previous experiences working on interdisciplinary projects to build specialist knowledge bases that this would be a challenging endeavour – and so far the Mapping Museums project has not disappointed!

The challenges faced in these kinds of interdisciplinary research projects are numerous:

  • the research programme cannot be tackled through expertise and methodologies arising from one discipline, but require multi-, cross- and interdisciplinary approaches;
  • gradual development of a common language of discourse is needed between researchers from the different disciplines: often a term has different meanings in different disciplines, e.g. words such as “design”, “Implementation”, “testing”, “ontology”;
  • from the point of view of the computer scientist, there is typically a lack of well-defined “requirements” at the outset of the research project; identifying a commonly agreed initial set of requirements is a necessary first step, on the basis of which we can then begin to research and design initial prototype software;
  • the production of initial prototypes typically leads to the elicitation of additional and more precise requirements, which often contradict the initial requirements!
  • because the very nature of research is open-ended and non-predictable, the research project progresses in this iterative and collaborative way, comprising successive cycles of
    • requirements elicitation
    • research
    • design
    • implementation
    • trialling

All stages involve the whole project team, as well as possibly additional domain experts and stakeholders.

In the case of the Mapping Museums project, it was evident from the outset that the gradual collection of diverse data and the gradual development of understanding about the required functionality of the database and visualisations would require this kind of iterative and “agile” methodology to be adopted by the research team.

This also pointed to the need to adopt “semantic” technologies in order to develop the database and visualisations, which are better suited to incremental data gathering and knowledge creation than more traditional relational database approaches.

Developing graphical conceptual models of the museums data from the outset of the project has also allowed us to develop a common understanding of the information that the database will contain:

 

The first 9 months of the project have resulted in a first version of the database, and in the conversion of our conceptual models into a formal ontology. We have also started to experiment with some initial data visualisations:

 

© Alexandra Poulovassilis

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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

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

Categories
Research Process

Problems with the Data

When I first began this research I had assumed that there was very little data on the museums that were founded in the late twentieth century. In fact, the contrary is true. A great deal of information has been collected about museums from the 1960s onwards. By our reckoning there were at least nine major cross-UK surveys, three that concentrated on Wales, two on Ireland, and one apiece on museums in Scotland and England. Dozens of smaller surveys concentrated on specific regions or aspects of the sector, Arts Council England keeps lists of museums across the UK, and the Museums Association runs a Find-A-Museum service. Why then, is it so hard to get information about the rising numbers of museums? In this blog I identify five reasons why this massive amount of data has not translated into information.

 

  1. Lost data

The first major survey that falls within our time period is the Standing Commission Report on Provincial Museums of 1963. Very conveniently, it is available in print form from major public libraries, and contains both the final report of the committee and a complete list of museums sorted according to region. From there onwards, however, the raw data largely disappears from government publications on the subject. The 1973 Standing Commission report, for instance, enumerates the different types of museums and provides an overview of emerging trends, but it does not provide a list of museums or the information that relates to individual venues, which we need if we are to track the emergence and development of the sector. As far as we know, the original data that was collected for that survey and for subsequent surveys conducted by the Standing Commission has been lost.

Research on museums conducted by other organisations is similarly missing. In 1983, the Association of Independent Museums (AIM) undertook a large-scale survey of that sector, but they do not seem to have published a report, and initially we were only aware of the survey’s existence from a handful of references in other contemporaneous sources. After some time, however, we found photocopies of the typewritten lists and the original survey questionnaires lodged in a university archive. We have been less successful in finding the information associated with the ‘UK Data base project’ of 1987. The Museums Association, which ran the survey, has a small in-house archive and its historic materials are kept at University College London. Unfortunately, neither archive contains any of the relevant materials. No-body in either organisation has any recollection of the survey taking place or any idea of where the material went, although it is likely that the UK Data base materials were thrown out when the Museums Association moved offices earlier this century.

  1. Indecipherable data

A second problem for researchers interested in the museums of the late twentieth century is whether the data is usable or not. One of the most important surveys of UK museums was the

Digest of Museum Statistics, otherwise known as DOMUS. From 1994 to 1998, it collected information on all accredited museums, some 1700, and generated a huge amount of material. When the project was closed, most of the paperwork was deposited in the National Archives and so researchers can easily find and download documents pertaining to the surveys. The problem is, the material is difficult to decipher. As well as some documentary material, the archive consists of three folders, each containing around fifty files that contain the raw data from DOMUS. All of the folders and files have coded names. The files comprise of spreadsheets with numerous columns that have similarly opaque headings. Some sheets are virtually empty, while others contain over two thousand entries. There is no explanation as to how these tables relate to each other, what the files or columns refer to, or how the user is supposed to decipher them. Using this data requires someone to unlock the coding system and re-constitute the original database.

  1. Data that is not easily accessible

The Museums Association currently compiles the most extensive dataset relating to UK museums. Their Find-A-Museum service lists information on the whereabouts, visitor numbers, staff, subject matter and governance of around two thousand museums. However, the Museums Association is a commercial enterprise and accessing this data incurs a fee. Users must pay to join the association and cover the subscription charges for the Find A Museum Service, at a minimum cost of around £186 for an individual or £450 for an organisation. In addition, the data cannot be downloaded or manipulated, and can only be examined via the service’s own rather limited search engine. Find-A-Museum is intended for museum professionals who want to look up information on specific venues, and is not designed with researchers in mind, but it does mean that one of the most substantial data sources on museums to be available in the UK cannot be used for broader analysis.

Other lists and surveys of museums are compiled by the various government bodies that oversee museums, namely, the Arts Council England (ACE), the Northern Ireland Museums Council (NIMC), Museums Galleries Scotland (MGS), and the Welsh Museums, Archives, Libraries Division (MALD). These organisations do not publish their data although they do make it available on request and without charge. In our experience, the four government bodies have been very helpful in the provision of data but we are aware that they do not have staff whose role it is to deal with such requests and there is no automatic or established mechanism by which data can be obtained.

 

  1. Incompatible data formats

Having acquired data from the various surveys and lists conducted by ACE, NIMC, MGS, and MALD, researchers will find that each of the government bodies collects different data, with significant variations in the level of detail, and about slightly different kinds of organisations over a range of dates. The spreadsheets cannot be simply merged. Moreover, if researchers want to include historic data, as we do, this all has to be transcribed by hand.

 

  1. Partial and missing data

On collating the available information, researchers might spot a further problem, which is that surveys and lists compiled by government bodies invariably concentrate on accredited museums. The accreditation scheme, which is co-ordinated by Arts Council England, establishes that a museum has achieved professional standards, but small independent museums often lack the staff or the know-how to apply for accreditation, or may not meet the criteria set by the scheme. Museums Galleries Scotland and the Museums Archives Libraries Division in Wales invite non-accredited museums to make themselves known and they do list such venues in their reports and on websites, although submitting data still requires a certain degree of professional capacity or interest. Thus as far as the official reports are concerned, small independent museums are routinely omitted. Indeed, the only survey to have actively sought information on such venues was the one conducted by AIM in 1982/3, and never published.

There are also other omissions. Properties owned by the National Trust register in some surveys and not in others, while few surveys include art galleries that do not have permanent collections, so established venues such as the Baltic in Newcastle or the Whitechapel in London rarely appear in the data.

Finally, information that is essential to a historically minded researcher is less relevant to museum professionals who focus on the current environment. Only one of the surveys registered the foundation dates of museums, and none have listed closure. Once the doors of a museum have been shut to the public, that venue ceases to appear in surveys.

 

In summary, then: Over the last five decades several associations and government departments have collected an enormous amount of information about UK museums. There is no lack of data. There are problems with archiving, making that information comprehensible and accessible, with sharing data across national and organisational borders, with collecting historical data and information on venues that do not reach professional standards or that do not quite fit an orthodox model of museums, and on compiling data. These factors help explain why researchers cannot elucidate recent developments within the museum sector and specifically the emergence of independent museums. There is a wider question, however, about why arts organisations seem to have been so poor at keeping, managing and sharing data. Thoughts on that matter would be welcome, as indeed would any clues as to the whereabouts of the data collected for the 1987 UK Museums database project and for Standing Commission surveys other than the 1963 publication.

©Fiona Candlin May 2017

Categories
Research Process

Not Knowing About Museums

There is a lot that we don’t know about museums. In an age when it is possible to download an institution’s annual reports and follow their exhibitions and events via social media, it seems unlikely that academics, museum professionals, and the museum-going public would be so uninformed about the recent history, characteristics, and scope of the sector. That situation seems doubly unlikely if we note the growth of audit culture, in which public venues are required to account for themselves to taxpayers and policy-makers, and trebly so when we consider the vast scholarship on museums. And yet it is the case. This situation is not limited to museums in developing nations. It equally applies to America and Western Europe. Granted, these areas contain tens of thousands of museums, but even at the smaller scale of the UK, which is the focus of this research, our overview of museums is remarkably sketchy.

We do know that the number of museums boomed in the late twentieth century. In 1960, the Standing Commission for Museums and Galleries took a census and listed some 896 museums. When the same commission took a census a decade later they reported that there were over 1,000 museums and, sounding somewhat anxious, they commented that there were no controls on their formation. No such restrictions were introduced and the number of museums continued to rise. The 1978 Standing Commission described the ‘sheer proliferation’ and ‘bewildering fecundity’ of the sector and, in the mid-1980s commentators started declaring that museums were opening at the rate of one a fortnight, and then one a week, or even three a week. By 1986 government reports placed the number of museums between 2000 and 2,300, although one survey thought it might be as high as 3,500. The official total subsequently inched to around 2,500 and, according to the Museums Association, that figure has remained more or less stable. At the very least, the number of museums has increased by around 180%.

We also know that the vast majority of those museums were independent, in that they did not receive direct funding from the state. In 1960 when the Standing Commission conducted its survey, around 300 museums were independently managed, about one third of the total, whereas in 1986 that statistic was more or less reversed, with around 1800 of a total 2,300 museums being independent, the remainder being national, local authority, university or regimental museums. This made them the single biggest type of museum within the sector.

The boom in the number of museums was and is recognized as a cultural phenomenon, but beyond the rising numbers and the fact that the majority of the museums were independent organisations, we have very little information about it. We don’t know exactly where the new museums opened within the UK, or when, or what subject matter they covered. Nor do we know if these new museums survived, or when they closed, or how many new museums opened: There may be around 2,500 museums but it is entirely possible that hundreds have closed and hundreds have opened in the past few decades.

Now I confess, that when I first realized that there was relatively little information on the development of the museum sector, I judged it to be little more than an inconvenience. At the time I was working on my book Micromuseology: an analysis of small independent museums and I was wholly uninterested in the specifics of whether there were more museums in the English Midlands than in the Scottish Highlands, or how many museums were devoted to hats as opposed to trains. That seemed like bean counting, a quantitative exercise that would reveal nothing of substantial interest about the sector. It took some time before I realized quite how wrong I was. Having this information would enable me to write new and very different histories of museums in the UK, and to begin to understand how the sector had changed. More than that, it would allow me rethink dominant preconceptions about the location and production of culture in Britain. Once I realised what the information would enable I started to wonder if it could be compiled, and is so, how. Over the following year I began to plan the Mapping Museum project, which eventually gained funding in June 2016 and was launched in the October of that year.

Over the next few blog posts I’ll explain a little about why the data was missing and how the Mapping team has begun to compile a dataset of all museums that opened (and closed) in the UK between 1960 and 2020.

 

©Fiona Candlin May 2017