Thank you for joining us everyone. We're just going to give it a few seconds to let people log on before we kick off formally. I can just see the numbers going up so we'll just wait a few minutes for everyone to get comfortable and ready for today's event. Ond bod yr enw yn gweithio tra'w amser gyda'r tynnu allan, rwy'n cael ei dweud i'r papyr rhwng diwrnod Cymru ar y D�rymenau Paul-Mellon yn y Gymhelfarad Feel. Rwy'n gweithio i'r Llywodraeth Microedd Maxxer, ac rwy'n gweithio'r Llywodraeth Pooh Llywodraeth Loed. Rwy'r llunau Ll來 yn ymgyrch yn roi ddiolch. этиb yn Gymryddon Birfyddon, ddodd Llan, gyda ddiolch ar y llunion Ond rwy'n gweithio'r llunion gan eich cyfnod yma yn gweithio i ddechrau o'i gygu â'r ffordd ar y cyfnod'r website. Maen nhw'n gweithio i ddim hanfod i byw o ddweud â'r website,fa ychydig o'r ddweud yn oed fel yna yn mydd y centre. Sfer y pwynd i yw'r gweithio, mae gwirio 你wch yn gwneud drwy gyflwyno a'r lookson, rhaid i hefyd y Llyfridd. Rwy'n meddwl am ddif� am y ddechrau, mae yw gweithio ar gyfer y cyfrwno, rwy'n hynod dweud y rhaid i ddweud, ac i ddweud y gwrthodau i gael ei gyrdd, ac mae'n gweithio'n gweithio'n gweithio hynny, rwy'n ddweud o'r digwydd ac mae'n gwneud eich cymryd, i ddweud gan amlwg i'r gwneud ac i'r gweithio i gael gwneud, a'r gweithio i'r eventau yma yn ysgrifetol dechrau, i'r reisio'n gyfathodol, i'r ddau'r gweithio'n gweithio. Lleiddoedd hi wnaeth y werth ynglyn â'r pethau yn byddwyr, ond yno, Martin, Michael Oeddon, Mae Co-connut yn gynnyddio'r gymuned yn gynllun o'r genesys gynnig o'r gyda chi ammellai ac addysg ar gyfer bod ychydig iawn a gwnaeth arnynach ffordd o ddau'r methodologau. Ond o dddarparu hwn yn eich bod ni'n gychwyn o gydig iawn i ddeudio ddyliadol, o ddau'r ddau ffysg – o ddiddoriaeth, arddurwch, storiograffi, a methodologi. Rydyn ni'n dweud bod yw'r ffordd yn y ffordd o'r ysgrifennu ar y technologi ac methodologi, a'r ysgrifennu o'r pethau o'r ddweud o'r ddweud o'r ymgyrch yn y rhan o'r gweithio, ymgyrch, ymgyrch, ymgyrch, ymgyrch, ymgyrch. Yn ymgyrch ymgyrch, mae'n cyfrifio, fel ymgyrch o'r ddweud o'r prynch, a'r ddigital, ymgyrch o'r ddweud o'r ymgyrch o'r ddweud o'r ddweud o'r ddweud o'r ymgyrch o'r bethau o'r ddweud o'r prydian ystafell o'r ddweud o'r ddweud o'r ddweud o'r treidog o'r ddweud, ad roeddwn i'n gwagoroddau cyfry, ar ddweud y ddweud o'r ddweud, ac yn dweud o'r ddweud o'r ddweud o'r ddweud o'r sgwpwy功wll ymgyrch o'r ysgrifennu ar yr ardydd. Ac derbyn conversation iddyn nhw, journal and open access, born digital journal British Arts Studies and you can find some articles there which are exploring mass data issues. And also our Royal Academy Chronicle and Danny is going to put the links to those publications in the chat and I know that one of our speakers is also going to use the data from the Royal Academy project as well so it's great to see the afterlife of that publication and the thinking around that. It's been spoken about today. So we really hope that like I say today will generate as many questions as it answers and think about further avenues and connections for thinking through these ideas. And before we get on to more of the the sort of meat of the intellectual inquiry of today. I just want to quickly walk you through some of the housekeeping that we have for Paul Mellon Centre virtual gatherings to make sure that these are really enjoyable and productive spaces for everyone involved. And you can see that as audience members, you can really interact with the event, and we really encourage you to be active in the chat box I can see that you're already interacting with us which is great so hi to everyone in the chat box as well. But if you want to put formal questions at the after the papers, please type those in the Q&A box and the chairs of our panel will read those out to the speakers. And we also do have a function by which you can speak to us and we can hear you. And you can put your question live, as it were, by speaking, if you'd like to do that, you use the raise hand function, and we can then unmute you and ask you to join the conversation. The session is being recorded and made available to the public. And again, I'd encourage you to go and have a look at the recording section of the PMC website. We do try and record a lot of our events and they're becoming their own kind of online resource and publication in their own rights. So it's there are great resource to point to your students if you teach or any of your other networks as ways of again interacting with events that you might not have been able to participate in or watch live. And you can also use a CC function to enable captioning, and you can find that at the bottom of your zoom screen. And again, like I say, to make sure this is a safe and productive environment. We will we do reserve the right to remove any audience members for offensive behaviour, but we really hope that doesn't happen and that this is like I say a really productive and generous space for everyone involved. So without further ado, I'm going to hand over to my colleague and co convener, Martin Myrone to say more about the topic and how we got here today. Hi, Martin, over to you. Hello everybody. Yes, I'm Martin Myrone. I'm head of grants fellowships and networks here at the Paul Mellon Centre. And with Sarah Turner and Shreya Chatterty. We've kind of worked on on framing today's event and sending out the invitation. And well, before anything else, so thank you to everybody who's contributing today, and everybody who's listening in or watching in as well. And even if you're not going to use the chat to post comments that's worth looking in on because I've just been seeing comments appearing from all around the world and Rome and Cairo. And this, yeah, it is, it is an international participation, which is always fantastically heartening to see and a reflection of the digital world that we're in, which, you know, for what its limitations is also hugely enabling as well. As Sarah's already kind of indicated, we've been thinking about this event or something in the area which would lead to this event for quite a while now. We've been kind of checking back through email exchanges and discussions. It was really the winter of 2020 to 21, where, in a kind of those kind of happy coincidences we had several different conversations, which seemed to indicate that an awareness that out there, and a whole range of different contexts with people working in potentially very different ways, and in different environments into different ends, when nonetheless engaged in research or book projects, which we're dealing with and representing large volumes of data in order to meet some historical objective, or to address an area of inquiry. And there was a sense that there was, is there something new, is there something novel, is there something interesting, is there something that's worth reflecting on in that range of work, accepting this diversity, that there may be some common ground or some common questions. And I guess there were probably a couple of points which occurred to us when we started with that observation. It was that a lot of this work was focused on objects and on the records of objects and the way objects have been circulated and recorded, notably in the art market. But there were also projects that were looking slightly differently at populations, at people, kind of human data, bodies of data relating to kind of human entities, artists and consumers, and that seemed to be kind of interesting. So that was one thing to note. The other thing was a sense that this wasn't digital, it was digital art history, but it wasn't something that was contained by the idea of digital art history. The outputs might be digital, and we've mentioned a couple of projects already which have been published online. There were also book projects, there were people who were working towards book, and people who were working towards kind of quite established art historical perspectives as well. So technology was enabling, technology was facilitating, technology was the medium, but it wasn't an end of itself. There was something there which wasn't being addressed in the discourse around digital art history, but was more about the intersection between technology and mass data, and art historical practice in a broad sense. And in that regard, I guess it's notable that there are presidents for the sorts of work that we're recognizing, and thinking needs to be represented and connected up, which were distinctly non-digital, or which were developed before modern mass data technology, and Montias on Dutch art, or White on White on 19th century France. I'm sure they may have had some access to rudimentary databases, but they were producing books, and they were working for some material which perhaps are massed and analysed in ways which preceded the sorts of technological instruments that we have now. And we are aware, kind of broadly aware that there was a whole range of research questions that arise from this about how we think about artists as a group, and how we think about previously neglected source materials which were newly available including exhibition catalogs, commercial directories, census records and news and media, and all of which can be interrogated much more readily and in potentially very revealing ways. And out of that comes a question about questions around what does it mean to think about industry beyond the singular, to think about the mass rather than the individual about patterns and populations, and what does it mean to think not about individual objects, but objects in large volumes. And what does it mean to think about artistic populations and how these intersect with larger political entities like community class and nation. And that led to an exploratory session in May 2021, which included a number of people here here today, but also Paris Spies Gans and whose book on women artists being, which was due to be published by the Paul Mellon Centre, which is now out, was one of the projects of which we thought was interesting and that it was, you know, it was a book project but it was utilising large volumes of information. And that workshop really kind of just kind of looking at what what work was out there and what the potential was led eventually to the call for papers and to today's session. The questions which are kind of very much on our mind today as we hear the presentations are was a fundamental question rather brutal question. Why do this work. What's the point. What impacts does what impact does mass data have upon our historical methodologies. What does it mean to have this data, and what impact does it have on art history as a discipline. But most of all, and this I think is the kind of endocrine which emerged out of that exploratory workshop and which we're thinking about today. What can we learn from each other. What can we learn across disciplines, across methodologies, across national and cultural contexts, across different kinds of material and different periods. And although we know we as the Paul Mellon Centre are very much focused on British art and that's our raison d'etre, and that's what we're we're aim for we have a very open and porous idea of what British art is. It's a place to start asking questions rather than in itself. And today's activities have been framed by without reference to British art as a limiting notion. There are people addressing British art, but we're really interested in creating international connections and facilitating a kind of comparative dialogue across disciplines across fields in order to, we hope, enrich and deepen our understanding of mass data methodologies and art history. Hence the range of papers and the range of presentations that we have today which cover. I think quite a kind of dazzling array, potentially dazzling array of materials periods methodologies and kinds of work. But without further ado, we should move into panel one. The format for today is to have quite succinct papers but they've got 15 minute slots for each presentation. A number of presentations are collaborative they have more than one speaker involved, and we will run through the papers in sequence. We will very briefly introduce the speakers were full of biographies online, and then have a more sustained Q&A session to take us up to the lunchtime break where we'll reflect and go into conversation around the around the presentations together. So panel one, we're going to start with Anita Gowers and Paul Wilson. Anita Gowers has worked in the higher education sector across a range of disciplines from neuroscience to the creative creative industries and is currently undertaking an artist PhD on the Australian picture framing industry at the Australian National University. Paul Wilson focuses on applying technologies to advance our individual and collective thinking and performance, and it's produced a whole range of data analysis and visualizations including large historical data sets. We have postgraduate courses on technology, leadership and data science. And together, they are addressing, well this is a pretty fundamental question, why art history needs data science. So, over to you, Anita and Paul. Thank you very much. I will share my screen. Okay, just confirming you can see the start of the presentation now. That's great. Thanks, Paul. Thanks, Martin. Our purpose today is to share some brief insights into what data science is about, how you can use it effectively, and the implications for art history research. To begin with, I'm not an art historian myself. I've worked across fields, including a range of data science projects over about 20 years. My co-presenter, Anita Gowers, will explore some analysis about history data in a bit more detail. I'm going to briefly explore some origin ideas and approaches to data science. The basic story of data science is about, it comes from our limited mental capabilities and the discovery that we can use things outside ourselves and actually need to to advance the way we think. There's a lot of recent research from Nobel Prize winners like Daniel Kahneman and Richard Taylor that was recognised the limitations of our thinking and the importance of being able to leverage the things that are part of what's called the extended mind that's out of the work of David Chalmers and Andy Clarke from the late 1990s. This is a really interesting, exciting area. I'd encourage you to have a bit more of a look into it, but I won't go into much details now. The key thing is that the massive growth of technology has really changed what's possible. Businesses and industry are really getting involved in taking on what's called data driven decision making and the World Bank and others are talking about a fourth industrial revolution. The activities going on here are part of a much larger puzzle and also means there's going to be interesting feedback between different fields of human endeavour as different fields pick up and really start to accelerate with these sets of ideas and technologies. In terms of practical points, I'd suggest that you routinely use data visualisations to review your data and findings. Most of our brains are dedicated to processing visual information and it just makes it much easier to see there's some glitch in your data or even something that was unexpected that leads to a new insight. Tables and numbers just don't work and for that reason I strongly encourage you to hunt anomalies until you resolve them. You never know where they lead. I've discovered glitches in the Australian Bureau of Statistics data and been able to confirm that they've got an issue there. So not making assumptions about the data is important too. I anticipate that data science will be new to many people in the field of art history research. In these circumstances, a particularly nice method to use is topic modelling or latent Dirichlet allocation. This is essentially based on the work of David Bligh. It provides a nice way to discover the thematic structure in large collections of documents and to help visualise them. It's nice because you don't actually need to put a structuring to the text. This process will start just classifying things without you needing to have any initial ideas. I've used this process for about 10 years. It involves a little bit of work to get the documents ready, but it's very versatile and you can build up on it in various ways. So the example of David's work I've got here is classified the content of Science magazine articles in decades. You probably do the same sort of thing with information about different artists across time to get just a bit of a flavour of what's happening. It's not about presenting particular answers but exploring the themes that might be worth delving into in a little bit more detail. He's also used this to analyse the reading habits of US politicians and figure out a bit more about... Paul, I just want to check. I'm not seeing the change of your slides and I think I'm just looking in the chat box and other people. There we go. So that's David Bligh and that's the analysis that I was talking about there. He's got a very interesting presentation on YouTube, probabilistic topic models, and it goes through some of these in a bit more detail. I'll keep moving along. I'm not sure, yes. I work for a company that had $40 million to help Tasmanian business and industry adopt e-commerce at one stage. And a lot of business and infrastructure applying for funding. And what I found was that there's somebody else in the world who had already done what this particular business industry was thinking of. What that means is you don't need to consider that you're doing something new and unique. You can test out ideas by just looking around to see what people have done.  One of the sites that's useful for this is flying data. They've got a subscription service and various explanations or tutorials that we've actually examples of what you can do. It's an alternative. I'd suggest that for people who are getting into the field, you don't necessarily need to leap into the details yourself. You might be able to find a young graduate or postgraduate student and have them pursue some of this for you. Another interesting group is Kaggle. They have a large community of data scientists. They have data sets and they have notebooks which are full explanations or code to do different types of analysis. Another interesting thing they do is have competitions. This is where a company provides a data set in some goal and perhaps a financial reward. What happens is they reward the data scientists to contribute to a solution. This is nice because you don't need to understand the technologies involved or the concepts involved. You can get some very smart people working on the challenge that you're trying to understand without needing to be an expert yourself. Just on a final note, the movie Moneyball is quite an interesting example of what can be done with data analytics. The basic story is that talent scouts, however long the sport had been played, had been focused on developing or identifying people to recruit. A Yale economist and a coach realised that they could apply data analytics methods and get high-performing teams for much less money. I found it quite interesting that the author of this work, the researcher, said that a lot of the human experts that were involved in this scouting activity were focused on the really flashy aspects of the player's behaviour, like being able to hit or run and this sort of thing, but there was really important other things that were more subtle than had been missed entirely. I think that provides scope for thinking that maybe there's some things in the artistic side of things or in understanding great works that have been overlooked for some subtle reason that might not be apparent. In conclusion, I think one of the interesting things is the technology convergence that's going on. There's analysis methods that could put on a platform for one domain of human knowledge can equally be applied to others. In the same way, career paths are converging. Data science professions are in demand, so just as the nature of the work means you're not locked into art history. That's probably a good thing because there's so many ideas that can be drawn from elsewhere. There will be pathways into and out of art history and that I think will fuel a positive cycle of growth. It's also worth noting that art history is heading into interesting competitive territory. We've got a lot of focus on having people's attention and allowing them to pursue their interests in a useful way. This is a bit of a challenging area because something else is a mouse click away. It's really important that offerings are compelling, engaging and allow some sort of interaction I'd suggest to really help people feel engaging and get value. Finally, if you're starting out your data science project for the first time, remember it's fine to start small. There's no need to take on the world. It's actually a lot of fun to create analysis insights that people haven't seen before. They get excited and new or two. Even a small success makes the next step easier to take. Thanks very much. I'll hand over to Anita. Onda, share my screen. Right. Can everyone see my slides? That's great. Thanks, Anita. Terrific. And thank you very much, Paul. And hello everyone. As mentioned, I'm Anita and I'm a PhD candidate in the Centre for Art History at ANU. Let me begin by saying up front, I am not a data scientist and I am not a statistician. So what I'm presenting today is basically the useful things that I've picked up about how to interrogate art history data using scientific methodologies. So in other words, I'm giving the practical to Paul's theoretical provocations. My returner generously shared a subset of data behind the online archives, the Royal Academy summer exhibition, a chronicle 1769 to 2018. It's a longitudinal data set. And whilst there are a couple of patchy areas of the data, it is still considerably a remarkable timeframe for a data set that is found within the discipline of art history. The information on this website is presented in depth and annually. So here is a cut and paste from some of the slides that are online on Chronicle 250 displayed from 1769. And what we can see here depicting the exhibitors of Royal Academy members and non Academy members as a percentage within a pie chart. And on the right hand side, it's the number of works displayed and that includes all works again by the Academy members and non Academy members. So what is really clearly visual with these pie charts is that for 1769, there were far more academicians compared to non academicians and logically there were more works by members of the Academy. But what I'm interested in is examining this subset to see if there are longitudinal trends that span the entire data set, which will potentially generate questions about the data. So I've received this data in an Excel file format consisting of 17 variables and realise relatively quickly that mapping each of these would be incredibly time consuming. So we decided to pop this information into a scatter matrix. Don't adjust your eyesight. I have done the font small deliberately. So essentially a scatter matrix is a set of points plotted on a horizontal and a vertical axis to give a summary of all of the relationships between the variables. It visualises these pairs allowing many relationships to be explored within the one chart within the one single visualisation. And what the diagonal shows the distribution of the data, the data above the diagonal plots the inverse of the data below. So in other words, it swaps the vertical and the horizontal diagonals. And with a quick glance, a few things jump out. We can see the remarkable similarities between the number of academicians and the number of exhibits because they track nicely together. However, there's something interesting going on within the exhibition length, which is up here and as well as the attendance, which is here. And also the sales because you would assume the longer the exhibition, the more people and the more sales. So to look at that data further, I've popped it into a bar chart. So this is the exhibitors with academy members and non academy members.  The point of this bar chart is for your eyes to look for the anomalies in the data without focusing on the numbers. So you can see this bar chart displays the academicians, which is the blue, which is remarkably consistent over time, with the exception of the very first year being 1769. And the non-academicians, we can see some significant variations here 1771. There's a drop in 1805, sort of 1850 to 1860 here. There's some high points, a drop here, which is First World War, Second World War, and also the Brenton Woods system in the early 1970s. If we take that same information and drop it into what's called a percentage stacked bar chart, this time we can see the 1769 data, which was barely visible here, absolutely pops out at us in this demonstration of methodology. 1771 also pops out and you can see some of these other anomalies as well. So the display of the data impacts how we mentally perceive it. I've done the same thing here with exhibits and year showing the non-members in red and the Royal Academy members in blue. And again, you can see this remarkable variation. So as art historians, you'd be looking at these variations to then go back to the data, say, well, why are there these peaks? Why are there these troughs? And potentially, on a very broad scale, we could say, well, maybe there's a much stronger relationship between non-academy members exhibiting depending on what's going on in the broader world. These gaps here represent the gaps in the data. And when we pop this exact same information back into the stack chart, we can also again see how it shows us different things, particularly this one, which wasn't really noticeable in the former chart. Now, this visualisation is a scatter plot and that shows the relationships between two discontinuous variables. We can see clusters and each dot on the scatter plot represents a pair of exhibition length and attendance. So intuitively, you would think that the longer an exhibition, you would achieve greater attendance. But what the data shows is that over the last 250 years, exhibitions that lasted approximately 90 days achieve the maximum visitor numbers. And exhibitions longer than 90 days didn't necessarily increase attendance. Again, whilst this scatter plot plots greater sales, they're correlated to exhibitions. So what we can see is that 70 days is basically the medium length. So the correlation is not causation. So whilst there are relationships between the variables, one doesn't necessarily cause the other. And in both of these scatter plots, they are only comparing two variables and there are clearly other things at play. So in other words, making an exhibition in length 70 days is not necessarily going to achieve more sales. But when data is presented in using these scientific methodologies, it helps you ask more questions and refine what you're looking at. So by asking questions of the data drives new discoveries and by looking at the data using scientific methodologies, it helps contextualise art history for us and it actually makes it more relevant for those outside of the discipline. It is not a panacea for art history research. It is not a conveyor belt process where you enter the data, convert it to visuals and suddenly you have the answer on your PhD. Data needs to be tuned. It's a process of exploring, looking, examining and checking that will generate new questions and new research. So data science is a different way of understanding our contribution to the commonwealth of art history knowledge. And it is imperative that we as art historians adapt these new data science techniques alongside traditional art historical methodologies in order to adopt new generations to explore art history. And I hope this has been helpful in showing how visualising data in our discipline provides new avenues potentially with new future research questions. Thank you. Great. Thank you so much, both of you, for a really kind of visually stimulating as well as kind of intellectually stimulating start of the day. So thank you for that. Yes, it's a little bit difficult quite sure what to do with applause, applause, you know, there are smiles, you can put thumbs up. But also, if you've got questions, observations, comments, we will take them together in the Q&A session. But I think you can post questions at any time in the Q&A box. It's quite, you know, if something is occurring to you, do take the opportunity to put it down now. Otherwise, we'll turn to them in the group discussion after our next two presentations. And we'll move on next to Shane Marcy, who's a PhD candidate in the Department of Art, Art History and Visual Studies at Duke University. And Shane's presentation today is digital humanities as a fundamental methodological approach. A case study of the postcard phrase, which I understand it relates very closely to your doctoral project, although I know you're also a active author on the online dictionary of art historians. So I think things kind of connect together over the morning as well. So over to you, Shane. Great. Thank you very much. Can you hear me? Yep, that's all good. That's great. Thank you very much. And thank you Anita and Paul. That was fascinating, especially what Paul mentioned about the importance of starting small and maybe even the pathway out of art history that you mentioned. But so I won't reiterate what you've just said in your introduction, but I am involved in a number of digital projects and the project manager of digital public buildings, North Carolina, but I do also participate in the dictionary of art historians. I want to do other work at the Colab at Duke around 3D printing and trying to expand the parameters of, of what can be done with that as a medium. Sorry, just sharing my screen there. Right, can you see that now? Excellent. That's good. A particular project is distinct for a number of reasons. For a starter represents, as you mentioned, a component of my own dissertation, but it's also a case study on the basis of which I want to suggest a kind of broad but relatively simple argument in favour of mass data methodologies as a kind of fundamental aspect of research in the humanities. It's a kind of rudimentary digital research as a kind of basic form of best practice based on the accessibility of a wide range of tools and the increasing availability of digitized data, especially in the form of online archives. And the capacity that these resources hold for carrying out kind of manageable projects that are limited in scope, but which have the potential to overturn. So, my dissertation just in brief looks at American modernity through the lens of the postcard craze, which took place between in the United States between about 1907 and 1920, during which period the nation was inundated with these objects. And I explore the medium as both an object of art historical investigation, but also as an exemplary form of mass produced commodity in order to ask questions about, in particular, the political economy of the progressive era, I think maybe the best way to just succinctly describe my kind of evolving approach to the medium is to look at the relationship between both sides of the object. On the one hand, on the one side, and through an exploration of the inscribed messages, I ask how the postcards insistence on personal interaction was able to establish a uniquely intimate engagement with what was often a kind of quite alienating mass produced commodity form. On the other side of the object, I'm looking at the image, especially the view card and exploring how it adapted new technologies photography, which was accorded a kind of scientific verisimilitude, but also chromolithography, which carried profoundly commercial connotations, and how these came together through the scale of the postcard craze to create a kind of totalising and commodified vision of reality. Maybe what Walter Benjamin might describe or Marx might describe as a phantasmagoria. And ultimately, I argue that in putting the two sides together, that the intimacy created by the personal engagement with the commodity form allowed for a far stronger kind of identification with this phantasmagoric reality that's suggested by the medium's imagery. And that in doing so, the medium helped to establish a very particular and important form of identification between the modern individual and the commodity form. And significantly, this took place at a time when this was precisely what the American advertising industry was striving for. And of course, you know, this kind of relationship continues to exert a powerful force in our own everyday experiences, but that's the project overall. The digital component of this was concerned with something far more specific, which was visualising the scale of the craze. Specifically, the numbers of postcards purchased in the United States. So, in terms of this project, this is primarily a production analysis. And it's crucial because part of the argument is that these images were produced in such quantities and were so ubiquitous that they impacted the way in which people perceived their everyday experiences. So in terms of the phenomenon scale, both the scholarship and the primary sources clearly convey a narrative of this explosion in postcard sales alluded to in this image of Uncle Sam drowning in a sea of postcards followed by a complete crash. So what I did first of all was to gather information in order to verify this assumption. And so I said about visualising the existing data that is the data for the postcard craze that is currently cited in the literature. And this is based on a book by George and Dorothy Miller upon which almost all of the subsequent scholarship relies. The data themselves are drawn from the Postmaster's General Annual Report, which is a government document that has been made available online in recent years. And what the millers do is that they cite the data for the years that they believe represent the peak of the craze, which for them is about 1908 to 1913. So this is a very small data set that they're using and it looks like this when it's visualised. And on the basis of this data, the millers determined that the peak of the craze occurs in about 1913 when about one billion postcards were sent annually. So in short, what the millers did was they decided they knew the dates of the craze. They went and got the data to back that up at a time when this data was far less accessible. And since then, nobody has kind of critiqued this methodology in spite of the fact that several books have been written on the subject, that one of which explicitly adopts a digital approach to focus on American postcards. So I wanted to take a more comprehensive view. So the next thing I did was to gather all of the data from the Postmaster's General Report between 1873 and 1930. This evolved going through every annual Postmaster's General Report, finding the number of postcards at sites, entering it on air table, which was also able to tabulate this off and kind of quite fragmented data for me, and then visualising it on tableau. And what this relatively simple chart showed was really, really interesting and importantly not readily apparent from just looking at the data. What I expected to see was a huge rise starting in 1906, followed by a complete crash around 1913, I guess kind of bell curve. But actually what we see is this image here. And this is really significant because there's no boom and bust here. Instead, what we're looking at is a really slow, gradual incline interrupted only after World War I in 1917, when the price of mailing a postcard was temporarily doubled. But the number reverts to the previous trajectory almost immediately and continues along that line well past 1913 to the 1950s. So it was this visualisation that brought my attention to the fact that there's a discrepancy between the accepted narrative of boom and bust that we know occurred because of the overwhelming number of primary resources that attest to it, and the numbers that are being cited in the scholarship would show nothing of the sort, and that I needed to explain this. What I eventually realised was the Postmaster's General Report, which the Miller's citing was only referencing one subset of postcards. In its most simple terms, it only showed government issued postcards which were known as postal cards. And the explanation for this is that these postal cards were the only kind of postcard available prior to 1898 when the government had, the United States government had a monopoly on the medium which they relinquished then and allowed privately issued postcards to enter into the market. But there's very little data for these cards because in part the majority of them produced in Germany, but they were also produced in both in Germany and the United States by numerous private manufacturers. And the reason that there's such specific data for government issued postal cards is because they contained a preprinted stamp which is essentially kind of cash and therefore had to be accounted for quite carefully. So this strongly suggested that the real scale of the craze was far greater than previously had been understood that extended far beyond government issued postal cards and included these privately produced postcards, but it raised the problem of accounting for this set of private postcards and I started to search around for what kind of available data could we chart that might help us to approximate the scale of the craze. And after searching around for a while, I eventually turned to the sales of one cent stamps. I noticed that in the primary sources, stamps are identified as a proxy for the craze. So you might see an interview at a post office cashier in Atlantic City or somewhere who says that the sale of one cent stamps has gone through the roof thanks to the craze and you see this mentioned many times in the primary sources. And the data for the stamps was also included in the same postmasters general report because all postage stamps like postal cards were issued by the state. Unfortunately, the specific data drops out from the report after 1913 after a new postmaster general's report is appointed by Woodrow Wilson, but it remains a really solid and reliable data set. And so based on my expectations, again, what I was expecting to see here was a sharp rise in around 1906 and a peak in 1910. And that was exactly what this visualisation showed. So what we're looking at here and the orange line at the bottom shows the slow growth of government issued postal cards over time. And this orange line at the bottom was previously assumed to represent the numbers for the entire craze. The red line shows the expected sale of one cent stamps during this period. This is an algorithm that we've been working on that attempts to project the number of postcards that we, excuse me, the number of one cent stamps that we would expect to be sold during this period. It's still something that we have to refine slightly to take into account for rural free delivery and things like that, but generally speaking, it's relatively accurate line. And the blue line shows the actual sale of one cent stamps during the postcard craze. And we see an uptick immediately after 1898. And here we have the beginnings of an outline at least of a true kind of boom and bust cycle. So in short, the primary source upon which all this literature lied at the dates approximately right, but the numbers completely wrong. And what this rudimentary digital visualisation showed was that the number of postcards sent through the mail at the peak of the craze, which was assumed to be about one billion, which I think is already an extremely high number, was in fact closer to three and a half billion. And this is really significant in terms of the actual scale of the craze and the breadth of its impact. But, you know, obviously, you're probably thinking, yeah, but what about all the postcards that were produced but weren't sent through the mail? For example, there was a huge culture of collecting and scrapbooking postcards. So I was also presented with the problem of approximating this number, and this is where the data is the patchiest. However, there are a number of kind of corroborating facts to back up the small number of data points that I have identified, but this part is really still a work in progress. So I started looking for government tariff schedules online at the Library of Congress and discovered that after 1908 and the passage of the Payne Aldrich, tariff acts postcards are classified individually within them. I haven't yet been able to identify the specific government report that details the classification of the tariffs, but I found references to these elsewhere. And that's the data that I visualized in the final chart for the years between 1906 and 1909 based on the figures for these imports. And what I want to draw your attention to here is the bright yellow line at the top, which represents an approximation of the total number of cards imported to the US from Germany during this period. And you can see how these numbers dwarf the original estimates. So the final total for the peak of the craze represents the 1 billion postcards that are initially considered to constitute the entire phenomenon, plus up to as many as potentially 9 billion apported from Germany, of which approximately 2.5 billion were mailed. And of course this 9 billion figure doesn't even include postcard produced within the United States, of which there were many especially after 1908. So the digital tools and resources help to both identify and demonstrate the fact that we were dealing with a phenomenon that was almost an order of magnitude greater than previously had been assumed. And in terms of the significance from my research, we're now looking at a medium that penetrated everyday life in a way that we had kind of never anticipated. But from a broader perspective, what I want to argue is that this points to the usefulness of existing accumulations of mass data in digital archives that can be accessed with relative ease. And that the fact of this availability, combined with the usability and the accessibility of tools such as Airtable and Tableau help to establish an argument for the digital humanities as a kind of fundamental instrument of best practice. That is not as a supplement so much, but something more basic to the discipline of art history. You know, instead of suggesting a digital approach as a potentially usable methodology, I feel as if it raises the question as to whether we should be asking why is such an approach absent of what precludes any given project from at least a rudimentary digital analysis using sources and tools that were previously unavailable, but are now far more widely accessible. And then finally, at the same time, there's potential for a larger project here I feel I think it points towards opportunities to engage with other digitised collections. I'm thinking of the New York Public Library in particular, where you might be able to take advantage of either a digital content analysis, and or indeed the emerging technology around a handwriting analysis, or perhaps most interestingly, a relational analysis of these two facets. Thank you. Fantastic. And yes, there are all sorts of applause, visual applause, visual applause around the room and beyond. Thank you. Thank you for that. Fantastic. And thank you for some mind boggling numbers in there as well. We've sort of moved in terms of volume to the billions already, and it's not even one o'clock so fantastic for that. I will keep moving as I say, do keep up your if you've got comments about the thing on the tech side or, or about the event as a whole do person in the chat questions for the panellists to have ready, put them in the Q&A, they can be lined up for our discussion section. And that will move straight on now to our third, third and final presentation in this morning panel, the panel one. And we're sticking with Duke, with Hannah, Paul and Lee. Hannah L Jacobs is digital humanities specialist for Duke University's digital art history and visual culture lab, visual culture research lab, and is studying for masters in information science, the University of North Carolina at Chapel Hill. Hannah L Jacobs is a professor of art history and German studies at Duke, and is co director of the digital art history and visual culture research lab. And Lee Sorenson is a librarian and bibliographer at Duke University and another co founder and co editor of the dictionary of art historians. And it is indeed the dictionary of historians studying art history at scale, which is the title of this presentation so welcome our three presenters over to you. And thank you so much for for having us. I'm going to kick things off and then I'll pass things over to Lee. So the dictionary of art historians is an open online resource that documents the lives of art historians of mostly Western art. The project is supported by Duke University's digital art history and visual culture research lab with Lee and I as co PS and Paul is our research collaborator. Through the lab and funds from Duke's office of undergraduate research support we work with student research assistants to draft new entries, revise existing entries and continue to improve the data structure. And this presentation will be sharing the project's history, showing the latest version of the website and database and discussing possible research questions that the date that the dictionaries data might support. Thank you, Hannah. Like few contemporary database sites, the dictionary of art historians was begun as a paper project over 40 years ago, and has developed as the internet developed. It's a specific mission for graduate students to research significant Western art historians by their methodology. It includes only art historians who have died or at the end of their careers. There are over 3000 entries covering globally historians of Western art. Each entry focuses on a bio bibliographic form, the art historians familial background schools they attended and faculty under whom they studied. Art historians writings in the chronology of their lives, as in fact they happened. It identifies debates and animosities concluding with bibliography of their own writings and sources of the information used, and the art historians archives, if available. It is similar to many contemporary databases, the dictionaries goals and structures broadened and sharpened by the examining crowd use and feedback. When the project joined Dukes digital art history and visual cultural research lab in 2017. It was brought up to date functionally by loading the static files into a content management tool and migrating it to the Drupal open source software. It is a collection of the processes of our second presenter and the projects co editor Hannah Jacobs. The previous clunky way of keyword searching was replaced with the construction of discrete fields for the most common queries gender institution country of birth and death, and subject specialty of the historian. It is content. We were lucky to be able to use the early general art historiographies, which were the beginning, which are beginning to appear. In addition, the published collections of specific groups of art historians appeared. We were able to mirror current scholarship. One of these was the massive inventory of German art historians who fled Nazi Germany between 1933 and 45. It is important to note that Paul will speak about. It's important to note that the data of when lans book is still only available in searchable form in our dictionary. Today, the project boasts of unique data groups of art historians, most notably women historians and those of color. Representative data visualizing and graphically analyzing our discoveries for the first time. We are truly able to study art history at scale. Thank you Lee, and we're going to take a brief tour of the latest version of the website and database. The dictionary's website and database are designed as a tool to help researchers working at a range of scales. So from studying an individual as Lee mentioned to investigating themes across many entries. The site is built in Drupal also as Lee mentioned and this version that you're seeing today has just been launched in Drupal nine and we're going to be migrating it to Drupal 10 very shortly. This is a major upgrade for the site and not just in terms of the platform but also in terms of some of the data work that we've been doing to improve scalability. Individual entries and provide basic demographic information such as names birth and death dates birth and death places countries of origin or residents and gender. And we also document institutions that art historians have worked at the types of careers they have had and their major fields of study. And Lee did not mention the careers I'll say because that is one of the newest fields that that we've just made available. As mentioned our research assistants work with Lee to draft biographical entries the overview field in which we also link between entries to help readers experience the many relationships that are represented in the dictionary. Contributors are also able to develop a selected bibliography of the art historians work, a list of sources used to create the entry and when available links to archives that hold the art historians papers. Each entry is attributed to all those who contribute that entry, which allows our students to point to their work on resumes and applications. On an individual entry page under more resources researchers can access links to other prominent data repositories and online collections that contain relative relevant information about an art historian. This feature developed out of a 2019 workshop with the Getty Research Institute, as well as an earlier partnership with the Getty Research Portal. And you'll see that the more resources links include the archives of American art, the dictionary of French art historians, the digital public library of America, as well as the Getty Research Portal and the social networks and archival context project and wiki data and WorldCat. To help researchers find entries of interest, we continue to offer a full text database search. But for those who want to see what's represented in the dictionary, we have a browse page where they can search through entries alphabetically. Folks can also filter entries by gender country subject area institution and career type. We also provide a directory that lists all of the entries by name along with some key contextual data as an as an alternative form of browsing. Entries data fields rely on a number of data standards, which are documented in the about section of the website under data access. Entries data fields are mapped to the Dublin Core metadata standard and our standardized vocabularies include the Getty's thesaurus of geographic names for place names, the Getty's art and architecture thesaurus and homosaurus. We also add our own localized terminology where necessary, and this information is documented in our data dictionary. Scholars wishing to study many entries, we offer an API that allows researchers to download data or to connect our data set with their own in a separate web application. All content is licensed under a creative commons attribute share a like license to encourage digital digital scholarship, and the data are currently available as JSON, but we can make a format such as XML and CSV available as well. And we're also in the process of submitting the data set to Duke's digital repository as an archival record of our work to date. And just to give you an initial sense of the makeup of our data set, here's a visualization showing countries of residence for art historians who were active in the 20th century. Likewise, a visualization showing subject areas represented among 20th century art historians. These two visualizations were created by my first exporting the data and doing some minor data transformation. And then I shared that data with our labs graduate assistant data Hogan, who was able to plug the data into the visualization software tableau to produce these exploratory visualizations. Finally, we have an extensive and multilingual about section that describes the project, a blog where we post periodic updates about our work and our contact information, and we always welcome information about art historians who should be included updates or corrections to existing entries and other expressions of interest. So now I'm going to hand things over to Paul, who will share some of our digital research that draws on the dictionaries data. Thanks Hannah thanks Lee. Yeah, there we go thank you. Having looked at the origin and structure of the dictionary of art historians I want to turn our attention to how exactly one category of evidence mentioned by Lee can extend from the DOAH data to address a significant question of art historical research. That focuses on well known aspect of European Euro American art history, namely the migration and exile of Jewish and leftist art historians from Nazi Germany 1933 to 45. Such a history is well known in terms of individual biographies of course like proud timer, but scaling up the question to include the large number of art historians and the DOAH, who were exiles allows us to see patterns and patterns that raise new questions about the history of the spread of art history itself. In addition, this extended analysis also allows us to reflect back on the other sections, since dealing with a large number of art historians in aggregate points back to the data structure of the DOAH in the first place, as well as how the site may be extended through linked open data, and attending to interoperable functionalities. Having looked at the structure of the DOAH, as well as visualization possibilities, how can we test its potential with the subgroup of exiles. Here we start with the source that Lee has mentioned and contributed much to the DOAH, Orica Wendlands Biographies Handbook, We developed data set from these art historians DOAH and then extended that with additional geographic and temporal data from Wendland. From this we were able to map the movement of art historians from Germany to the world, such as this map of the final post war destination of all German exiles listed in the dictionary. And the other maps we have created show known patterns like the early move to Paris in London, for example, but also more unexpected locations, such as the importance of non coastal locations in the United States. Furthermore, an animation helps us to see the clustering here in the United States of the initial move in exile, but also the move within the United States of exiles, possibly indicating the precarious nature of their unemployment as they moved from site to site. The point here would be that the biographical nature of the dictionary has led us to non biographical systemic categories that then form a context in turn for the distinct entries of the DOAH. Working at different scales pushes the DOAH to the level of a research site and also expands its potential. In this regard, we are thinking about how we can start to connect systemic questions with our biographical dictionary. For example, we have begun to extract questions about social and intellectual networks by pulling out bibliographic information of the DOAH, such as this map of the exile path of any art historian who was active at the Wabog Institute, both in its Hamburg and London iterations. I would argue that this map is in and of itself not that interesting, except as an exploratory site. The visualization in this sense may lead us to other questions, such as whether these specific exiles also shared professional or intellectual trajectories after leaving Nazi Germany. For example, see patterns in terms of which journals the Wabog group of exiles published are these patterns indications of standard career choices and publishing venues like the art bulletin, or do they cluster around editorial boards through the social network of the institution, or content from intellectual communities. In this case, the extension of the exile work from the DOAH leads us back to ask systemic questions of the DOAH as a means of expanding its scholarly use. In the end, producing and using the DOAH allows us to think about the intellectual questions and preconceptions that were specific to each phase of our analysis. As we thought through the structure and history of the DOAH, as much as we learned through exploring its digital and art historical possibilities. We also learned through the process of entering and visualizing data, a process of knowledge building that is certainly well known to the digital humanities. Ultimately, our goal is to see scholarship, in this case art historical, and resource building as dialogically engaged intellectual practices that are too often separated institutionally and epistemologically in our worlds. And I want to emphasize that last point that resource building and scholarship are integrated activities, not necessarily distinct processes. Our argument calls for more engagement between the areas of digital and evidential expertise in order to draw out the complexity of critical questions concerning human experience, as well as its current expression through digital and art historical means. Thank you. Thank you so much and we're looking forward to the Q&A here again as our website. We encourage you to check it out. Thank you. Thank you all again, sort of round of applause for sharing some of the kind of front of house and back of house of such an amazing resource. We've got a couple of questions lined up already, which we will come to in a moment, but if you've got comments, observations on any of the papers, any of the presentations this morning, do post them in the Q&A. We've got a bit of time now for discussion around the panel, so if everybody, everybody who's speaking this morning should, I think, kind of unmute and be ready to jump in. I mean, I've got a couple of thoughts really, but I've got a very kind of basic question, a probably rather stupid question, which you may want to dismiss immediately, which is about, well, in all three presentations, there's a lot of emphasis and importance put on visualisation and the possibilities of visualising data, visualising information, not just because it communicates effectively or does a good job or is instantaneous, but because it can actually reveal things that you would not be aware of otherwise. There's a kind of revelatory dimension to visualisation. With that in mind, I wonder, perhaps around the panel, what attention you give, if any, to almost the aesthetics of that visualisation, even things like the choice of colours.  I mean, I wonder in terms of your own projects and your own work, what emphasis do you put on those aesthetics, how much you depend on kind of off the shelf kit in order to a pallet which is just ready or if you've had experience of working with designers and whether that's part of the process as well. I'll make a comment. Yeah. What you tried to do with the design of visuals is decrease the cognitive load on people, the amount of work they have to do to interpret what it is. So that's why you use align colours and align with common uses and organise things that, you know, in a timeline format consistent with what people expect because you're leveraging what they already know. One broader way to think about this that I've found useful is that what you're engaging is conceptual toolmaking. Whatever you're doing with the data, the actual interface with the yourself and other people is this interface and that's the conceptual tool, the effectiveness of that tool is really important. Jane, what about your visualisations? Is that what's the working process there? Are you kind of taking things off the shelf and working with that more? I'll just jump in quickly there. I think that one of the big issues here is that we're working with pre-packaged software and that that itself comes with its own expectations and at least in our lab, we talk a lot about that and we talk about the idea that, well, you know, if you're using Tableau, that's really being used by the advertising industry and that has an issue, you know that there's a meaning to that. One thing that is really interesting for example just in the dictionary is of course that, you know, Hannah and Lee have spent a lot of time thinking about the user end but, you know, the maps we're producing in, you know, we're just using ArcGIS Esri, you know, we're trying to make them look very drafty. We're not trying to actually make them look very good. And we call them draft maps and the reason is to kind of point to that a exploratory function but also point to the weakness of the methods and the tools we're using. You know, the fact that it is about a kind of connection to a privatised economy that is different than what we might prefer. So I do think that actually that is a really interesting question of how we think about visualisation and that's intersecting with a kind of broader digital economy. And do you see kind of futures being mapped out there? I'm thinking about kind of animation and we've sort of quite a lot of static images that I think there were references to some of these things being digitised and kind of having a duration as well as a kind of slide like quality. Is that something which is developing in your own areas or which you're utilising yourselves? I'll note to you the area of digital twins which is emerging in the corporate areas where we're trying to map a lot of different types of data onto, for example, a model of a business or a model of a race car. And the purpose of doing that is then people are aligned in their models and the data all fits together in a meaningful way that relates to the real world. So that's a particularly powerful approach. Picking up on what was said before, in the initial stages where you're exploring things, it's not terribly useful to refine things too much. You're just hunting for ideas and perhaps it's at the presentation where you put a fair bit more work into it that you'd be a bit more elaborate. Yeah, I would just add to that, just as significant as the visual effect, the visual impact of producing these data sets and the kind of whatever forms we're producing them in, in my instance, its graphs, is the opportunity, which is I'm working on at the moment, which is to make them interactive to some extent. I think that part of the force of the visualisation can be, or the force of visualisation can be harnessed and enhanced through allowing people to interact with it themselves and to truly tease out the significance of the comparison between and amongst different aspects of the chart. OK, great. I've got some questions lined up here, so we should start to move to them. I've got a question posted by Lena Kraus, and it's for the dictionary of art historians team. I'm very interested in the API and the reference to other authorities such as Wicked Data. I tried the links on a few profiles, but they bring me to an exemplary query with Georgia Fasari, and I can't see the authority IDs in the JSON file. I was wondering if you have mapped each person in your dictionary with the other authorities. I imagine it would include work to contribute to Wicked Data, since some art historians might not have a Wicked Data node yet for the example. Are you interested in doing so? I can take that question. Excellent question. The way that we currently have it set up is more in a one-way system where folks who are looking at an entry on the dictionary can do an automated search, so those links are automated searches of the other resources. I will say I've noticed there's a bug, so if you click on one and it's pulling up an example entry or it hasn't searched for the correct person, there's something that I'm working on there right now. So you've hit on something that is a challenge with the recent launch, but the intention is that it will search for whichever art historian the page that you're currently on. As far as including IDs and connecting into Wicked Data, that is an aspiration for the dictionary to actually have some of that data pulled in and directly available in the API. It's not currently there and we do anticipate that we would be contributing to some of these resources. Some of the resources are limited. For example, the Archives of American Art is not necessarily going to have our European colleagues listed, but for things like Wicked Data and perhaps WorldCat there may be opportunities to make suggestions. That was a core part of our partnership with the Getty research portal actually was as they were identifying texts that they should digitize for the portal. We were in conversation with them and sharing data with them so that they could identify authors to include. So that's something that we're hoping to work on in the future. Okay, great, thank you. Sarah's got a hand up. An actual hand. This isn't really a question because I'm just trying to shape some ideas, but I'm just picking up on an idea that I think was presented by Paul right at the beginning. The idea that doing this kind of work has the potential to focus or shine light on things that have been missed perhaps or left out of the grander narratives or conventional traditional forms of history writing, let's say. It's probably kind of paraphrasing and reducing your arguments a lot here, but it just made me think about, you know, and I think maybe we'll explore this in some of the later presentations as well. About whether mass data methodologies part of the impetus or one of the very strong strands of this research is work is kind of shifting the kind of conventional gaze of our history from the object or questions of aesthetics and normal questions to just the things we've heard today about, well, people, institutions and networks, you know, sort of postal services, it's sort of shining a light on, you know, just some of the uncanonised of history. Again, sorry, it's not a question, but it's just the thought about, you know, does mass data methodology have that potential to kind of yet on earth things that have been left out of conventional disciplinary structure is where I'm going with that. I'll make a brief comment. Yes, I think it's really important that it does. There's a broader societal function to creativity and art and the increasing print towards technology and shifts in the careers that people are taking are going to challenge that in various ways. And you see that too with artificial intelligence, with the creating artworks or music works or authoring things. We need to understand and find ways for people to participate and understand their own development in an artistic sense, quite apart from what the canon side of things is all about. And yes, it's very considerable potential for that. I think as well as an eater as well what you was, you know, the history of the Royal Academy isn't really written about exhibition length. You know, it's about Joshua Reynolds and, you know, the sort of formation of national schools, you know, that's that's what's tended to come to the fore, but I'm kind of interested in the kind of intersection of the kind of. Yeah, then both bureaucratic, like the number of days, the sales, you know, and actually the intersection of those with then writing about careers and aesthetic choices of artists. So I wonder, Anita, if you want to just pick up on that as well. Well, and also just by dropping that data into different visualisations, it's then the possibility and just by looking at those trends using your eye to say where are the outliers? Where's, where is anomalies? And then to go back into the data and say, well, did this occur because there was a, the Royal Society made a change about who could exhibit? Was there something global in terms of, of course, you know, the non-academicians that were less during, you know, wars? And if that's the case, maybe then the Royal Academy is engaged in broader rather than just looking at it within London and within the London. Kind of ecosystem of art history. And to Paul's point, I think the possibility of creativity with mass data to allow the public to interact with the data, I think is critical. And I think Shane sort of referenced that. The other thing I, the tool that I actually built for my PhD use Neo4j and it, and it visualises relationships between framers, artists and frame makers. And that uncovered links that would have taken me years to find with my bits of paper everywhere. Like that was phenomenal in terms of visualising relationships. And then I would go back using traditional historical methodologies to follow up those links. I mean, it's striking how already today there's been several points made about the need to keep different, both the kind of the established methodologies, the practical, all the kind of analog methodologies in dialogue with the technology as well.  There was a question that was posted earlier on that connects with this discussion a bit, but I see that Ming and Hans have hands up as well. So Ming, I think you were first. Thank you, Martin, and thank you everyone for those fascinating papers. This ties in a little bit to what we'll be speaking in about in our paper as well. So I won't sort of jump the gun here, but I'm wondering to what extent all of your work, and I'm addressing all of the speakers here, thinks about the question of bias in data structures and the bias that is embedded itself in the data, which, you know, as we know data is not a neutral category. And so how do you contend with those issues in your work? I can jump in with one sort of obvious example. When we're covering women or art historians of color, they're not only are they documented last but when they are documented they have shorter entries and so we're in kind of a predicament. How do we fill in a lot of places that are just missing from those people. And it's a concern. In some cases we've been able to use our own knowledge or whatever to fill those things in, but in a lot of very precise visualization, we do understand that there's some bias there, that there's absence bias in the data. This actually does this does link to the question that I was kind of that was posted and I can't. I think it's kind of got deleted now, but there's a very simple question. I'm kind of perhaps a kind of deceptively simple question for the depiction of art historians which was about, do you do you cover independent art historians as well as people with institution affiliations, but out of that you can sort of gloss that I think well how do you define an art historian I mean how are you creating your data set in the first instance. We come across this question a lot, and actually we have a flexible definition for groups that were excluded from the Academy in many cases, we look at their effect on the public their profile. Generally we say if you published something that was art historical, you fit the first definition of what an art historian is and for people who were again we're prevented from from doing much more on that that's good enough for us. And the dictionary, I should say that the the selection of art historians is not our own it's not our favorite art historians. We began with the four major bibliographies coltormon and and Bethan and things like that and then as we wrote those entries, the people who were mentioned built it in that way so, while we have adjusted for the the minority fields, and basically itself generating. Yeah, because it's so interesting here also with Shane where you're changing the criteria of how you create your data set can completely transform the overall historical picture. I understood what you was that you're saying. I'm correctly. There's hands is going to stand up and then react. I just want to add one small thing to that Martin.  You know this this is not a new art historical problem. If you look at Foufflien's principles of art history, you will find a shocking number of paintings from the Alta Pino Catech in Munich, why because that's walking down the street what he could see. So that was his data set, or at least obviously he was working more I'm exaggerating but so this is not a new problem that we have to deal with. And I think it's always going back to Shane's comments it's about well, this is now a fundamental part of our method just like knowing which, which access we have to which collections. And I think I think what I was going to ask is very much pointing into the same direction because I was also struck by Shane's approach where, you know, drawing on completely different data sets changes the parameters substantially. And that's exactly what I was to saw as an interesting question for the dictionary. Because. Well, there are many art historians who literally have left probably no paper trail British art historians for example, who even taught at universities, but who have for example you see a criterion Lee, who have never published as an art historian, simply because that was the way that you know academia was structured at the time. I find it very interesting there to think about the frameworks that we impose on our work by the by something as simple as saying we're doing a dictionary, which by definition is of course based around prosop prosop graph prosop graphic approach right that we that we do look at individuals where we have at least a certain amount of data. And that's because you could of course also source thinking about mass data, all the names from course syllabi or even student lists. People who in depending on your definition also count as art historians, but as I encounter in my work quite quite regularly are virtually, you know, not documented in other in any other meaningful way. I'm really interested in how the kind of the type of the type of publication that we're tackling, for example dictionary also imposes a certain. Yeah, selective approach as to what data we mine primarily, and it's very interesting, I think to think about how to push the boundaries or something like a dictionary towards people that are literally undocumented, which do you have a lot actually of artists, you know, where it's just say a name, and then work around 1739 or something like that. And to find an art story in certain parameters, I mean to finding an artist is completely fraught almost by definition. Is it about self identification is about exhibiting is about making a living as an artist.  I was just going to go back to the main question. And basically, my data set anyway, and I'll go into this in more detail about multiple scale methodologies. And my data set is based on stratified something cross multiple institutions and resources for the exact reason that I want to mitigate previous problems of the data collection and with institutional record looping. But I have created an extensive appendix, I don't expect my examiners to read so that if anyone wants to use the data set in future and I really hope they do, they can go back and critique expand on it. My, my data set is representative is certainly not exhaustive. And I think that's, I think it's really important that we bear in mind that whilst we're talking about these projects, and that they can keep, we can keep getting more resources, sorry, more information from them. That actually they can all start into linking and we can start seeing things from different perspectives, but only if we question how that date has been gathered in the first place. There are a couple of questions in the Q&A, one of them I can see Sarah Turner is answering live it looks like, which is a kind of technical question about the Chronicle 250. But there's a question again for the dictionary about historians, but I guess it does kind of connect in with this issue about classification and categorisation is from Laurel Zuckerman. Does the dictionary distinguished between Jewish and non Jewish exiles from Nazi Germany, for instance categories for Holocaust victims or Nazi party members. That's a great question, but I'll see. Yeah, and I think it does we don't have field for religion, because it's tricky in many cases, but if you're interested in this you can do a keyword search with Vennlon and je w which gets Jew Jewish, whatever like that. That should be a pretty discreet list, at least from Vennlon of who the Jewish Holocaust victims were and if you don't get that and then the Vennlon one. Those would probably be people who were of other either religions or leftist as Paul said. That's a good example of how the dictionaries itself of kind of jumping off point so we went back to Vennlon and we pulled out Jewish non Jewish, in order to make that distinction in our maps, which then can lead back to questions of the actual say Jewish art historians who are in then the dictionary itself so it really is a two way street there. Yeah, it leads you into tension kind of fraught questions about identification self identification also struck, you know, you've got to get a fairly binary gender classification system and play and of course that's there can be questions about that in terms of perspectives on identity and self identification and self understanding. I shouldn't say that the dictionary has multiple identification fields it's not just binary. Well, I think it was male female or other wasn't it, or male female or not known, I thought was all the. But that's that's what that is to say that there's a whole range of ways of searching. Okay, do we have, I think something else can just come in from Sally Woodcock. Apologies for a rather nuts and bolts question. So, in this context, if, if, if any context that you need to apologise for nuts and bolts question in the early 1990s. I used a relational database called idealist which for its time had unusually sophisticated search mechanisms, but also turned out to have dreadful data exporting importing. It is long obsolete, but I still readily use it for a data set of about 9000 artists. I've been trying to find a replacement that is available offline picture stores where I sometimes work usually have no internet access. I did look at air table mentioned by the speakers, but it appears to be online only. Can anyone suggest a database that is capable of holding large text fields and working with dates and numbers which we doesn't always need an internet connection. Many thanks. It's interesting. I'm going to make some point as well about how dependent we are on online resources as well now. But Rian, you have your hands straight up as if you may have an answer or response. Yeah, well, if my, I used Microsoft access for mine, it does require you obviously importing all the data and the relationships. But it was quite straightforward. I mean, it basically I do use mine online because I use my remotely through my university because I use a Mac and access isn't available on Mac. But that worked pretty well for me because I had a similar concern that if I was too reliant on the internet, would I would I be caught out. And also, would I ever be able to download the scale of the data set. This is before I knew about all the tech available. And it was also free for me to access. So yeah, I'd recommend it does have it. Sometimes it has its limitations, but if once you know, get familiar with it, it's great. And my university anyway provided a whole handbook on how to build my data set and gave me training sessions and stuff. So I always want you to pass on that information if you need it. I'll hand it as well. I think about your hand. Yeah. Yeah, that's, I think that's all excellent. Just to speak to an alternative to air table there's an open source project called base row and I'll put the link in the chat that has created something that is very similar to air table and because it is open source you can choose whether or not to run it online or actually just host it on your own computer so if folks are interested in alternatives to air table that is there. After that comes over comes a more more general question for the for the panel, particularly the panel who have panel members who are primarily art historians or historians by training which is how do you get the technical know how to produce to generate this these data and these visualizations it. I mean, is that is that access to training. Do you just kind of find your own way. Through a university, they have a remarkable amount of training available for PhD students. And also, I think it's about showing initiative to walk across campus to, you know, the computational areas and it actually allows you to take different subjects so for example you can take. I actually think it should be compulsory for art historians in first year to be able to take undertake some kind of computational software learning. The challenge that we have in our art historical discipline is that the professors look at something and press print, and you will have been into many a professorial office which is full of books and lots of drawers with files in it and so that older generation is. And the, and the idea of the commiser is not linked to using digital technologies as part of the research. But yes, within an educational institution there's a remarkable amount available. There's also quite a lot online if you're proactive. I was going to say Duke University Libraries has a data visualization lab that anybody can come in and play with the software they don't do the projects for you but they have experts there who can suggest software and show you how to use it. Yeah, just I was similar that I had to do a lot of cold calling. It was exhausting in the first year because I had my idea for my methodology and I didn't want to compromise my methodology by not being able to access the resources and again I mentioned this in my talk about the fact that I didn't have funding to build software. I had to use everything that's free and available remotely. Sorry, my dog wants to say hello. And so yeah, I had to physically, in some cases, go to other members of staff in other departments and beg for their time and obviously they're stressed in their own capacities. So I went to geography, for example, for a 3IS support. But also, I went to other institutions like British Library, Biggie Labs, and again just begged for help. I was not one of their funded students. So I think it would be really interesting later to consider what resources we can pull or pull together from these discussions to ensure that other students don't have to be in such awkward positions because I found it quite intimidating to go into a sort who is new to these institutions to have to go and beg, borrow and steal for these resources. But then once people got through the door, people were more the way to help. Great.  Yeah, I just wanted to add briefly, at its most basic level, obviously engaging with these methodologies is no different to engaging in any kind of new area of exploration, but I agree with what you're saying about it being more intimidating. And I think that what Paul Wilson was saying about this idea of starting small and doing it incrementally and taking it step by step is using the, as Rhianne said, the kind of resources that are initially available to you online. I think this is a really effective approach to getting to the more complex things because when you see these mass visualizations initially, there is a certain amount of intimidation about the menu. It is, you do struggle to conceive of how you get from where you are right now to do much more complex kind of visualization. And but I think it's the same kind of slow intellectual building that we do when we investigate any new area, just that it's most basic level. So, yeah, you have your hand back and I've got a question for Shane who might come back there. Yeah, absolutely. So I'm not an art historian. And I will just say that one thing I would emphasize here too is collaboration and seeking out partners and that can look like the kinds of consultations that have already been described right universities that have those kinds of resources available. There are also plenty of workshops and institutes that are held around the world, some online some in person, and some that have funding schemes that are great, both in terms of of gaining skills but also in terms of building community and looking for for potential collaborators and that that's something that I highly encourage folks to do. Okay. There's a question posted here for Shane. I think looking at the time this made for us to close for this this session but is posted by Lena Kraus and question for Shane. Could you tell us more about your methodology to gather your data. Did you automate some of the extraction from the digitised reports. That's an excellent question. Unfortunately, the OCR on the digitised reports and the nature of the way that they're laid out meant it was kind of not possible to extract them in any kind of efficient way. I had to go through them each individually and pulled them out. And a second impediment to that was that every time a new postmaster general is appointed, they seem to decide to reformat the report so, and that's part of the reason why some of the data drops out in 1913. There was nothing sufficiently standardised about it to enable me to even start thinking about how I might do it at scale or using anything other than the most rudimentary approach for that, fortunately. But thank you for that. And I think it nicely illustrates how already today we're getting that sense of how these kind of details of the archive, the detail of methodology can actually open up and kind of can transform your methodology in quite dramatic ways. That as well as that dialogue between between different methods kind of established methods and new methods which are facilitated by technology. And we are running up to our break, which I think is that kind of, it's not billed as a lunch break, because we're not providing you with lunch, but it's that time of day, I think. And it's been such a fantastically rich and stimulating, stimulating session already with such a range of approaches and questions arising out of it. So the conversation is going to continue with this afternoon's panel and then with Hans contribution towards the end as well, which is going to I think offer some more kind of historiographical reflections and conversation coming out of that. So, for now, I think you could join me in thanking through our virtual applause and various expressions and gestures. I'm thanking all our panellists this morning Anita and Paul, Shane, Hannah, Paul and Lee. Thank you so much. And we will return at 215. But for now, round of applause and do check the chat as well. There are various notes and suggestions and links posted in the chat for your reference as well, but otherwise do enjoy your 30 minute break or so and we will see you back in the room at 215. I've sort of half joked before I'm sure that sure there must be some way of getting sandwiches delivered to people's homes during breaks on online sessions, but we have to do a menu of options, and we feel like breakfast options for the US. Of course. Yeah, time zones. Australia, I know a need for Anita and Paul, I think it's some pretty unearthly hour. Anita, what time are you on Tasmania? It's quarter past one in the morning. Oh my goodness. I think that's like Horlicks or Martini, depending on your body. Hold the sandwich, send the, at this stage, probably sugar. Yeah, really chewy black coffee. That's great. No, I have to say things like this is certainly one of the silver linings from Ovid. Oh, I don't be able to connect up around the globe like this. Yeah. Different world. Next time you have one of these events, have Anita show you some of her other words. She's done some really amazing things with Neo4j. Well worth a look. That's collaboration in action, isn't it? But do keep posting. I see there's quite a lot posted in the chat. I think people do take note, and they really appreciate chance to see any links, anything that you've posted, anything that you put up. Anyway, people coming back in. Well, I think I'm noticing it's 215. So, and it looks as though we have most of us back from our break. I hope everyone had a good, a good kind of refreshing, tasty break and lunch, and it's nice to see you all again. So we're kicking off our second panel in the mass data methodologies workshop today. My name is Bailey Cards and I work at the Paul Mellon Centre as an editor of open access online publications and some of those have been mentioned already such as our journal British Art Studies and as Sarah said we really see those publications as kind of testing grounds for exploring how work using these kinds of methodologies can can be shared and communicated so it's been an amazing workshop so far and I'm really looking forward to the series of papers that we have coming up. I've been told that I don't need to run through the kind of zoom housekeeping again, which is great but just for anybody listening just to be aware that you can share questions at any time through the Q&A button. You also can share messages in the chat and those will be visible to everyone and you can just kind of comment on that at any point. And you can also raise your hand if you would like to be called on to ask a question for yourself. I'll dive kind of right into our first paper and introduce our first speaker. So we have Rian Addison McCreiner, who is an AHRC collaborative PhD students between the University of York and Tate Britain, and was formerly curator of historic fine art at the Whitworth University of Manchester. She will be sharing her paper, multi-scaler mixed methodologies, rediscovering landscape artist studios, and just to note that's a different title than we had listed on our event, which I think isn't correct, but that that is the correct title so thank you, Rian, take it away. Hi, thank you. And, sorry, I'm just re-gilling screens to make sure I can see everything. So my thesis indoor space is spout or mines, landscape artist studios 1780 to 1850 explores the irony that all landscapes were being painted in urban London. The outside world is being created inside the limits of the room. When a scholarship is considered the studio con galleries of landscape artists is being predominantly through monographic photographs focusing on less than 20 artists, replacing the wider landscape artist population and skewing our understanding of their production of landscape paintings and how we interpret them today. To have a clear understanding of the landscape artist's urban experience, I applied multi-scaler mixed methodologies starting with the landscape artist community on a macro level and metaphorically speaking zooming into the studio. I would discuss the multi-scaler methodology a little later, but first I want to focus on the mass data aspect, which formed the foundation for my research. So why use mass data methodologies that some people have already touched on it already. As Martin Myrone and Diana Greenwald have each discussed, mass data sets will consist of art or artists which are not perceived as masterpieces or did not attain celebrity status, having their presence provide a wider social context, and thus are historical context. Arguably the greatest social context that these sets provide is a seemingly more democratic, non-hierarchial overview of artists' populations. Paris Spice Gans has argued that this requires a willingness to use qualitative data and qualitative data approaches to address the idea that a canon is flawed when it fails to truthfully represent the period it aims to embody. Thus, in order to have a more accurate understanding of the landscape community, I needed to create a database which set aside previous scholarship, step back to look at the breadth of the community, and return to primary evidence where possible. For a macro perspective of the landscape artist population, I built a database and GIS map, which you can see a brief overview on the screen here. The database holds details of around 2,700 landscape artists at around 6,550 addresses. This data goes well beyond the numbers that current scholarship implies, not only establishing where the studios were located, but revealed how prolific the genre was and exposed a neglected artistic community. Using stratified sampling, that is a stripped set of parameters, I created the data from exhibition catalogs and London directories, which listed artists' addresses. Using stratified sampling allowed for cross sampling of the data from multiple institutions, which could be compared to provide more comprehensive data set and clarify errors and contradictions. It also mitigated but does not eradicate institutional biases and resets the reliability and validity in the data. And it also allowed, as we've mentioned before, crossographical analysis, reconstructing the landscape artist community using accumulation of scarce data. In short, a stratified sampling ensured that whether previously overlooked are successful, professional or amateur by their own definition, each person sits equally in the database as a landscape artist. Critically embracing advanced technologies has allowed for the social interpretation of data which is not possible in previous scholarship. This included identifying clusters of addresses, the proximity of landscape artists to one another, and making queries by location to show the geographic significance of the urban studio on the creation of landscape work. So, I'm now going to show you a brief video demonstrating the database and map. I've deliberately pre-recorded this to hopefully ensure that there's no lag or anything. So, here we go. The database was built in Microsoft Access, which allows for the creation of different tables, which form relationships, as well as being able to explore the data in statistical analysis. You can see an overview of the six tables that each link according to a key field. So, for example, every artist's address and source is designated a unique ID number to prevent duplications. This means that an artist can have multiple addresses and each dress can have multiple sources. On the left panel, you can see six tables. From these tables, I can then create queries which are listed below. Queries draw on specific fields to create another table which you can then use for calculations of filters depending on your question. The table 1, artist, holds immediate information about the artist that our unique identifiers. Table 2, artist addresses, allows for artists to have multiple addresses, their unique ID number corresponding with geotagging on the map which I will come to. Table 3, address dates, allows us to track the order in which artists were at any given property and accounts for the returning to addresses on multiple occasions. The other tables 4 and 5 are a bit by institutions and suppliers and table 6, sources, does exactly what it says on the tin and was essential for clarifying contradictions in the data. It also ensured internal validity that someone else could replicate the same findings in the future. Let's look at an existing query. Here, what I'm about to bring up, are the London addresses for John Constable, which I extracted from a case study. His artist ID is the number 105, which is linked to multiple address IDs. The address dates have also been pulled through to the query, allowing me to filter in order of occupation. I went to the query further. Constable lived it on Charlotte Street, Fitzroga, twice in his career, firstly in number 63 and later in number 35. To establish his interactions with other landscape artists, I created queries to the street and for the two separate time periods. So here, for example, we can see the other landscape artists living on Charlotte Street when Constable's living at number 35. Caution must be applied however. This is where technical and historical knowledge is required to interpret the data. Although these artists are listed on Charlotte Street, there were numerous iterations of Charlotte Street throughout Fitzrovia. So if we cross-referenced these artists with the accurately mapped ones in Fitzrovia, it filters down to considerably less as we shall see in a moment. So whilst the database contains over another hundred artists on the Charlotte Street, we can't be certain that this is the correct Charlotte Street. Oh dear, I apologise, I've just messed up my own video. Start that again. I built a map using open-source software QGIS. The purpose of the map is to be able to, in the picture in my son background, the purpose of the map was to be able to process the vast number of addresses and place them in a visual context known as a meta-image. This allows for a greater understanding of the locations of landscape artist studios and their proximity to institutions, other locations, and provide real-life information such as walking times between locations. Here is an overview of all the landscape artist's addresses between 1780 and 1850. My main source for this thesis is Richard Horwood's Plan for Cities of London, created from 1792 to 1999. Horwood's map is the earliest to list house numbers in London, thus allowing the marking of the good locations to be as accurate as possible period. The metropolis expanded exponentially between 1799 and 1850, so I overlaid VR Davies' 1843 map of London to create a visual parameter of the city. I also used OpenStreetMap, so that's VR Davies in a bit more detail, and I used OpenStreetMap for a 21st century perspective. Horwood's map was georeferenced by several other people, I can't take credit for that, using surviving monuments and road junctions to align with the real-world map. Although the alignment is detailed, we must consider that multiple layers, which have been hand-drawn, will contain elements of human error or artistic licence. The maps have been digitally walked to accommodate the slight variations from the modern OpenStreetMap, so the location of the StreetMap on Horwood's map may be 50 metres west of where it is in real life, for example. On the left panel, you can also see the data files. The map works in tandem with the database. Each layer of the data points can have an attribute table attached, which in my case contains a brief amount of information which corresponds to the database. So, for example, this is all the addresses of female landscape artists, and it should bring up the attribute table now, which includes their address ID and artist ID. I then imported additional information from the database such as their names. As for the database, you can use filters to ask queries and save them on different layers. You can filter through the data or select points on the map. So, for example, here I'm going to show how I can select using a polygon around Soho Square. Or, apparently my brain's working faster than my video. So these are just the artists highlighted in yellow. Or here I can construct QGIS to build the data according to the density of a population using HeapsMap. It was also possible to go back to the database and extract more information about the addresses and build an understanding of relationships with where the artist lived. So, here are all the artists for whom it was clear they lived on Charlotte Street for Trobia, which I'll explore again more in a moment. Now to the realities of executing such research. I've thoroughly enjoyed collating data and exploring research threads that I've never imagined would emerge, however, I face a lot of challenges. I face objections from within my own department as to whether the use of mass data was art history, but rather sociology. As Martin, my role has argued in making modern artists. Martin was formerly my supervisor, by the way. Art history does not exist without sociology and such a social context can be facilitated by large scale datasets. When I started, five years ago, I found there was a disparate information on how to collate the data. But since then, huge technological and scholarly leaps have been made with the likes of layers of London being open access and the publication of the Routledge companion to digital humanities and art history. There is the challenge of actually interpreting the data, which is why it's important to have a fully functioning platform. For example, I had to pay around for a lot of time with Microsoft access as to how to filter things by time period and the technical and historical knowledge to interpret that data. Initially, I struggled to find training and technical support, but I did manage it in the end and I needed training to use the likes of Python, OpenRefine, Microsoft Access and GIS platforms. I was probably too ambitious to be honest. A big challenge was executing the research with no project funding, but I adapted my approach so that I only used free open source software. However, in their present format, neither the database or map are publicly accessible. Because mass data hasn't been embraced in art history at all recently, projects are often on different platforms making comparison more challenging. Or they have different criteria for creating data, and thus aren't strictly comparable. So it would really be interesting in seeing how we can work together to draw our projects to make comparisons. Whilst these may seem like negative points really, I'm highlighting them because I'm so enthusiastic to iron these things out for other people. So mass data was only one third of my methodology, providing a macro perspective of the landscape artist community. The database could not account for the context of artwork's creation or production or its cultural significance. To redress this and balance the second stage of my research that zooms in on a meso level, that is looking at a cross-section of the landscape artist community through four case studies, John Constable being one of them. The third stage of my research zooms into the micro level of the studio, using qualitative material to understand the activity that's put within and moulded the landscape artist studio. Adopting a multi-scaler mix methodology was beneficial for three reasons. Firstly, the cross-sographical approach, using the scarce amount of data cumulatively. The second part of the significance of location was retained at every level of analysis. To keep the locations in relation to landscape production at the forefront of my analysis. And finally, the mix methodology allowed me to simultaneously ask questions of the overall landscape artist community and the individual experience. The macro data would just be statistics if it wasn't contextualised by the qualitative data. Likewise, the macro data provided an overview of the landscape artist community. The meso and micro analysis would have mirrored previous monographic scholarship, which provides an limited context for the wider production of their works. The findings of my research devise institutional narrative that there are only a handful of successful landscape artists and has revealed different communities within the landscape artist population. Particularly female landscape artists who scout and feature in literature. In short, this methodology provides an enhanced macro view that demonstrates the possibilities of alternative narratives that can be drawn from our state and GIS resources. So I'm now going to briefly present a case study to demonstrate the value in the multi-scaler methodology. Charlotte Street Fitzrovia has always been noted as a popular street for artists, however there's never been a macro analysis of who passed through the street or the significance of the genre. I've mapped 23 landscape artists who resided on Charlotte Street. Charlotte Street offered cheap rooms, which were ideal if the landscape artists were visiting for the exhibition season. However, at the other end of the scale, artists who were well provided by independent means such as Joseph Browning would build extensions to accommodate their practice. Browning lived at 35 Charlotte Street for 39 years, which was highly unusual for landscape artists. So the data collection was really important for reflecting the reality of how landscape artists typically occupied numerous lodgings in London throughout their career. Situated almost opposite Baratyn's property was number 63. The first landscape artists we are aware of lodging here is George Moreland. Moreland's notorious lifestyle of drinking and debt was prevailed in art history, art historical memory, more so than art. By 1792, Moreland was legally bound to pay his creditors £120 per month to consolidate debts and was housed at 63 Charlotte Street. It was described as an elegant house with a garden coach house of stables. When Moreland occupied the house, there were four of the landscape artists on the street, including Francis Wheatley, with whom Moreland had published numerous prints throughout his career. In the same years movie he did to Charlotte Street, Moreland exhibited an astronomical 15 works at the Royal Academy. He reduced his canvas sizes to increase productivity, but he's made some exceptions to attract attention. The benevolent sportsman pictured here was one of the largest works ever painted at Charlotte Street and was reportedly executed in about a week to ensure it was ready for execution. Bit of an exaggeration I can imagine. 17 years later, in 1811, John Constable would move into number 63. Whereas Moreland had had the whole property, Constable leased rooms on the first floor above the cabinet, make it an post for a rich weight. Constable requested that the back drawing room would be painted salmon with crimson post of furniture. Constable himself was a different batch show that all would assist upon it, reassuring himself that the colours will suit the pictures that he wanted to display. Constable's studio was the first floor front room, likely the space used by Moreland, which had the slightest barter from northeasterly light. Here he struck to the studio to be painted with a sort of purple brown from the floor to ceiling, not sparing even the doors or door posts for white is disagreeable to a painter's eyes near pictures. Arguing Constable's wall colour was strategic to create a neutral environment to not influence the palette of his works. However, in February 1814, he was struggling to add warmth to a cloud scene in Suffolk. The studio would not have had the ideal lighting conditions due to the combination of purple brown walls and the London winter. Unable to resolve the issues in the compiles of the studio, Constable pledged to prepare an on-plane air oil sketch from then on. There are no surviving images to see if it would be Charlotte Street or its interior, but fortuitously, the landscape of artists of figure painter William James Moeller drew the inside of his studio of 22 Charlotte Street. On the east side of the street, Moeller's first floor room studio would have had less light than Moreland Constable. In this confidence sketch here, Moeller places emphasis on the context of the room. Although it was likely that Moeller had several rooms to live in, this sketch shows the limited studio space an artist of Charlotte Street realistically had. Though this is a very brief and focused example, the database and map reveal relationships and circumstances that would not otherwise be possible without mass data. However, the data was only contextualised as part of the multi-scaler mix methodology that allows us to understand how a location could shape a landscape painting. There are far more questions that could be asked of this data set. If we consider the number of landscape artists identified in the database, around 2700 exhibiting over a 70-year period, then it's possible to imagine the burgeoning activity that was occurring within our studios and how many more are waiting to be uncovered. I designed this research to have a life well beyond my thesis. My methodology was designed to ensure internal validity. That is the process that can be replicated to generate the same results, as well as external validity, so the methodology can be applied to artists' studios of other genres to allow for direct comparison. I would like the database and map to become publicly accessible and user-friendly. The data itself will also be contributing towards collaborative research such as the British Art Network's Mapping British Women Artist project. So, looking to the future, mass data could be utilised to look across time periods, artistic genres and material practices to name a few. But I would argue that embracing multi-scaler mixed methodologies and advanced technologies are essential to art history if we were to expose forgotten artistic communities from little surviving material. I welcome anyone to get in touch to discuss my methodologies and resources I've used. Thank you. Thank you so much, Rhian. That was so interesting, and I think it speaks really brilliantly to some of the questions we had in the first panel about the kind of nuts and bolts of how people are working and connecting it through to specific examples of mixed forms of analysis. So, yeah, thank you. And it will be great to discuss your paper more in the Q&A. I'm going to introduce our second paper now, which is being given by a team of researchers who have very cleverly coordinated Zoom backgrounds. I'll introduce Ming Tiampo, who is Professor of Art History and Co-Director of the Centre for Transnational Cultural Analysis at Carleton University. Penzi Atta, an Egyptian-Canadian visual artist, curator and researcher living and working on the unseeded territory of the Algonquin Anishinaabe nation in Ottawa. Yanake Van Hoove is pursuing her MA in Art and Architectural History with a specialisation in Digital Humanities at Carleton University and Maribel Hidalgo Urbanecha, who is a Worlding Public Cultures Postdoctoral Research Fellow at the Chelsea College of Arts at the University of the Arts London. And their paper is Mobile Subjects Contrapuntal Modernisms. Thank you so much, Bailey. Are you able to hear me and see me? Hello. Yes, we can. First, I just wanted to thank the Paul Non Centre, Sarah Turner, Martin Myrone, Danny Convy and Bailey Card for pulling this all together. It's really such a pleasure to be in this context. Reflecting the importance of collaboration to data-driven work and also to global art history from a pluriversal perspective, which I call Worlding, we are presenting in a team of four today, although we're just four of a much larger team, which represents the digital side of these much larger collaborations. So I'll be speaking first, followed by Panzi Atta, then Yanake Van Hoove and finally Maribel Hidalgo Urbanecha. Mobile Subjects Contrapuntal Modernisms investigates the circulation of artists from the decolonising world through the colonial and artistic capitals of London and Paris. This tale of two cities considers how these capitals of decolonising empires functioned as critical meeting places, anti-colonial hubs and sites of exchange in the decades after World War II due to post-war mass migration. It considers how these mobile subjects, artists from Asia, Africa and Latin America, relocated to the different immigration contexts of London and Paris facilitated by scholarships, educational opportunities, exhibitions and journals. It furthermore seeks to make visible the transcultural artistic practices, communities, networks, discourses, solidarities and worlds that artists co-constituted through, between and in these capitals, as well as their impacts on regional histories of modern art. The purpose of this project is to propose a new analytical model that sees metropoles not as points of origin or as global training grounds for artists from the global south, but as spaces of intersection and flow that allow us to understand the transnational condition of modern art and the co-constitution of modernism. In order to do this, it is not enough to rely upon the traditional methods of art history, nor on any one individuals into the expertise and data sets. So here, as Paris Spice Gans argues about women artists in the 18th and 19th centuries, it is necessary to demonstrate that these artists from the global south in London and Paris were quote, exceptional, but not exceptions. The project thus uses data science to connect scholars and their existing data sets through a collaborative database and workshops that map and investigate the presence of overseas artists in London from 1945 to 1989 and Paris from 1900 to 1989. Working collaboratively, and our team is much bigger than just us, we are experimenting with artistic visualizations, engaging with issues of decolonizing data ontologies and database structures, and are now working to bring it all together in a critical database and data visualization, which we will demonstrate later. As Diana Sieve-Greenwald argues in her book, Painting by Numbers, data science is only one tool in our analytical toolbox, which our team uses as a starting point for art historical and interdisciplinary methods that range from formal analysis and slow looking to postcolonial and decolonial theory, global micro history, global urban history and worlding. As one part of our larger methodology, data provides a macro view, something that we've heard many times today, allowing us to zoom out to see patterns and connections before zooming in again to look closely with fresh eyes able to see beyond the disciplinary assumptions of art historical inquiry. And this is very similar to what Rhian just presented earlier on multi scalar mixed methodology, so I think I think we have lots to talk about there. So what I'm showing you here in the slide is an example of what our colleague and team member Michelle Greete did with her website and book Transatlantic Encounters Latin American Artists in Paris between the wars and so she has a book, which is much more traditional piece of art history, and also this website which I'm showing you on the right hand side. I'm here is a similar pairing of more traditional scholarship and digital methods in a collaborative article and archive feature Slade London Asia that I created with Liz Bruchet in British Art Studies. Now I'll put the links in the chat later. I'll be using a large data set as a starting point. The article in British Art Studies I'll address large movements and trends and also zoomed in to explore micro histories, such as what the ways in which artists from decolonizing contexts went on to establish and consolidate art institutions in the global south in critical dialogue with their time at the slate. I think we have all of the smaller micro stories the micro histories that we presented in the context of this larger macro history in British Art Studies. So now I'm just going to hand it over to my colleague pansy. Over to you. So when we when we that we've sought to visualize these concepts is through this animation, tracing the movement of international artists to the Slade School of Fine Arts through the mid 20th century. Unlike the standard Mercator projection, which is typically used to visualize the globe. This map is centered on the Pacific rather than on Greenwich. This draws attention to the global spam of these artists networks shifting London from the center to the margins while keeping the relationship between the continents familiar enough to remain legible. At the same time, geopolitical borders are rendered as organic porous indeterminate charcoal lines, making visible a semblance of their social and political presence, as well as their volatility over the course of this time period. Similarly, the paths denoting the artists movements are sketchy indefinite multiple their lines produce a geographic and temporal network that is transiently interlinked through sharp digital artifacts that branch out across the maps clusters. Animated using blenders open open source robust tools, each of the paths visual characteristics, their opacity texture their visual noise can be generatively mapped on to the data sets variables encoding seemingly decorative visual qualities with quantitative or qualitative meaning. Accordingly, this work reflects and reproduces the project analytical methodology. While we know the artists national origins and their destinations, we cannot be certain of their routes for the multitude of possible connections they may have made along the way. The materiality of this visualization puts into practice a decolonizing approach to mass data methodology by making space for multiplicity and uncertainty in analysis of the global, telling a story about the numbers that accounts for the indeterminacy of the human. And now over to you know this work. Thanks pansy. Okay, so what we're looking at here at its core this network model shows approximate connections that existed between racialized artists beyond this the model can help us identify new insights and evidence. It can help us identify blind spots as described by Johanna Dricker in our data, particularly in areas that have been historically overlooked. My view does not tell us much other than reaffirming the fact that there was a massive network of non Western artists active between Paris and London throughout the 1900s. The colors are coded by country of birth and there is a dot or node representing each artist in a second node connected by a line for each connection point or institution exhibition or gallery that an artist was involved with. There are 3073 artists in 2993 connecting lines within this network. Next slide please make. So when hovering over an artist a box will pop up with more detailed information. We are mindful of the gaps in our data, as indicated by the question marks beside the fields that we do not have information for. From here we can also identify some major clusters within our data, including was it a international and slate school. Next slide please. And we're playing a video here without sound. So to develop a better understanding of the stories buried within this network, we need to dig deeper into the layers of further stuff we have taken us to prepare a second version of this network with a time based animation to visualize how this web of internet has fluctuated and evolved. As you can see the network builds over time with new connections each year being represented by the colored lines coded by country. We could also isolate a specific timeframe or country of birth using filters, forcing individuals connections changing over time within this network and I'll give it a minute to finish playing here I talked a little bit too fast for this video. Connections are still building over time and eventually we're going to end up with view that shows us at the end of the 1900s or the midpoint. The amount of connections within our network. Okay. Next slide please me. Another filter we can use to dig deeper into this data is filtering by specific connection point. So this static network includes the same information about artists who studied at the slate school that pansy just presented in her animation. We can see all the artists who had an affiliation with this late school over a span of roughly 80 years, which still does not tell us much about the possible connections between artists. Our next steps will be to create more flexible code based models of the data that resonate with the uncertain and inconsistent nature of the histories we are working with this data contains gaps, ambiguities and multiple meanings that situates these histories in a fluid rather than final position over to Maribel. Thank you, Jenica adopting a quantitative methodology and in our case, a response to the need of a question in stereotypes and assumptions, which is a principle that guides data centric digital humanities methods. And these approach answers questions that require evidence of a different scale of complexity than traditional our history challenges monographic studies. But in addition to this key to our project is a critical intervention of our methods. The dialogue with the decolonizing data research cluster of the word in public cultures project mobile subjects contra puntal modernisms is engaged in a critical interrogation of data centric methods. If you're curious about the word in public cultures projects and and also about the work we've been doing and around the seed oxyram ontology. You can visit the two links on the slides. Critical nature of our approach is leading us to up to adopt post colonial and decolonial digital humanities strategies and practices that respond to the fact that, as Rupika reason argues, cultural canons are being reproduced and amplify not only in the visibility and discoverability of knowledge, but also in a post demologies of digital knowledge production. The adoption of well established and universally recognized methods that are often seen as neutral by the white portion of the community is considered good practice as it facilitates a change of information but the truth is that the community has reinforced 16 power dynamics and knowledge biases. So in response to these issues and quoting Rupika's reason work again post colonial approaches to digital humanities intervene gaps and omissions by engaging with the politics of representation and look beyond representation to develop design practices that lay bare the politics surrounding digital knowledge production. Accordingly, our aim is to address gaps and omissions in terms of artist representation, as well as to rethink digital knowledge production and epistemologies. As we develop the project database and data visualizations that accommodate artists as next next slide please. In particular and concerning data visualization, we found ourselves engaged responding to the idea that modeling data into a graphical expression such as a visualization is a form of knowledge production and a form of its design, namely an in connection to the visualizations my my colleagues pancy and Janaka have just shown. Johanna Drucker states that maps are a record of exploration pathic and tactile narrative and immersive when creating in from inside experience of discovery, but rationalized through projection when produced from outside as images. I'm representing a curve form on a flat surface, as well as a many cultural imperialism work, provide their own history within the range of projection methods. And then about network diagrams. I assume an absolute distinction between notes and the edges that connected and their displays organized by algorithms that optimize display rather than following strict interpretation of their relationships between among the nodes. The visual arrangement cannot be read as an accurate presentation of information only as an approximation. Next slide please. I would like to finish our presentation with a reflection on the next steps that follow critic and acknowledgement of the piece demologies and knowledge production. And we'd like to share with all of you the following questions, which are guiding our process of rethinking and redesigning the database and data visualizations model we use and and these questions are. Can we decolonize data centric methodologies. How can data methodologies be reimagined to render complex histories and identities and to surface horizontal relationships. How can we challenge dominant models for data visualization visualization that have been built upon colonial and western epistemologies. I would like to express uncertainty and be with the contradiction and cultural nuances through data visualizations we will really like to have to know your thoughts about these questions during the Q&A on to finalize. We wanted to say thank you to our amazing colleagues without whom this project could have never been developed so thank you very much. Thank you so much for that fantastic paper I see lots of applause. And for those questions which we will come back to and it's really exciting to see all the connections between our first two papers already as well. I'm going to introduce our final speaker, Mary Okin and congratulations are in order because Mary's PhD was conferred today. I'm very pleased to introduce Mary as holding a PhD in the history of art and architecture from UC Santa Barbara. Mary is also a lecturer at San Jose State University specializing in the history of American art and digital humanities. Her paper will be challenging the American art canon with mass data mining at 10th Street visualizing New York City's 10th Street Studio building. Thank you. So good morning everyone. I really delighted to be here. Can you hear me? That's great thanks Mary. So I'm really delighted to be here and my presentation concerns a digital humanities project that I've been developing as a side project my dissertation and it's still and it's kind of nascent stages. Although we've just myself and by former student and unpaid research assistant have just published an article recently and I'll share in the chat with sort of preliminary findings. So I suppose I should say that we're still in search of a method but we've explored some and I hope that'll be interesting for people. So I am a California based Americanist with a specialty in histories of California painting. And my research on California painters has made me curious over time about why it's the case that the measure it seems for determining an artist significance in place in American art history is often bound to whether or not they achieve fame in New York. It seems to be a kind of pattern that you have to have an exhibition, whether you're living or dead artist to be considered important, and that New York is that kind of central place. So this project, which developed out of a graduate seminar where I was kind of, you know, you need to find a data set and explore it has led me to really think about that and come up with at least some answers for the origins of that center of art being New York. The project has also helped me to challenge or explore the single author model so it's lovely to see that among colleagues as well, and to think about, you know, how to be part of a community that is experimenting with data. I've had the great fortune to be participating in the early stages of this project in something called the Tour de Mour Inclusive Digital Art History Initiative that was started by Panorama the Journal of the Historians of American Art. So I've had a chance to work with Diana C. Breedwald and other folks and it's been amazing. So thank you, both to them and to my unpaid wonderful research assistant, Seely Richard. So the 10th Street Studio Building for those of you who may not be familiar with it was the first building specifically designed to house artist studios in the United States. It was developed and built in 1857, and it was demolished in 1956. This visualization