Sadly 2020 is not being a year for in-person parties! However, I hope you'll raise a socially distanced glass safely at home to celebrate the eighth birthday of Wikidata, which first went live on 29th October 2012.

You can follow the festivities on social media with posts tagged #WikidataBirthday and read a message from the development team here. The WikiCite 2020 virtual conference kicked the celebrations off a few days early, with sessions about open citations and linked bibliographic data (videos online here) and depending what time you read this post, you may still be able to join a 24-hours long online meetup, where people can drop in to chat to others about Wikidata.

If you are reading this post and wondering what Wikidata is, then you might want to read this introduction. Essentially it "is a document-oriented database, focused on items, which represent topics, concepts, or objects. Each item is allocated a unique, persistent identifier, a positive integer prefixed with the upper-case letter Q, known as a "QID". This enables the basic information required to identify the topic that the item covers to be translated without favouring any language."^[1]

Many libraries around the world have been actively adding data about their collections to Wikidata, and a number of groups to support and encourage this work have been established.

The IFLA Wikidata Working Group was formed in late 2019 to explore and advocate for the use of and contribution to Wikidata by library and information professionals. To support the integration of Wikidata and Wikibase with library systems, and alignment of the Wikidata ontology with library metadata formats such as BIBFRAME, RDA, and MARC.

This group was originally due to host a satellite event for the World Library and Information Congress 2020 in Dublin, which was sadly cancelled due to Covid-19. However this event was quickly converted into the Wikicite + Libraries series of six online discussions; about open citations, language revitalisation, knowledge equity, access to scholarly publications, linking and visualising bibliographic data. The recordings of which have all been made available online, via a Youtube playlist.

They have also set up a mailing list ([email protected]) and held an online launch party on the 8th October (slides). If you would like to attend their next meeting, it will be on the 24th November, the booking form is here.

Another online community for librarians working with Wikidata, is the LD4 Wikidata Affinity Group, which explores how libraries can contribute to and leverage Wikidata as a platform for publishing, linking, and enriching library linked data. They meet biweekly via Zoom. At each meeting, either the co-facilitators or an invited guest will give a presentation, or a demonstration, then there is a wider discussion of any issues, which members have encountered, and an opportunity for sharing helpful resources.

If you work in libraries and are curious about Wikidata, I highly recommend attending these groups. If you are looking for a introductory guide, then Practical Wikidata for Librarians is an excellent starting point. There is also Library Carpentry Wikidata currently in development, which is shaping up to be a very useful resource.

It can't be all work and no play though, so I'm celebrating Wikidata's birthday with a seasonal slice of Frankencolin the Caterpillar cake!

This post is by Digital Curator Stella Wisdom (@miss_wisdom)

1. https://en.wikipedia.org/wiki/Wikidata  ↩︎

People 'automatically' identified in digital TV news related programme clips.

Guest blog post by Andrew Brown (PhD researcher),  Ernesto Coto (Research Software Engineer) and Andrew Zisserman (Professor) of the Visual Geometry Group, Department of Engineering Science, University of Oxford, and BL Labs Public Award Runner-up for Research, 2019. Posted on their behalf by Mahendra Mahey, Manager of BL Labs.

In this work, we automatically identify and label (tag) people in large video archives without the need for any manual annotation or supervision. The project was carried out with the British Library on a sample of 106 videos from their “Television and radio news” archive; a large collection of news programs from the last 10 years. This archive serves as an important and fascinating resource for researchers and the general public alike. However, the sheer scale of the data, coupled with a lack of relevant metadata, makes indexing, analysing and navigating this content an increasingly difficult task. Relying on human annotation is no longer feasible, and without an effective way to navigate these videos, this bank of knowledge is largely inaccessible.

As users, we are typically interested in human-centric queries such as:

• “When did Jeremy Corbyn first appear in a Newsnight episode?” or
• “Show me all of the times when Hugh Grant and Shirley Williams appeared together.

Currently this is nigh on impossible without trawling through hundreds of hours of content. 

We posed the following research question:

Is it possible to enable automatic person-search capabilities such as this in the archive, without the need for any manual supervision or labelling?

The answer is “yes”, and the method is described next.

Video Pre-Processing

The basic unit which enables person labelling in videos is the face-track; a group of consecutive face detections within a shot that correspond to the same identity. Face-tracks are extracted from all of the videos in the archive. The task of labelling the people in the videos is then to assign a label to each one of these extracted face-tracks. The video below gives an example of two face-tracks found in a scene.

Two face-tracks found in British Library digital news footage by Visual Geometry Group - University of Oxford.

Techniques at Our Disposal

The base technology used for this work is a state-of-the-art convolutional neural network (CNN), trained for facial recognition [1]. The CNN extracts feature-vectors (a list of numbers) from face images, which indicate the identity of the depicted person. To label a face-track, the distance between the feature-vector for the face-track, and the feature-vector for a face-image with known identity is computed. The face-track is labelled as depicting that identity if the distance is smaller than a certain threshold (i.e. they match). We also use a speaker recognition CNN [2] that works in the same way, except it labels speech segments from unknown identities using speech segments from known identities within the video.

Labelling the Face-Tracks

Our method for automatically labelling the people in the video archive is divided into three main stages:

(1) Our first labelling method uses what we term a “celebrity feature-vector bank”, which consists of names of people that are likely to appear in the videos, and their corresponding feature-vectors. The names are automatically sourced from IMDB cast lists for the programmes (the titles of the programmes are freely available in the meta-data). Face-images for each of the names are automatically downloaded from image-search engines. Incorrect face-images and people with no images of themselves on search engines are automatically removed at this stage. We compute the feature-vectors for each identity and add them to the bank alongside the names. The face-tracks from the video archives are then simply labelled by finding matches in the feature-vector bank.

Face-tracks from the video archives are labelled by finding matches in the feature-vector bank. 

(2) Our second labelling method uses the idea that if a name is spoken, or found displayed in a scene, then that person is likely to be found within that scene. The task is then to automatically determine whether there is a correspondence or not. Text is automatically read from the news videos using Optical Character Recognition (OCR), and speech is automatically transcribed using Automatic Speech Recognition (ASR). Names are identified and they are searched for on image search engines. The top ranked images are downloaded and the feature-vectors are computed from the faces. If any are close enough to the feature-vectors from the face-tracks present in the scene, then that face-track is labelled with that name. The video below details this process for a written name.

Using text or spoken word and face recognition to identify a person in a news clip.

(3) For our third labelling method, we use speaker recognition to identify any non-labelled speaking people. We use the labels from the previous two stages to automatically acquire labelled speech segments from the corresponding labelled face-tracks. For each remaining non-labelled speaking person, we extract the speech feature-vector and compute the distance of it to the feature-vectors of the labelled speech segments. If one is close enough, then the non-labelled speech segment and corresponding face-track is assigned that name. This process manages to label speaking face-tracks with visually challenging faces, e.g. deep in shadow or at an extremely non-frontal pose.

Indexing and Searching Identities

The results of our work can be browsed via a web search engine of our own design. A search bar allows for users to specify the person or group of people that they would like to search for. People’s names are efficiently indexed so that the complete list of names can be filtered as the user types in the search bar. The search results are returned instantly with their associated metadata (programme name, data and time) and can be displayed in multiple ways. The video associated with each search result can be played, visualising the location and the name of all identified people in the video. See the video below for more details. This allows for the archive videos to be easily navigated using person-search, thus opening them up for use by the general public.

Archive videos easily navigated using person-search.

For examples of more of our Computer Vision research and open-source software, visit the Visual Geometry Group website.

This work was supported by the EPSRC Programme Grant Seebibyte EP/M013774/1. 

[1] Qiong Cao, Li Shen, Weidi Xie, Omkar M. Parkhi, and Andrew Zisserman. VGGFace2: A dataset for recognising faces across pose and age. In Proc. International Conference on Automatic Face & Gesture Recognition, 2018.

[2] Joon Son Chung, Arsha Nagrani and Andrew Zisserman. VoxCeleb2: Deep Speaker Recognition. INTERSPEECH, 2018

BL Labs Public Awards 2020

Inspired by this work that uses the British Library's digital archived news footage? Have you done something innovative using the British Library's digital collections and data? Why not consider entering your work for a BL Labs Public Award 2020 and win fame, glory and even a bit of money?

This year's public and staff awards 2020 are open for submission, the deadline for entry for both is Monday 30 November 2020.

Whilst we welcome projects on any use of our digital collections and data (especially in research, artistic, educational and community categories), we are particularly interested in entries in our public awards that have focused on anti-racist work, about the pandemic or that are using computational methods such as the use of Jupyter Notebooks.

This is a guest post by Giulia Carla Rossi, Curator of Digital Publications at the British Library. You can find her @giugimonogatari.

In June 2020 the Office for Students announced a campaign to fill 2,500 new places on artificial intelligence and data science conversion courses in universities across the UK. While I’m not planning to retrain in cyber, I was lucky enough to be in the cohort for the trial run of one of these courses: Birkbeck’s Postgraduate Certificate in Applied Data Science. The course started as a collaborative project between The British Library, The National Archives and Birkbeck University to develop a computing course aimed at professionals working in the cultural heritage sector. The trial run has now ended and the course is set to start in full from January 2021.

The course is designed for graduates who are new to computer science – which was perfect for me, as I had no previous coding knowledge besides some very basic HTML and CSS. It was a very steep learning curve, starting from scratch and ending with developing my own piece of software, but it was great to see how code could be applied to everyday issues to facilitate and automate parts of our workload. The fact that it was targeted at information professionals and that we could use existing datasets to learn from real life examples made it easier to integrate study with work. After a while, I started to look at the everyday tasks in my to-do list and wonder “Can this be solved with Python?”

After a taught module (Demystifying Computing with Python), students had to work on an individual project module and develop a software based on their work (to solve an issue, facilitate a task, re-use and analyse existing resources). I had an idea of the themes I wanted to explore – as Curator of Digital Publications, I’m interested in new media and platforms used to deliver content, and how text and stories are shaped by these tools. When I read about French company Short Édition and the short story vending machine in Canary Wharf I knew I had found my project.

My project is to build a stand-alone printer that prints random poems from a dataset of out-of-copyright texts. A little portable Bot-ish (sic!) Library to showcase the British Library collections and fill the world with more poetry.

A Short Story Station in Canary Wharf, London and my own sketch of a printing machine. (photo by the author)

 

Finding poetry

For my project, I decided to use the British Library’s “Digitised printed books (18th-19th century)” collection. This comprises over 60,000 volumes of 18^th and 19^th century texts, digitised in partnership with Microsoft and made available under Public Domain Mark. My work focused on the metadata dataset and the dataset of OCR derived text (shout out to the Digital Research team for kindly providing me with this dataset, as its size far exceeded what my computer is able to download).

The British Library actively encourages researchers to use its “digital collection and data in exciting and innovative ways” and projects with similar goals to mine had been undertaken before. In 2017, Dr Jennifer Batt worked with staff at the British Library on a data mining project: her goal was to identify poetry within a dataset of 18^th Century digitised newspapers from the British Library’s Burney Collection. In her research, Batt argued that employing a set of recurring words didn’t help her finding poetry within the dataset, as only very few of the poems included key terms like ‘stanza’ and ‘line’ – and none included the word ‘poem’. In my case, I chose to work with the metadata dataset first, as a way of filtering books based on their title, and while, as Batt proved, it’s unlikely that a poem itself includes a term defining its poetry style I was quite confident that such terms might appear in the title of a poetry collection.

My first step then was to identify books containing poetry, by searching through the metadata dataset using key words associated with poetry. My goal was not to find all the poetry in the dataset, but to identify books containing some form of poetry, that could be reused to create my printer dataset. I used the Poetry Foundation’s online “Glossary of Poetic Terms - Forms & Types of Poems” to identify key terms to use, eliminating the anachronisms (no poetry slam in the 19th century, I'm afraid) and ambiguous terms (“romance” returned too many results that weren’t relevant to my research). The result was 4580 book titles containing one or more poetry-related words.

 

My list of poetry terms used to search through the dataset

 

 

Creating verses: when coding meets grammar

I then wanted to extract individual poems from my dataset. The variety of book structures and poetry styles made it impossible to find a blanket rule that could be applied to all books. I chose to test my code out on books that I knew had one poem per page, so that I could extract pages and easily get my poems. Because of its relatively simple structure - and possibly because of some nostalgia for my secondary school Italian class - I started my experiments with Giacomo Pincherle’s 1865 translation of Dante’s sonnets, “In Omaggio a Dante. Dante's Memorial. [Containing five sonnets from Dante, Petrarch and Metastasio, with English versions by G. Pincherle, and five original sonnets in English by G. Pincherle.]”

Once I solved the problem of extracting single poems, the issue was ‘reshaping’ the text to match the print edition. Line breaks are essential to the meaning of a poem and the OCR text was just one continuous string of text that completely disregarded the metric and rhythm of the original work. The rationale behind my choice of book was also that sonnets present a fairly regular structure, which I was hoping could be of use when reshaping the text. The idea of using the poem’s metre as a tool to determine line length seemed the most effective choice: by knowing the type of metre used (iambic pentameter, terza rima, etc.) it’s possible to anticipate the number of syllables for each line and where line breaks should occur.

So I created a function to count how many syllables a word has following English grammar rules. As it’s often the case with coding, someone has likely already encountered the same problem as you and, if you’re lucky, they have found a solution: I used a function found online as my base (thank you, StackOverflow), building on it in order to cover as many grammar rules (and exceptions) as I was aware of. I used the same model and adapted it to Italian grammar rules, in order to account for the Italian sonnets in the book as well. I then decided to combine the syllable count with the use of capitalisation at the beginning of a line. This increased the chances of a successful result in case the syllable count would return a wrong result (which might happen whenever typos appear in the OCR text).

 

The same sonnet restructured so that each line is a new string (above), and matches the line breaks in the print edition (below)

 

It was very helpful that all books in the datasets were digitised and are available to access remotely (you can search for them on the British Library catalogue by using the search term “blmsd”), so I could check and compare my results to the print editions from home even during lockdown. I also tested my functions on sonnets from Henry Thomas Mackenzie Bell’s “Old Year Leaves Being old verses revived. [With the addition of two sonnets.]” and Welbore Saint Clair Baddeley’s “Legend of the Death of Antar, an eastern romance. Also lyrical poems, songs, and sonnets.”

Example of sonnet from Legend of the Death of Antar, an eastern romance. The function that divides the poems into lines could be adapted to accommodate breaks between stanzas as well.

 

Main challenges and gaps in research

• Typos in the OCR text: Errors and typos were introduced when the books in the collection were first digitised, which translated into exceptions to the rules I devised for identifying and restructuring poems. In order to ensure the text of every poem has been correctly captured and that typos have been fixed, some degree of manual intervention might be required.
• Scalability: The variety of poetry styles and book structures, paired with the lack of tagging around verse text, make it impossible to find a single formula that can be applied to all cases. What I created is quite dependent on a book having one poem per page, and using capitalisation in a certain way.
• Time constraint: the time limit we had to deliver the project - and my very-recently-acquired-and-still-very-much-developing skill set - meant I had to focus on a limited number of books and had to prioritise writing the software over building the printer itself.

 

Next steps

One of the outputs of this project is a JSON file containing a dictionary of poetry books. After searching for poetry terms, I paired the poetry titles and relative metadata with their pages from the OCR dataset, so the resulting file combines useful data from the two original datasets (book IDs, titles, authors’ names and the OCR text of each book). It’s also slightly easier to navigate compared to the OCR dataset as books can be retrieved by ID, and each page is an item in a list that can be easily called. One of the next steps will be to upload this onto the British Library data repository, in the hope that people might be encouraged to use it and conduct further research around this data collection.

Another, very obvious, next step is: building the printer! The individual components have already been purchased (Adafruit IoT Pi Printer Project Pack and Raspberry Pi 3). I will then have to build the thermal printer with Raspberry Pi and connect it to my poetry dataset. It’s interesting to note that other higher education institutions and libraries have been experimenting with similar ideas - like the University of Idaho Library’s Vandal Poem of the Day Bot and the University of British Columbia’s randomised book recommendations printer for libraries.

Component parts of the Adafruit IoT Pi Printer Project Pack. (photo by the author)

My aim when working on this project was for the printer to be used to showcase British Library collections; the idea was for it to be located in a public area in the Library, to reach new audiences that might not necessarily be there for research purposes. The printer could also be reprogrammed to print different genres and be customised for different occasions (e.g. exhibitions, anniversary celebrations, etc.) All of this was planned before Covid-19 happened, so it might be necessary to slightly adapt things now - and any suggestions in merit are very welcome! :)

Finally, none of this would have been possible without Nora McGregor, Stelios Sotiriadis, Peter Wood, the Digital Scholarship and BL Labs teams, and the support of my line manager and my team.

Looking for entries now!

Nominate an existing British Library staff member or a team that has done something exciting, innovative and cool with the British Library’s digital collections or data.

The 2020 British Library Labs Staff Award, now in its fifth year, gives recognition to current British Library staff who have created something brilliant using the Library’s digital collections or data.

Perhaps you know of a project that developed new forms of knowledge, or an activity that delivered commercial value to the library. Did the person or team create an artistic work that inspired, stimulated, amazed and provoked? Do you know of a project developed by the Library where quality learning experiences were generated using the Library’s digital content? 

You may nominate a current member of British Library staff, a team, or yourself (if you are a member of staff), for the Staff Award using this form.

The deadline for submission is NOON (GMT), Monday 30 November 2020.

Nominees will be highlighted on Tuesday 15 December 2020 at the online British Library Labs Annual Symposium where some (winners and runners-up) will also be asked to talk about their projects (everyone is welcome to attend, you just need to register).

You can see the projects submitted by members of staff and public for the awards in our online archive.

In 2019, last year's winner focused on the brilliant work of the Imaging Team for the 'Qatar Foundation Partnership Project Hack Days', which were sessions organised for the team to experiment with the Library's digital collections. 

The runner-up for the BL Labs Staff Award in 2019 was the Heritage Made Digital team and their social media campaign to promote the British Library's digital collections one language a week from letters 'A' to 'U' #AToUnknown).

In the public Awards, last year's winners (2019) drew attention to artistic, research, teaching & learning, and community activities that used our data and / or digital collections.

British Library Labs is a project within the Digital Scholarship department at the British Library that supports and inspires the use of the Library's digital collections and data in exciting and innovative ways. It was previously funded by the Andrew W. Mellon Foundation and is now solely funded by the British Library.

If you have any questions, please contact us at [email protected].

This a guest post is by British Library collaborative doctoral student Carol Butler, you can follow her on twitter as @fantomascarol.

Update: Due to a phenomenal response, Carol has recruited enough interviewees for the study, so the link to the application form has been removed (13/10/2020).

In 2016 I started a PhD project in partnership with the British Library and the Centre for Human-Computer Interaction Design (CHCID) at City, University of London. My research has focused on the phenomena of fiction authors interacting with readers through online media, such as websites, forums and social media, to promote and discuss their work. My aim is to identify potential avenues for redesigning or introducing new technology to better support authors and readers. I am now in my fourth and final year, aiming to complete my research this winter.

The internet has impacted how society interacts with almost everything, and literature has been no exception. It’s often thought that if a person or a business is not online, they are effectively invisible, and over the last ten years or so it has become increasingly common – expected, even - for authors to have an online presence allowing readers, globally, to connect with them.

Opportunities for authors and readers to interact together existed long before the internet, through events such as readings, signings, and festivals. The internet does not replace these – indeed, festivals have grown in popularity in recent years, and many have embraced technology to broaden their engagement outside of the event itself. However, unlike organised events, readers and authors can potentially interact online far more directly, outside of formal mediation. Perceived benefits from this disintermediation are commonly hailed – i.e. that it can break down access barriers for readers (e.g. geography and time, so they can more easily learn about the books they enjoy and the person behind the story), and help authors to better understand their market and the reception to their books. However, being a relatively new phenomenon, we don’t know much yet about how interacting with each other online may differ to doing so at a festival or event, and what complications the new environment may introduce to the experience, or even exacerbate. It is this research gap that my work has been addressing.

Early in my research, I conducted interviews with fiction authors and readers who use different online technologies (e.g. social media such as Twitter and Facebook, forums such as Reddit, or literary-specific sites such as GoodReads) to interact with other readers and authors. All participants generously shared their honest, open accounts about what they do, where and why, and where they encounter problems. It became clear that, although the benefits to being online are widely accepted and everyone had good experiences to report, in reality, people’s reasons for being online were riddled with contradictions, and, in some cases, it was debatable whether the positives outweighed the negatives, or whether the practice served a meaningful purpose at all. Ultimately – it’s complex, and not everything we thought we knew is necessarily as clear cut as it’s often perceived. 

This led me to make a U-turn in my research. Before working out how to improve technology to better support interactions as they currently stand, I needed to find out more about people’s motivations to be online, and to question whether we were focused on the right problem in the first place. From this I’ve been working to reframe how we, in the research field of Human-Computer Interaction, may understand the dynamics between authors and readers, by building a broader picture of context and influences in the literary field.

I’m going to write another blog post in the coming months to talk about what I’ve found, and what I think we need to focus on in the near future. In particular, I think it is important to improve support for authors, as many find themselves in a tricky position because of the expectation that they are available and public-facing, effectively 24/7. However, before I expand on that, I am about to embark on one final study to address some outstanding questions I have about the needs of their market – fiction readers. 

Over the next few weeks, I will be recruiting people who read fiction – whether they interact online about reading or not - to join me for what I am informally referring to as ‘an interview with props’. This study is happening a few months later than I’d originally intended, as restrictions in relation to Covid-19 required me to change my original plans (e.g. to meet people face-to-face). My study has ‘gone digital’, changing how I can facilitate the sessions, and what I can realistically expect from them.

I will be asking people to join me to chat online, using Zoom, to reflect on a series of sketched interface design ideas I have created, and to discuss their current thoughts about authors being available online. The design sketches represent deviations from the technology currently in common use - some significant, and some subtle. The designs are not being tested on behalf of any affiliated company, and neither do I necessarily anticipate any of them to be developed into working technology in the future. Ultimately, they are probes to get us talking about broader issues surrounding author and reader interactions, and I’m hoping that by getting peoples perspectives about them, I’ll learn more about why the designs *don’t* work, moreover why they do, to help inform future research and design work.

I’ve been ‘umming and ahhing’ about how best to share these designs with participants through a digital platform. Sitting together in the same room, as I’d originally planned, we could all move them around, pick them up, take a red pen to them, make notes on post-its, and sketch alternative ideas on paper. There are fantastic online technologies available these days, which have proved invaluable during this pandemic. But they can’t provide the same experience that being physically present together can (a predicament which, perhaps ironically, is fitting with the research problem itself!).

A sneaky peek at a sketch in the making, on Miro

I have decided to use a website called Miro.com to facilitate the study – an interactive whiteboard tool that allows participants to add digital post-it notes, doodles, and more. I’ve never used it before now, and to my knowledge there is no published research out there (yet) by others in my research field who have used it with participants, for me to learn from their experience. I think I must prepare myself for a few technical glitches! But I am hopeful that participants will enjoy the experience, which will be informal, encouraging, and in no way a judgement of their abilities with the technology. I am confident that their contribution will greatly help my work – and future work which will help authors and readers in the real world.

If anyone who is reading this is interested in participating, please do get in touch. Information about the study and how to contact me can be found here or please email [email protected].

Update: Due to a phenomenal response, Carol has recruited enough interviewees for the study, so the link to the application form has been removed (13/10/2020). Thanks to everyone who has applied.

The New Media Writing Prize (NMWP) is in an annual international award, which encourages and promotes the best in new media writing; showcasing innovative digital fiction, poetry and journalism. The types of interactive writing that we have been examining, researching and tentatively collecting in our emerging formats work at the Library.

Last year we celebrated ten years of the prize, looking back over previous winning entries, with a Digital Conversation event at the British Library. Now we are looking forward to seeing what types of work will be entered into this year's prize.

If you are a writer of interactive works, then you may be interested to know that the 2020 New Media Writing prize is currently open for entries. You can nominate works via the online entry form at https://newmediawritingprize.co.uk/enter/. This year, there is only one category, the if:book UK New Media Writing prize. However, you can enter fiction, poetry, journalism, games, anything as long as it is interactive and makes use of digital media. The deadline is Friday 27th November 2020, 12 noon GMT, or for student entries, these must be entered by Friday 18th December 2020, 12 noon GMT. The organisers are especially encouraging entries from students and will give special consideration to entries from students at undergraduate or postgraduate level. 

There is one award of £1000 for the winner, and there will be commendations for shortlisted works, which the judges feel are deserving of a special mention. All the rules are here, and please do read the FAQs section of the NMWP website, which has more details about what the judges are looking for in entries. If you have a question that is not covered by the FAQ, then you can email the organisers at [email protected]. You may also want to check out the winners and shortlisted entries from the 2019 prize, which I blogged about here, for inspiration. If you do enter, then good luck!

This post is by Digital Curator Stella Wisdom (@miss_wisdom). 

This is a guest post by Anne Courtney, Gulf History Cataloguer with the Qatar Digital Library, https://www.qdl.qa/en 

Sonification

Over the summer, I’ve been investigating the sonification of data. On the Qatar Project (QDL), we generate a large amount of data, and I wanted to experiment with different methods of representing it. Sonification was a new technique for me, which I learnt about through this article: https://programminghistorian.org/en/lessons/sonification.

 

What is sonification?

Sonification is the method of representing data in an aural format, rather than visual format, such as a graph. It is particularly useful for showing changes in data over time. Different trends are highlighted depending on the choices made during the process, in the same way as they would be when drawing a graph.

 

How does it work?

First, all the data must be put in the right format:

Figure 1: Excel data of longitude points where the Palsgrave anchored

Then, the data is used to generate a midi file. The Programming Historian provides an example python script for this, and by changing parts of it, it is possible to change the tempo, note length, scale, and other features.

Figure 2: Python script ready to output a midi file of occurrences of Anjouan over time

Finally, to overlay the different midi files, edit them, and change the instruments, I used MuseScore, freely-downloadable music notation software. Other alternatives include LMMS and Garageband:

Figure 3: The score of the voyages of the Discovery, Palsgrave, and Mary, labelled to show the different places where they anchored.

 

The sound of authorities

Each item which the Qatar project catalogues has authority terms linked to it, which list the main subjects and places connected to the item. As each item is dated, it is possible to trace trends in subjects and places over time by assigning the dates of the items to the authority terms. Each authority term ends up with a list of dates when it was mentioned. By assigning different instruments to the different authorities, it is possible to hear how they are connected to each other.

This sound file contains the sounds of places connected with the trade in enslaved people, and how they intersect with the authority term ‘slave trade’. The file begins in 1700 and finishes in 1900. One of the advantages of sonification is that the silence is as eloquent as the data. The authority terms are mentioned more at the end of the time period than the start, and so the piece becomes noisier as the British increasingly concern themselves with these areas. The pitch of the instruments is determined, in this instance, by the months of the records in which they are mentioned.

Authorities

The authority terms are represented by these instruments:

Anjouan: piccolo

Madagascar: cello

Zanzibar: horn

Mauritius: piano

Slave Trade: tubular bell

 

Listening for ships

Ships

This piece follows the journeys of three ships from March 1633 to January 1637. In this example, the pitch is important because it represents longitude; the further east the ships travel, the higher the pitch. The Discovery and the Palsgrave mostly travelled together from Gravesend to India, and they both made frequent trips between the Gulf and India. The Mary set out from England in April 1636 to begin her own journey to India. The notes represent the time the ships spent in harbour, and the silence is the time spent at sea. The Discovery is represented by the flute, the Palsgrave by the violin, and the Mary by the horn.

For many people, our personal understanding of time has been challenged during the covid-19 pandemic, with minutes, hours and days of the week seeming to all merge together into "blursday", without our previous pre covid-19 routines to help us mark points in time.

Talking of time, the AHRC-funded Chronotopic Cartographies research project has spent the last few years investigating how we might use digital tools to analyse, map, and visualise the spaces, places and time within literary texts. It draws on the literary theorist Mikhail Bakhtin's concept of the 'chronotope': a way of describing how time and place are linked and represented in different literary genres.

To showcase research from this project, next Tuesday (29th September 2020) we are co-hosting with them an online interdisciplinary conference: "Mapping Space, Mapping Time, Mapping Texts". 

The "Mapping Space, Mapping Time, Mapping Texts" registration page is here. Once you have signed up, you will receive an email with links to recorded keynotes and webinar sessions. You will also received an email with links to the Flickr wall of virtual research posters and hangout spaces, on the morning of the conference.

The conference will go live from 09.00 BST, all webinars and live Q&A sessions will be held in Microsoft Teams. If you don't have Teams installed, you can do so before the event here. We appreciate that many participants will be joining from different time zones and that attendees may want to dip in and out of sessions; so please join at whatever pace suits you.

Our keynote speakers: James Kneale, Anders Engberg-Pederson and Robert T. Tally Jr have provided recordings of their presentations and will be joining the event for live Q&A sessions over the course of the day. You can watch the keynote recordings at any time, but if you want to have the conference experience, then log in to the webinars at the times below so you can participate "live" across the day. Q&A sessions will be held after each keynote at the times below. 

Schedule:

9.00 BST: Conference goes live, keynotes and posters available online, urls sent via email.

9.30: Short introduction and welcome from Sally Bushell

10.00-11.00: First Keynote: James Kneale

11.00-11.30: Live Q&A (chaired by Rebecca Hutcheon)

2.00-3.00: Second Keynote: Anders Engberg-Pedersen

3.00-3.30: Live Q&A (chaired by Duncan Hay)

5.00-6.00: Third Keynote: Robert T. Tally Jr

6.00-6.30: Live Q&A (chaired by Sally Bushell)

In the breaks between sessions, please do browse the online Flickr wall of research posters and hang out in conference virtual chat room.

We very much look forward to seeing you on-screen, on the day (remember it is Tuesday, not Blursday!).

This post is by Digital Curator Stella Wisdom (@miss_wisdom)