Week Beginning 31st January 2022

I split my time over many different projects this week.  For the Books and Borrowing project I completed the work I started last week on processing the Wigtown data, writing a little script that amalgamated borrowing records that had the same page order number on any page.  These occurrences arose when multiple volumes of a book were borrowed by a person at the same time and each volume was recorded separately.  My script worked perfectly and many such records were amalgamated.

I then moved onto incorporating images of register pages from Leighton into the CMS.  This proved to be a rather complicated process for one of the four registers as around 30 pages for the register had already been manually created in the CMS and had borrowing records associated with them.  However, these pages had been created in a somewhat random order, starting at folio number 25 and mostly being in order down to 43, at which point the numbers are all over the place, presumably because the pages were created in the order that they were transcribed.    As it stands the CMS relies on the ‘page ID’ order when generating lists of pages as ‘Folio Number’ isn’t necessarily in numerical order (e.g. front / back matter with Roman numerals).  If out of sequence pages crop up a lot we may have to think about adding a new ‘page order’ column, or possibly use the ‘previous’ and ‘next’ IDs to ascertain the order pages should be displayed.  After some discussion with the team it looks like pages are usually created in page order and Leighton is an unusual case, so we can keep using the auto-incrementing page ID for listing pages in the contents page.  I therefore generated a fresh batch of pages for the Leighton register then moved the borrowing records from the existing mixed up pages to the appropriate new page, then deleted the existing pages so everything is all in order.

For the Speak For Yersel project I created a new exercise whereby users are presented with a map of Scotland divided into 12 geographical areas and there are eight map markers in a box in the sea to the east of Scotland.  Each marker is clickable, and clicking on it plays a sound file.  Each marker is also draggable and after listening to the sound file the user should then drag the marker to whichever area they think the speaker in the sound file is from.  After dragging all of the markers the user can then press a ‘check answers’ button to see which they got right, and press a ‘view correct locations’ button which animates the markers to their correct locations on the map.  It was a lot of fun making the exercise and I think it works pretty well.  It’s still just an initial version and no doubt we will be changing it, but here’s a screenshot of how it currently looks (with one answer correct and the rest incorrect):

For the Speech Star project I made some further changes to the speech database.  Videos no longer autoplay, as requested.  Also, the tables now feature checkboxes beside them.  You can select up to four videos by pressing on these checkboxes.  If you select more than four the earliest one you pressed is deselected, keeping a maximum of four no matter how many checkboxes you try to click on.  When at least one checkbox is pressed the tab contents will slide down and a button labelled ‘Open selected videos’ will appear.  If you press on this a wider popup will open, containing all of your chosen videos and the metadata about each.  This has required quite a lot of reworking to implement, but it seemed to be working well, until I realised that while the multiple videos load and play successfully in Firefox, in Chrome and MS Edge (which is based on Chrome) only the final video loads in properly, with only audio playing on the other videos.  I’ll need to investigate this further next week.  But here’s a screenshot of how things look in Firefox:

Also this week I spoke to Thomas Clancy about the Place-names of Iona project, including discussing how the front-end map will function (Thomas wants an option to view all data on a single map, which should work although we may need to add in clustering at higher zoom levels.  We also discussed how to handle external links and what to do about the elements database, that includes a lot of irrelevant elements from other projects.

I also had an email conversation with Ophira Gamliel in Theology about a proposal she’s putting together that will involve an interactive map, gave some advice to Diane Scott about cookie policy pages, worked with Raymond in Arts IT Support to fix an issue with a server update that was affecting the playback of videos on the Seeing Speech and Dynamic Dialects websites and updated a script that Fraser Dallachy needed access to for his work on a Scots Thesaurus.

Finally, I had some email conversations with the DSL people and made an update to the interface of the new DSL website to incorporate an ‘abbreviations’ button, which links to the appropriate DOST or SND abbreviations page.

 

 

Week Beginning 22nd November 2021

I spent a bit of time this week writing an abstract for the DH2022 conference.  I wrote about how I rescued the data for the Anglo-Norman Dictionary in order to create the new AND website.  The DH abstracts are actually 750-1000 words long so it took a bit of time to write.  I have sent it on to Marc for feedback and I’ll need to run it by the AND editors before submission as well (if it’s worth submitting).  I still don’t know whether there would be sufficient funds for me to attend the event, plus the acceptance rate for papers is very low, so I’ll just need to see how this develops.

Also this week I participated in a Zoom call for the DSL about user feedback and redeveloping the DSL website.  It was a pretty lengthy call, but it was interesting to be a part of.  Marc mentioned a service called Hotjar (https://www.hotjar.com/) that allows you to track how people use your website (e.g. tracking their mouse movements) and this seemed like an interesting way of learning about how an interface works (or doesn’t).  I also had a conversation with Rhona about the updates to the DSL DNS that need to be made to improve the security or their email systems.  Somewhat ironically, recent emails from their IT people had ended up in my spam folder and I hadn’t realised they were asking me for further changes to be made, which unfortunately has caused a delay.

I spoke to Gerry Carruthers about another new project he’s hoping to set up, and we’ll no doubt be having a meeting about this in the coming weeks.  I also gave some advice to the students who are migrating the IJOSTS articles to WordPress too and made some updates to the Iona Placenames website in preparation for their conference.

For the Anglo-Norman Dictionary I fixed an issue with one of the textbase texts that had duplicate notes in one of its pages and then I worked on a new feature for the DMS that enables the editors to search the phrases contained in locutions in entries.  Editors can either match locution phrases beginning with a term (e.g. ta*), ending with a term (e.g. *de) or without a wildcard the term can appear anywhere in the phrase.  Other options found on the public site (e.g. single character wildcards and exact matches) are not included in this search.

The first time a search is performed the system needs to query all entries to retrieve only those that feature a locution.  These results are then stored in the session for use the next time a search is performed.  This means subsequent searches in a session should be quicker, and also means if the entries are updated between sessions to add or remove locutions the updates will be taken into consideration.

Search results work in a similar way to the old DMS option:  Any matching locution phrases are listed, together with their translations if present (if there are multiple senses / subsenses for a locution then all translations are listed, separated by a ‘|’ character).  Any cross references appear with an arrow and then the slug of the cross referenced entry.  There is also a link to the entry the locution is part of, which opens in a new tab on the live site.  A count of the total number of entries with locutions, the number of entries your search matched a phrase in and the total number of locutions is displayed above the results.

I spent the rest of the week working on the Speak For Yersel project.  We had a Zoom call on Monday to discuss the mockups I’d been working on last week and to discuss the user interface that Jennifer and Mary would like me to develop for the site (previous interfaces were just created for test purposes).  I spent the rest of my available time developing a further version of the grammar exercise with the new interface, that included logos, new fonts and colour schemes, sections appearing in different orders and an overall progress bar for the full exercise rather than individual ones for the questionnaire and the quiz sections.

I added in UoG and AHRC logos underneath the exercise area and added both an ‘About’ and ‘Activities’ menu items with ‘Activities’ as the active item.  The active state of the menu wasn’t mentioned in the document but I gave it a bottom border and made the text green not blue (but the difference is not hugely noticeable).  This is also used when hovering over a menu item.  I made the ‘Let’s go’ button blue not green to make it consistent with the navigation button in subsequent stages.  When a new stage loads the page now scrolls to the top as on mobile phones the content was changing but the visible section remained as it was previously, meaning the user had to manually scroll up.  I also retained the ‘I would never say that!’ header in the top-left corner of all stages rather than having ‘activities’ so it’s clearer what activity the user is currently working on.  For the map in the quiz questions I’ve added the ‘Remember’ text above the map rather than above the answer buttons as this seemed more logical and on the quiz the map pane scrolls up and scrolls down when the next question loads so as to make it clearer that it’s changed.  Also, the quiz score and feedback text now scroll down one after the other and in the final ‘explore’ page the clicked on menu item now remains highlighted to make it clearer which map is being displayed.  Here’s a screenshot of how the new interface looks:

Week Beginning 8th November 2021

I spent a bit of time this week working for the DSL.  I needed to act as the go-between for the DSL’s new IT people who are updating their email system and the University’s IT people who manage the DNS record on behalf of the DSL.  IT took a few attempts before the required changes were successfully in place.  I also read through a document that had been prepared about automatically ‘fixing’ the DSL’s dates to make them machine readable, and gave some feedback on the many different procedures that will need to be performed on the various date forms to produce the desired structure.

I also looked into an issue with cross references within citations that work in the live site but are not functioning in the new site or in the DSL’s editing system.  After some investigation it seems like it’s another case of the original API ‘fixing’ the XML in some way each time it’s processed in order for these links to work.  The XML for ‘put_v’ stored in the original API is as follows:

<cit><cref><date>1591</date> <title>Edinb. B. Rec.</title> V 41 (see <ref>Putting</ref> <i>vbl. n.</i> 1 (1)).</cref></cit>

There is a <ref> tag but no other information in this tag.  This is the same for the XML exported from DPS and used in the new dsl site (which has an additional bibliographic reference in):

<cit><cref refid=”bib013153″><date>1591</date> <title>Edinb. B. Rec.</title> V 41 (see <ref>Putting</ref> <i>vbl. n.</i> 1 (1)).</cref></cit>

The XSLT for both the live and new sites doesn’t include anything to process a <ref> that doesn’t include any attributes so both the live and new sites shouldn’t be displaying a link through to ‘putting’.  But of course the live site does.  I had generated and stored the XML that the original API (which I did not develop) outputs whenever the live site asks for an entry.  When looking at this I found the following:

<cit><cref ref=”db674″><date>1591</date> <title>Edinb. B. Rec.</title> V 41 (see <ref action=”link” href=”dost/putting”>Putting</ref> <i>vbl. n.</i> 1 (1)).</cref></cit>

You can see that the original API is injecting both a bibliographical cross-reference and the ‘putting’ reference.  The former we previously identified and sorted but the latter unfortunately hasn’t, although references that are not in citations do seem to have been fixed.  I updated the XSLT on the new dsl site to process the <ref> so the link now works, however this is not an approach that can be relied upon as all the XSLT is currently doing is taking the contents of the tag (Putting) and making a link out of it.  If the ‘slug’ of the entry doesn’t match the display form then the link is not going to work.  The original API includes a table containing cross references, but this doesn’t differentiate ones in citations from regular ones, and as the ‘putting_v’ entry contains 83 references it’s not going to be easy to pick out from this the ones that still need to be added.  This will need further discussion with the editors.

Continuing on a dictionary theme, I also did some further work for the Anglo-Norman Dictionary.  Last week I processed entries where a varlist date needed to be used as the citation date, but we noticed that the earliest date for entries hadn’t been updated in many cases where it should have been.  This week I figured out what went wrong.  My script only updated the entry’s date if the new date from the varlist was earlier than the existing earliest date for the entry.  This is obviously not what we want as in the majority of cases the varlist date will be later and should replace the earlier date that is erroneous.  Thankfully it was easy to pick out all of the entries that have a ‘usevardate’ and I then reran a corrected version of the script that checks and replaces an entry’s earliest date.

The editor spotted a couple of entries that still hadn’t been updated after this process and I then had to investigate them.  One of them had an error in the edited markup that was preventing the update from being applied.  For the other I realised that my code to update the XML wasn’t looking at all senses, just the first in each entry.  My script was attempting to loop through all senses as follows:

foreach($xml->main_entry->sense -> attestation as $a){

//process here

}

Which unfortunately only loops through all attestations in the first sense.  What I needed to do was:

 

foreach($xml->main_entry->sense as $s){

foreach($s->attestation as $a){

//process here

}

}

As the sense that needed updating for ‘aspreté’ was the last one the XML wasn’t getting changed, this meant ‘usevardate’ wasn’t present in the XML therefore my update to regenerate the earliest dates didn’t catch this entry (despite all dates for citations being successfully updated in the database for the entry).  I then fixed my script and regenerated all data again, including fixing the data so the ones with XML errors were updated.  I then ran a further spreadsheet containing entries that needed updated through the fixed script, resulting in a further 257 citations that had their dates updated.

Finally, I updated the Dictionary Management System so that ‘usevardate’ dates are taken into consideration when processing and publishing uploaded XML files.  If a ‘usevardate’ is found then this date is used for the attestation, which automatically affects the earliest date that is generated for the entry and also the dates used for attestations for search purposes.  I tried this out by downloading the XML for ‘admirable’, which features a ‘usevardate’.  I then edited the XML to remove the ‘usevardate’ before uploading and publishing this version.  As expected the dates for the attestation and the entry’s earliest date were affected by this change.  I then edited the XML to reinstate the ‘usevardate’ and uploaded and published this version, which took into consideration the ‘usevardate’ when generating the entry’s earliest date and attestation dates and returned the entry to the way it was before the test.

Also this week I set up a WordPress site that will be used for the archive of the International Journal of Scottish Theatre and Screen and migrated one of the issues to WordPress, which required me to do the following:

  1. Open the file in a PDF viewer for reference (e.g. Adobe Acrobat)
  2. Open the file in MS Word, which converts it into an editable format
  3. Create a WordPress page for the article with the article’s title as the page title and setting the page ‘parent’ as Volume 1
  4. Copy and paste the article contents from Word into WordPress
  5. Go through the article in WordPress, referencing the file in Acrobat, and manually fixing any issues that I spotted (e.g. fixing the display of headings, fixing some line breaks that were erroneously added). Footnotes proved to be particularly tricky as their layout was not handled very well by Word.  It’s possible that some footnotes are not quite right, especially with the ‘Trainspotting’ article that has more than 70(!) footnotes.
  6. Publish the WordPress page and update the ‘Volume 1’ page to add a link to it.

None of this was particularly difficult to do, but it was somewhat time-consuming.  There are a further 18 issues left to do (as far as I can tell), although some of these will take longer as they contain more articles, and some of these are more structurally complicated (e.g. including images).  Gerry Carruthers is getting a couple of students to do the rest and we have a meeting scheduled next week where I’ll talk through the process.

I also made some further tweaks to the WordPress site for the ‘Our Heritage, Our Stories’ site and dealt with renewing the domain for TheGlasgowStory.com site, which is now safe for a further nine years.  I also generated an Excel spreadsheet of the full lexical dataset from Mapping Metaphor for Wendy Anderson after she had a request for the data from some researchers in Germany.

I spent the rest of the week working for the Speak For Yersel project, continuing to generate mockups of the interactive exercises.  I completed an initial version of the overall structure for both the accessibility and word choice question types for the grammar exercise, so it will be possible to just ‘plug in’ any number of other questions that fit these templates.  What I haven’t done yet is incorporate the maps, the post-questionnaire ‘explore’ or the final quiz, as these need more content.  Here’s how things currently look:

I used another different font for the heading (Slackey), with the same one used for the ‘Question x of y’ text too’.  I also used CSS gradients quite a bit in this version, as the team seemed quite keen on these.  There’s a subtle diagonal gradient in the header and footer backgrounds, and a more obvious  top-to-bottom one in the answer buttons.  I used different combinations of colours too.  I created a progress bar, which works, but with only two questions in the system it’s not especially obvious what it does.  Rather than having people click an answer and then click a ‘next’ button to continue I’ve made it so that clicking an answer automatically loads the next step, and clicking an answer loads a panel with a ‘map’ – this is just a static image for now.  It also loads a ‘next’ button if there is a next question.  Clicking the ‘next’ button slides up the map panel, loads the next question in and advances the progress bar.  Users will be accessing this on many different screen sizes and I’ve tested it out on my Android phone and my iPad in both portrait and landscape orientations and all seems to work well to me.  However, the map panel will be displayed below rather than beside the questions on narrower screens.

I then began experimenting with randomly positioned markers in polygonal areas.  Initially I wanted to see whether this would be possible in ArcGIS, and a bit of Googling suggested it would be, see for example this post: http://gis.mtu.edu/?p=127 which is 10 years old, so the instructions don’t in any way match up to how things work in the current version of ArcGIS, but it at least showed it should be possible.  I loaded the desktop version of ArcGIS up via Glasgow Anywhere and after some experimentation and a fair bit of exasperation I managed to create a polygon shape and add 100 randomly placed marker points to it, which you can see here:

Something we will have to bear in mind is how such points will look when zoomed:

This is just 100 points over a pretty large geographical area.  We might end up with thousands of points, which might make this approach unusable.  Another issue is it took ArcGIS more than a minute to generate and process these 100 random points.  I don’t know how much of this is down to running the software via Glasgow Anywhere, but if we’re dealing with tens of polygons and hundreds or thousands of data points this is just not going to be feasible.

An issue of greater concern is that as far as I can tell (after more than an hour of investigation) the ‘create random points’ option is not available via ArcGIS Online, which is the tool we would need to use to generate maps to share online (if we choose to use ArcGIS).  The online version seems to be really pared back in terms of functionality compared to the desktop version and I just couldn’t see any way of incorporating the random points system.  However, I discovered a way of generating random points using Leaflet and another javascript based geospatial library called turf.js (http://turfjs.org/).  The information about how to go about it is here:  https://gis.stackexchange.com/questions/163044/mapbox-how-to-generate-a-random-coordinate-inside-a-polygon

I created a test map using the SCOSYA area for Campbeltown and the SCOSYA base map.  As a solution I’d say it’s working pretty well – it’s very fast and seems to do what we want it to.  You can view an example of the script output here:

The script generates 100 randomly placed markers each time you load the page.  At zoomed out levels the markers are too big, but I can make them smaller – this is just an initial test.  There is unfortunately going to be some clustering of markers as well, due to the nature of the random number generator.  This may give people to wrong impression.  I could maybe update the code to reject markers that are in too close proximity to another one, but I’d need to see about that.  I’d say it’s looking promising, anyway!

Week Beginning 25th October 2021

I came down with some sort of stomach bug on Sunday and was off work with it on Monday and Tuesday.  Thankfully I was feeling well again by Wednesday and managed to cram quite a lot into the three remaining days of the week.  I spent about a day working on the Data Management Plan for the new Curious Travellers proposal, sending out a first draft on Wednesday afternoon and dealing with responses to the draft during the rest of the week.  I also had some discussions with the Dictionaries of the Scots Language’s IT people about updating the DNS record regarding emails, responded to a query about the technology behind the SCOTS corpus, updated the images used in the mockups of the STAR website and created the ‘attendees only’ page for the Iona Placenames conference and added some content to it.  I also had a conversation with one of the Books and Borrowing researchers about trimming out the blank pages from the recent page image upload, and I’ll need to write a script to implement this next week.

My main task of the week was to develop a test version of the ‘where is the speaker from?’ exercise for the Speak For Yersel project.  This exercise involves the user listening to an audio clip and pressing a button each time they hear something that identifies the speaker as being from a particular area.  In order to create this I needed to generate my own progress bar that tracks the recording as it’s played, implement ‘play’ and ‘pause’ buttons, implement a button that when pressed grabs the current point in the audio playback and places a marker in the progress bar, and implement a means of extrapolating the exact times of the button press to specific sections of the transcription of the audio file so we can ascertain which section contains the feature the user noted.

It took quite some planning and experimentation to get the various aspects of the feature working, but I managed to complete an initial version that I’m pretty pleased with.  It will still need a lot of work but it demonstrates that we will be able to create such an exercise.  The interface design is not final, it’s just there as a starting point, using the Bootstrap framework (https://getbootstrap.com), the colours from the SCOSYA logo and a couple of fonts from Google Fonts (https://fonts.google.com).  There is a big black bar with a sort of orange vertical line on the right.  Underneath this is the ‘Play’ button and what I’ve called the ‘Log’ button (but we probably want to think of something better).  I’ve used icons from Font Awesome (https://fontawesome.com/) including a speech bubble icon in the ‘log’ button.

As discussed previously, when you press the ‘Play’ button the audio plays and the orange line starts moving across the black area.  The ‘Play’ button also turns into a ‘Pause’ button.  The greyed out ‘Log’ button becomes active when the audio is playing.  If you press the ‘Log’ button a speech bubble icon is added to the black area at the point where the orange ‘needle’ is.

For now the exact log times are outputted in the footer area.  Once the audio clip finishes the ‘Play’ button becomes a ‘Start again’ button.  Pressing on this clears the speech bubble icons and the footer and starts the audio from the beginning again.  The log is also processed.  Currently 1 second is taken off each click time to account for thinking and clicking.  I’ve extracted the data from the transcript of the audio and manually converted it into JSON data which is more easily processed by JavaScript.  Each ‘block’ consists of an ID, the transcribed content and the start and end times of the block in milliseconds.

For the time being for each click the script looks through the transcript data to find an entry where the click time is between the entry’s start and end times.  A tally of clicks for each transcript entry is then stored. This then gets outputted in the footer so you can see how things are getting worked out.  This is of course just test data – we’ll need smaller transcript areas for the real thing.  Currently nothing gets submitted to the server or stored – it’s all just processed in the browser.  I’ve tested the page out in several browsers in Windows, on my iPad and on my Android phone and the interface works perfectly well on mobile phone screens.  Below is a screenshot showing audio playback and four linguistic features ‘logged’:

Also this week I had a conversation with the editor of the AND about updating the varlist dates.  I also updated the DTD to allow the new ‘usevardate’ attribute to be used to identify occasions where a varlist date should be used as the earliest citation date.  We also became aware that a small number of entries in the online dictionary are referencing an old DTD on the wrong server so I updated these.

Week Beginning 18th October 2021

I was back at work this week after having a lovely holiday in Northumberland last week.  I spent quite a bit of time in the early part of the week catching up with emails that had come in whilst I’d been off.  I fixed an issue with Bryony Randall’s https://imprintsarteditingmodernism.glasgow.ac.uk/ site, which was put together by an external contractor, but I have now inherited.  The site menu would not update via the WordPress admin interface and after a bit of digging around in the source files for the theme it would appear that the theme doesn’t display a menu anywhere, that is the menu which is editable from the WordPress Admin interface is not the menu that’s visible on the public site.  That menu is generated in a file called ‘header.php’ and only pulls in pages / posts that have been given one of three specific categories: Commissioned Artworks, Commissioned Text or Contributed Text (which appear as ‘Blogs’).  Any post / page that is given one of these categories will automatically appear in the menu.  Any post / page that is assigned to a different category or has no assigned category doesn’t appear.  I added a new category to the ‘header’ file and the missing posts all automatically appeared in the menu.

I also updated the introductory texts in the mockups for the STAR websites and replied to a query about making a place-names website from a student at Newcastle.  I spoke to Simon Taylor about a talk he’s giving about the place-name database and gave him some information on the database and systems I’d created for the projects I’ve been involved with.  I also spoke to the Iona Place-names people about their conference and getting the website ready for this.

I also had a chat with Luca Guariento about a new project involving the team from the Curious Travellers project.  As this is based in Critical Studies Luca wondered whether I’d write the Data Management Plan for the project and I said I would.  I spent quite a bit of time during the rest of the week reading through the bid documentation, writing lists of questions to ask the PI, emailing the PI, experimenting with different technologies that the project might use and beginning to write the Plan, which I aim to complete next week.

The project is planning on running some pre-digitised images of printed books through an OCR package and I investigated this.  Google owns and uses a program called Tesseract to run OCR for Google Books and Google Docs and it’s freely available (https://opensource.google/projects/tesseract).  It’s part of Google Docs – if you upload an image of text into Google Drive then open it in Google Docs the image will be automatically OCRed.  I took a screenshot of one of the Welsh tour pages (https://viewer.library.wales/4690846#?c=&m=&s=&cv=32&manifest=https%3A%2F%2Fdamsssl.llgc.org.uk%2Fiiif%2F2.0%2F4690846%2Fmanifest.json&xywh=-691%2C151%2C4725%2C3632) and cropped the text and then opened it in Google Docs and even on this relatively low resolution image the OCR results are pretty decent.  It managed to cope with most (but not all) long ‘s’ characters and there are surprisingly few errors – ‘Englija’ and ‘Lotty’ are a couple and have been caused by issues with the original print quality.  I’d say using Tesseract is going to be suitable for the project.

I spent a bit of time working on the Speak For Yersel project.  We had a team meeting on Thursday to go through in detail how one of the interactive exercises will work.  This one will allow people to listed to a sound clip and then relisten to it in order to click whenever they hear something that identifies the speaker as coming from a particular location.  Before the meeting I’d prepared a document giving an overview of the technical specification of the feature and we had a really useful session discussing the feature and exactly how it should function.  I’m hoping to make a start on a mockup of the feature next week.

Also for the project I’d enquired with Arts IT Support as to whether the University held a license for ArcGIS Online, which can be used to publish maps online.  It turns out that there is a University-wide license for this which is managed by the Geography department and a very helpful guy called Craig MacDonell arranged for me and the other team members to be set up with accounts for it.  I spent a bit of time experimenting with the interface and managed to publish a test heatmap based on data from SCOSYA.  I can’t get it to work directly with the SCOSYA API as it stands, but after exporting and tweaking one of the sets of rating data as a CSV I pretty quickly managed to make a heatmap based on the ratings and publish it: https://glasgow-uni.maps.arcgis.com/apps/instant/interactivelegend/index.html?appid=9e61be6879ec4e3f829417c12b9bfe51 This is just a really simple test, but we’d be able to embed such a map in our website and have it pull in data dynamically from CSVs generated in real-time and hosted on our server.

Also this week I had discussions with the Dictionaries of the Scots Language people about how dates will be handled.  Citation dates are being automatically processed to add in dates as attributes that can then be used for search purposes.  Where there are prefixes such as ‘a’ and ‘c’ the dates are going to be given ranges based on values for these prefixes.  We had a meeting to discuss the best way to handle this.  Marc had suggested that having a separate prefix attribute rather than hard coding the resulting ranges would be best.  I agreed with Marc that having a ‘prefix’ attribute would be a good idea, not only because it means we can easily tweak the resulting date ranges at a later point rather than having them hard-coded, but also because it then gives us an easy way to identify ‘a’, ‘c’ and ‘?’ dates if we ever want to do this.  If we only have the date ranges as attributes then picking out all ‘c’ dates (e.g. show me all citations that have a date between 1500 and 1600 that are ‘c’) would require looking at the contents of each date tag for the ‘c’ character which is messier.

A concern was raised that not having the exact dates as attributes would require a lot more computational work for the search function, but I would envisage generating and caching the full date ranges when the data is imported into the API so this wouldn’t be an issue.  However, there is a potential disadvantage to not including the full date range as attributes in the XML, and this is that if you ever want to use the XML files in another system and search the dates through it the full ranges would not be present in the XML so would require processing before they could be used.  But whether the date range is included in the XML or not I’d say it’s important to have the ‘prefix’ as an attribute, unless you’re absolutely sure that being able to easily identify dates that have a particular prefix isn’t important.

We decided that prefixes would be stored as attributes and that the date ranges for dates with a prefix would be generated whenever the data is exported from the DSL’s editing system, meaning editors wouldn’t have to deal with noting the date ranges and all the data would be fully usable without further processing as soon as it’s exported.

Also this week I was given access to a large number of images of registers from the Advocates Library that had been digitised by the NLS.  I downloaded these, batch processed them to add in the register numbers as a prefix to the filenames, uploaded the images to our server, created register records for each register and page records for each page.  The registers, pages and associated images can all now be accessed via our CMS.

My final task of the week was to continue work on the Anglo-Norman Dictionary.  I completed work on the script identifies which citations have varlists and which may need to have their citation date updated based on one of the forms in the varlist.  What the script does is to retrieve all entries that have a <varlist> somewhere in them.  It then grabs all of the forms in the <head> of the entry.  It then goes through every attestation (main sense and subsense plus locution sense and subsense) and picks out each one that has a <varlist> in it.

For each of these it then extracts the <aform> if there is one, or if there’s not then it extracts the final word before the <varlist>.  It runs a Levenshtein test on this ‘aform’ to ascertain how different it is from each of the <head> forms, logging the closest match (0 = exact match of one form, 1 = one character different from one of the forms etc).  It then picks out each <ms_form> in the <varlist> and runs the same Levenshtein test on each of these against all forms in the <head>.

If the score for the ‘aform’ is lower or equal to the lowest score for an <ms_form> then the output is added to the ‘varlist-aform-ok’ spreadsheet.  If the score for one of the <ms_form> words is lower than the ‘aform’ score the output is added to the ‘varlist-vform-check’ spreadsheet.

My hope is that by using the scores we can quickly ascertain which are ok and which need to be looked at by ordering the rows by score and dealing with the lowest scores first.  In the first spreadsheet there are 2187 rows that have a score of 0.  This means the ‘aform’ exactly matches one of the <head> forms.  I would imagine that these can safely be ignored.  There are a further 872 that have a score of 1, and we might want to have a quick glance through these to check they can be ignored, but I suspect most will be fine.  The higher the score the greater the likelihood that the ‘aform’ is not the form that should be used for dating purposes and one of the <varlist> forms should instead.  These would need to be checked and potentially updated.

The other spreadsheet contains rows where a <varlist> form has a lower score than the ‘aform’ – i.e. one of the <varlist> forms is closer to one of the <head> forms than the ‘aform’ is.  These are the ones that are more likely to have a date that needs updated. The ‘Var forms’ column lists each var form and its corresponding score.  It is likely that the var form with the lowest score is the form that we would need to pick the date out for.

In terms of what the editors could do with the spreadsheets:  My plan was that we’d add an extra column to note whether a row needs updated or not – maybe called ‘update’ – and be left blank for rows that they think look ok as they are and containing a ‘Y’ for rows that need to be updated.  For such rows they could manually update the XML column to add in the necessary date attributes.  Then I could process the spreadsheet in order to replace the quotation XML for any attestations that needs updated.

For the ‘vform-check’ spreadsheet I could update my script to automatically extract the dates for the lowest scoring form and attempt to automatically add in the required XML attributes for further manual checking, but I think this task will require quite a lot of manual checking from the onset so it may be best to just manually edit the spreadsheet here too.

Week Beginning 27th September 2021

I had two Zoom calls on Monday this week.  The first was with the Burns people to discuss the launch of the website for the ‘letters and poems’ part of ‘Editing Burns’, to complement the existing ‘Prose and song’ website (https://burnsc21.glasgow.ac.uk/).  The new website will launch in January with some video content and blogs, plus I will be working on a content management system for managing the network of Burns’ letter correspondents, which I will put together some time in November, assuming the team can send me on some sample data by then.  This system will eventually power the ‘Burns letter writing trail’ interactive maps that I’ll create for the new site sometime next year.

My second Zoom call was for the Books and Borrowing project to discuss adding data from a new source to the database.  The call gave us an opportunity to discuss the issues with the data that I’d highlighted last week.  It was good to catch up with the team again and to discuss the issues with the researcher who had originally prepared the spreadsheet containing the data.  We managed to address all of the issues and the researcher is going to spend a bit of time adapting the spreadsheet before sending it to me to be batch uploaded into our system.

I spent some further time this week investigating the issue of some of the citation dates in the Anglo-Norman Dictionary being wrong, as discussed last week.  The issue affects some 4309 entries where at least one citation features the form only in a variant text.  This means that the citation date should not be the date of the manuscript in the citation, but the date when the variant of the manuscript was published.  Unfortunately this situation was never flagged in the XML, and there was never any means of flagging the situation.  The variant date should only ever be used when the form of the word in the main manuscript is not directly related to the entry in question but the form in the variant text is.  The problem is it cannot be automatically ascertained when the form in the main manuscript is the relevant one and when the form in the variant text is as there is so much variation in forms.

For example, the entry https://anglo-norman.net/entry/bochet_1 there is a form ‘buchez’ in a citation and then two variant texts for this where the form is ‘huchez’ and ‘buistez’.  None of these forms are listed in the entry’s XML as variants so it’s not possible for a script to automatically deduce which is the correct date to use (the closest is ‘buchet’).  In this case the main citation form and its corresponding date should be used.  Whereas in the entry https://anglo-norman.net/entry/babeder the main citation form is ‘gabez’ while the variant text has ‘babedez’ and so this is the form and corresponding date that needs to be used.  It would be difficult for a script to automatically deduce this.  In this case a Levenstein test (which test how many letters need to be changed to turn one string into another) could work, but this would still need to be manually checked.

The editor wanted me to focus on those entries where the date issue affects the earliest date for an entry, as these are the most important as the issue results in an incorrect date being displayed for the entry in the header and the browse feature.  I wrote a script that finds all entries that feature ‘<varlist’ somewhere in the XML (the previously exported 4309 entries).  It then goes through all attestations (in all sense, subsense and locution sense and subsense sections) to pick out the one with the earliest date, exactly as the code for publishing an entry does.  What it then does is checks the quotation XML for the attestation with the earliest date for the presence of ‘<varlist’ and if it finds this it outputs information for the entry, consisting of the slug, the earliest date as recorded in the database, the earliest date of the attestation as found by the script, the ID of the  attestation and then the XML of the quotation.  The script has identified 1549 entries that have a varlist in the earliest citation, all of which will need to be edited.

However, every citation has a date associated with it and this is used in the advanced search where users have the option to limit their search to years based on the citation date.  Only updating citations that affect the entry’s earliest date won’t fix this, as there will still be many citations with varlists that haven’t been updated and will still therefore use the wrong date in the search.  Plus any future reordering of citations would require all citations with varlists to be updated to get entries in the correct order.  Fixing the earliest citations with varlists in entries based on the output of my script will fix the earliest date as used in the header of the entry and the ‘browse’ feature only, but I guess that’s a start.

Also this week I sorted out some access issues for the RNSN site, submitted the request for a new top-level ‘ac.uk’ domain for the STAR project and spent some time discussing the possibilities for managing access to videos of the conference sessions for the Iona place-names project.  I also updated the page about the Scots Dictionary for Schools app on the DSL website (https://dsl.ac.uk/our-publications/scots-dictionary-for-schools-app/) after it won the award for ‘Scots project of the year’.

I also spent a bit of time this week learning about the statistical package R (https://www.r-project.org/).  I downloaded and installed the package and the R Studio GUI and spent some time going through a number of tutorials and examples in the hope that this might help with the ‘Speak for Yersel’ project.

For a few years now I’ve been meaning to investigate using a spider / radar chart for the Historical Thesaurus, but I never found the time.  I unexpectedly found myself with some free time this week due to ‘Speak for Yersel’ not needing anything from me yet so I thought I’d do some investigation.  I found a nice looking d3.js template for spider / radar charts here: http://bl.ocks.org/nbremer/21746a9668ffdf6d8242  and set about reworking it with some HT data.

My idea was to use the chart to visualise the distribution of words in one or more HT categories across different parts of speech in order to quickly ascertain the relative distribution and frequency of words.  I wanted to get an overall picture of the makeup of the categories initially, but to then break this down into different time periods to understand how categories changed over time.

As an initial test I chose the categories 02.04.13 Love and 02.04.14 Hatred, and in this initial version I looked only at the specific contents of the categories – no subcategories and no child categories.  I manually extracted counts of the words across the various parts of speech and then manually split them up into words that were active in four broad time periods: OE (up to 1149), ME (1150-1449), EModE (1450-1799) and ModE (1800 onwards) and then plotted them on the spider / radar chart, as you can see in this screenshot:

You can quickly move through the different time periods plus the overall picture using the buttons above the visualisation, and I think the visualisation does a pretty good job of giving you a quick and easy to understand impression of how the two categories compare and evolve over time, allowing you to see, for example, how the number of nouns and adverbs for love and hate are pretty similar in OE:

but by ModE the number of nouns for Love have dropped dramatically, as have the number of adverbs for Hate:

We are of course dealing with small numbers of words here, but even so it’s much easier to use the visualisation to compare different categories and parts of speech than it is to use the HT’s browse interface.  Plus if such a visualisation was set up to incorporate all words in child categories and / or subcategories it could give a very useful overview of the makeup of different sections of the HT and how they develop over time.

There are some potential pitfalls to this visualisation approach, however.  The scale used currently changes based on the largest word count in the chosen period, meaning unless you’re paying attention you might get the wrong impression of the number of words.  I could change it so that the scale is always fixed as the largest, but that would then make it harder to make out details in periods that have much fewer words.  Also, I suspect most categories are going to have many more nouns than other parts of speech, and a large spike of nouns can make it harder to see what’s going on with the other axes.  Another thing to note is that the order of the axes is fairly arbitrary but can have a major impact on how someone may interpret the visualisation.  If you look at the OE chart the ‘Hate’ area looks massive compared to the ‘Love’ area, but this is purely because there is only one ‘Love’ adjective compared to 5 for ‘Hate’.  If the adverb axis had come after the noun one instead the shapes of ‘Love’ and ‘Hate’ would have been more similar.  You don’t necessarily appreciate on first glance that ‘Love’ and ‘Hate’ have very similar numbers of nouns in OE, which is concerning.  However, I think the visualisations have a potential for the HT and I’ve emailed the other HT people to see what they think.

 

Week Beginning 20th September 2021

This was a four-day week for me as I’d taken Friday off.  I went into my office at the University on Tuesday to have my Performance and Development Review with my line-manager Marc Alexander.  It was the first time I’d been at the University since before the summer and it felt really different to the last time – much busier and more back to normal, with lots of people in the building and a real bustle to the West End.  My PDR session was very positive and it was great to actually meet a colleague in person again – the first time I’d done so since the first lockdown began.  I spent the rest of the day trying to get my office PC up to date after months of inaction.  One of the STELLA apps (the Grammar one) had stopped working on iOS devices, seemingly because it was still a 32-bit app, and I wanted to generate a new version of it.  This meant upgrading MacOS on my dual-boot PC, which I hadn’t used for years and was very out of date.  I’m still not actually sure whether the Mac I’ve got will support a version of MacOS that will allow me to engage in app development, as I need to incrementally upgrade the MacOS version, which takes quite some time, and by the end of the day there were still further updates required.  I’ll need to continue with this another time.

I spent quite a bit of the remainder of the week working on the new ‘Speak for Yersel’ project.  We had a team meeting on Monday and a follow-up meeting on Wednesday with one of the researchers involved in the Manchester Voices project (https://www.manchestervoices.org/) who very helpfully showed us some of the data collection apps they use and some of the maps that they generate.  It gave us a lot to think about, which was great.  I spent some further time looking through other online map examples, such as the New York Times dialect quiz (https://www.nytimes.com/interactive/2014/upshot/dialect-quiz-map.html) and researching how we might generate the maps we’d like to see.  It’s going to take quite a bit more research to figure out how all of this is going to work.

Also this week I spoke to the Iona place-names people about how their conference in December might be moved online and fixed a permissions issue with the Imprints of New Modernist Editing website and discussed the domain name for the STAR project with Eleanor Lawson.  I also had a chat with Luca Guariento about the restrictions we have on using technologies on the servers in the College of Arts and how this might be addressed.

I also received a spreadsheet of borrowing records covering five registers for the Books and Borrowing project and went through it to figure out how the data might be integrated with our system.  The biggest issue is figuring out which page each record is on.  In the B&B system each borrowing record must ‘belong’ to a page, which in turn ‘belongs’ to a register.  If a borrowing record has no page it can’t exist in the system.  In this new data only three registers have a ‘Page No.’ column and not every record in these registers has a value in this column.  We’ll need to figure out what can be done about this, because as I say, having a page is mandatory in the B&B system.  We could use the ‘photo’ column as this is present across all registers and every row.  However, I noticed that there are multiple photos per page, e.g. for SL137144 page 2 has 2 photos (4538 and 4539) so photo IDs don’t have a 1:1 relationship with pages.  If we can think of a way to address the page issue then I should be able to import the data.

Finally, I continued to work on the Anglo-Norman Dictionary project, fixing some issues relating to yoghs in the entries and researching a potentially large issue relating to the extraction of earliest citation dates.  Apparently there are a number of cases when the date for a citation that should be used is not the date as coded in the date section of the citation’s XML, but should instead be a date taken from a manuscript containing a variant form within the citation.  The problem is there is no flag to state when this situation occurs, instead it occurs whenever the form of the word in the citation is markedly different within the citation but similar in the variant text.  It seems unlikely that an automated script would be able to ascertain when to use the variant date as there is just so much variation between the forms.  This will need some further investigation, which I hope to be able to do next week.

Week Beginning 6th September 2021

I spent more than a day this week preparing my performance and development review form.  It’s the first time there’s been a PDR since before covid and it took some time to prepare everything.  Thankfully this blog provides a good record of everything I’ve done so I could base my form almost entirely on the material found here, which helped considerably.

Also this week I investigated and fixed an issue with the SCOTS corpus for Wendy Anderson.  One of the transcriptions of two speakers had the speaker IDs the wrong way round compared to the IDs in the metadata.  This was slightly complicated to sort out as I wasn’t sure whether it was better to change the participant metadata to match the IDs used in the text or vice-versa.  It turned out to be very difficult to change the IDs in the metadata as they are used to link numerous tables in the database, so instead I updated the text that’s displayed.  Rather strangely, the ‘download plan text’ file contained different incorrect IDs.  I fixed this as well, but it does make me worry that the IDs might be off in other plain text transcriptions too.  However, I looked at a couple of others and they seem ok, though, so perhaps it’s an isolated case.

I was contacted this week by a lecturer in English Literature who is intending to put a proposal together for a project to transcribe an author’s correspondence, and I spent some time writing a lengthy email with home helpful advice.  I also spoke to Jennifer Smith about her ‘Speak for Yersel’ project that’s starting this month, and we arranged to have a meeting the week after next.  I also spent quite a bit of time continuing to work on mockups for the STAR project’s websites based on feedback I’d received on the mockups I completed last week.  I created another four mockups with different colours, fonts and layouts, which should give the team plenty of options to decide from.  I also received more than a thousand new page images of library registers for the Books and Borrowing project and processed these and uploaded them to the server.  I’ll need to generate page records for them next week.

Finally, I continued to make updates to the Textbase search facilities for the Anglo-Norman Dictionary.  I updated genre headings to make them bigger and bolder, with more of a gap between the heading and the preceding items.  I also added a larger indent to the items within a genre and reordered the genres based on a new suggested order.  For each book I included the siglum as a link through to the book’s entry on the bibliography page and in the search results where a result’s page has an underscore in it the reference now displays volume and page number (e.g. 3_801 displays as ‘Volume 3, page 801’).  I updated the textbase text page so that page dividers in the continuous text also display volume and page in such cases.

Highlighted terms in the textbase text page no longer have padding around them (which was causing what looked like spaces when the term appears mid-word).  The text highlighting is unfortunately a bit of a blunt instrument, as one of the editors discovered by searching for the terms ‘le’ and fable’:  term 1 is located and highlighted first, then term 2 is.  In this example the first term is ‘le’ and the second term is ‘fable’.  Therefore the ‘le’ in ‘fable’ is highlighted during the first sweep and then ‘fable’ itself isn’t highlighted as it has already been changed to have the markup for the ‘le’ highlighting added to it and no longer matches ‘fable’.  Also, ‘le’ is matching some HTML tags buried in the text (‘style’), which is then breaking the HTML, which is why some HTML is getting displayed.  I’m not sure much can be done about any of this without a massive reworking of things, but it’s only an issue when searching for things like ‘le’ rather than actual content words so hopefully it’s not such a big deal.

The editor also wondered whether it would be possible to add in an option for searching and viewing multiple terms altogether but this would require me to rework the entire search and it’s not something I want to tackle if I can avoid it.  If a user wants to view the search results for different terms they can select two terms then open the full results in a new tab, repeating the process for each pair of terms they’re interested in, switching from tab to tab as required. Next week I’ll need to rename some of the textbase texts and split one of the texts into two separate texts, which is going to require me to regenerate the entire dataset.

Week Beginning 16th August 2021

I continued to work on the new textbase search facilities for the Anglo-Norman Dictionary this week.  I completed work on the required endpoints for the API, creating the facilities that would process a search term (with optional wildcards), limit the search to selected books and or genres and return either full search results in the case of an exact search for a term or a list of possible matching terms and the number of occurrences of each term.  I then worked on the front-end to enable a query to be processed and submitted to the API based on the choices made by the user.

By default any text entered will match any term that contains the text – e.g. enter ‘jour’ (without apostrophes) and you’ll find all forms containing the characters ‘jour’ anywhere e.g. ‘adjourner’, ‘journ’.  If you want to do an exact match you have to use double quotes – “jour”.  You can also use an asterisk at the beginning or end to match forms starting or ending with the term – ‘jour*’ and ‘*jour’ or an asterisk at both ends ‘*jour*’ will only find forms that contain the term somewhere in the middle.  You can also use a question mark wildcard to denote any single character, e.g. ‘am?n*’ will find words beginning ‘aman’, ‘amen’ etc.

If your selected form in your selected books / genres matches multiple forms then an intermediary page bringing up a list of matching forms and a count of the number of times each form appears will be displayed.  This is the same as how the ‘translation’ advanced search works, for example, and I wanted to maintain a consistent way of doing things across the site.  Select a specific form and the actual occurrences of each item in the texts will appear.  Above this list is a ‘Select another form’ button that returns you to the intermediary page.  If your search only brings back one form the intermediary page is skipped, and as all selection options appear in the URL it’s possible to bookmark / cite the search results too.

Whilst working on this I realised that I’d need to regenerate the data, as it became clear that many words have been erroneously joined together due to there being no space between words when one tag is closed and a following one is opened.  When the tags are then stripped out the forms get squashed together, which has led to some crazy forms such as ‘amendeezreamendezremaundez’.  Previously I’d not added spaces between tags as I was thinking that a space would have to be ended before a closing tag (e.g. ‘</’ becomes ‘ </’) and this would potentially mess up words that have tags in them, such as superscript tags in names like McDonald.  However, I realised I could instead do a find and replace to add spaces between a closing tag and an opening tag (‘><’ becomes ‘> <’, which would not mess up individual tags within words and wouldn’t have any further implications as I strip out all additional spaces when processing the texts for search purposes anyway.

I also decided that I should generate the ‘key-word in context’ (KWIC) for each word and store this in the database.  I was going to generate this on the fly every time a search results page was displayed but it seems more efficient to generate and store this once rather than do it every time.  I therefore updated by data processing script to generate the KWIC for each of the 3.5 million words as they were extracted from the texts.  This took some time to both implement and execute.  I decided to pull out the 10 words on either side of the term, which used the ‘word order’ column that gets generated as each page is processed.  Some complications were introduced in cases where the term is either before the tenth word on the page or there are less than ten words after the term on the page.  I such cases the script needed to look at the page before or after the current page in order to pull out the words and fill out the KWIC with the appropriate words on the other pages.

With the updates to data processing in place and a fair bit of testing of the KWIC facility carried out, I re-ran my scripts to regenerate the data and all looked good.  However, after inserting the KWIC data the querying of the tables slowed to a crawl.  On my local PC queries which were previously taking 0.5 seconds were taking more than 10 seconds, while on the server execution time was almost 30 seconds.  It was really baffling as the only difference was the search words table now had two additional fields (KWIC left and KWIC right), neither of which were being queried or returned in the query.  It seemed really strange that adding new columns could have such an effect if they were not even being used in a query.  I had to spend quite a bit of time investigating this, including looking at MySQL settings such as key buffer size and trying again to change storage engines, switching from MyISAM to InnoDB and back again to see what was going on.  Eventually I looked again at the indexes I’d created for the table, and decided to delete them and start over, in case this somehow jump-started the search speed.  I previously had the ‘word stripped’ column indexed in a multiple column index with page ID and word type (either main page or textual apparatus).  Instead I created an index of the ‘word stripped’ column on its own, and this immediately boosted performance.  Queries that were previously taking close to 30 seconds to execute on the server were now taking less than a second.  It was such a relief to have figured out what the issue was, as I had been considering whether my whole approach would need to be dropped and replaced by something completely different.

As I now had a useable search facility I continued to develop the front-end that would use this facility.  Previously the exact match for a term was bringing up just the term in question and a link through to the page the term appeared on, but now I could begin to incorporate the KWIC text too.  My initial idea was to use a tabular layout, with each word of the KWIC in a different column, with a clickable table heading that would allow the data to be ordered by any of the columns (e.g. order the data alphabetically by the first word to the left of the term).  However, after creating such a facility I realised it didn’t work very well.  The text just didn’t scan very well due to columns having to be the width of whatever the longest word in the column was, and the text just took up too much horizontal space.  Instead, I decided to revert to using an unordered list, with the KWIC left and KWIC right in separate spans, with KWIC left right aligned to push it up against the search term no matter what the length of the KWIC left text.  I split the KWIC text up into individual words and stored this in an array to enable each search result to be ordered by any word in the KWIC, and began working on a facility to change the order using a select box above the search results.  This is as far as I got this week, but I’m pretty confident that I’ll get things finished next week.  Here’s a screenshot of how the KWIC looks so far:

Also this week I had an email conversation with the other College of Arts developers about professional web designers after Stevie Barrett enquired about them, arranged to meet with Gerry Carruthers to discuss the journal he would like us to host, gave some advice to Thomas Clancy about mailing lists and spoke to Joanna Kopaczyk about a website she would like to set up for a conference she’s organising next year.

Week Beginning 9th August 2021

I’d taken last week off as our final break of the summer, and we spent it on the Kintyre peninsula.  We had a great time and were exceptionally lucky with the weather.  The rains began as we headed home and I returned to a regular week of work.  My major task for the week was to begin work on the search facilities for the Anglo-Norman Dictionary’s textbase, a collection of almost 80 lengthy texts for which I had previously created facilities to browse and view texts.  The editors wanted me to replicate the search options that were available through the old site, which enabled a user to select which texts to search (either individual texts or groups of texts arranged by genre), enter a single term to search (either a full match or partial match at the beginning or end of a word), select a specific term from a list of possible matches and then view each hit via a keyword in context (KWIC) interface, showing a specific number of words before and after the hit, with a link through to the full text opened at that specific point.

This is a pretty major development and I decided initially that I’d have two major tasks to tackle.  I’d have to categorise the texts by their genre and I’d have to research how best to handle full text searching including limiting to specific texts, KWIC and reordering KWIC, and linking through to specific pages and highlighting the results.  I reckoned it was potentially going to be tricky as I don’t have much experience with this kind of searching.  My initial thought was to see whether Apache Solr might be able to offer the required functionality.  I used this for the DSL’s advanced search, which searches the full text of the entries and returns snippets featuring the word, with the word highlighted and the word then highlighted throughout the entry when an entry in the results is loaded (e.g. https://dsl.ac.uk/results/dreich/fulltext/withquotes/both/).  This isn’t exactly what is required here, but I hoped that there might be further options I can explore.  Failing that I wondered whether I could repurpose the code for the Scottish Corpus of Texts and Speech.  I didn’t create this site, but I redeveloped it significantly a few years ago and may be able to borrow parts from the concordance search. E.g. https://scottishcorpus.ac.uk/advanced-search/ and select ‘general’ then ‘word search’ then ‘word / phrase (concordance)’ then search for ‘haggis’ and scroll down to the section under the map.  When opening a document you can then cycle through the matching terms, which are highlighted, e.g. https://scottishcorpus.ac.uk/document/?documentid=1572&highlight=haggis#match1.

After spending some further time with the old search facility and considering the issues I realised there are a lot of things to be considered regarding preparing the texts for search purposes.  I can’t just plug the entire texts in as only certain parts of them should be used for searching – no front or back matter, no notes, textual apparatus or references.  In addition, in order to properly ascertain which words follow on from each other all XML tags need to be removed too, and this introduces issues where no space has been entered between tags but a space needs to exist between the contents of the tags, e.g. ‘dEspayne</item><item>La charge’ would otherwise become ‘dEspayneLa charge’.

As I’d need to process the texts no matter which search facility I end up using I decided to focus on this first, and set up some processing scripts and a database on my local PC to work with the texts.  Initially I managed to extract the page contents for each required page, remove notes etc and strip the tags and line breaks so that the page content is one continuous block of text.

I realised that the old search seems to be case sensitive, which doesn’t seem very helpful.  E.g. search for ‘Leycestre’ and you find nothing – you need to enter ‘leycestre’, even though all 264 occurrences actually have a capital L.  I decided to make the new search case insensitive – so searching for ‘Leycestre’, ‘leycestre’ or ‘LEYCESTRE’ will bring back the same results.  Also, the old search limits the keyword in context display to pages.  E.g. the first ‘Leycestre’ hit has no text after it as it’s the last word on the page.  I’m intending to take the same approach as I’m processing text on a page-by-page basis.  I may be able to fill out the KWIC with text from the preceding / subsequent page if you consider this to be important, but it would be something I’d have to add in after the main work is completed.  The old search also limits the KWIC to text that’s on the same line, e.g. in a search for ‘arcevesque’ the result ‘L’arcevesque puis metre en grant confundei’ has no text before because it’s on a different line (it also chops off the end of ‘confundeisun’ for some reason).  The new KWIC will ignore breaks in the text (other than page breaks) when displaying the context.  I also realised that I need to know what to do about words that have apostrophes in them.  The old search splits words on the apostrophe, so for example you can search for arcevesque but not l’arcevesque.  I’m intending to do the same.  The old search retains both parts before and after the apostrophe as separate search terms, so for example in “qu’il” you can search for “qu” and “il” (but not “qu’il”).

After some discussions with the editor, I updated my system to include textual apparatus, stored in a separate field to the main page text.  With all of the text extracted I decided that I’d just try and make my own system initially, to see whether it would be possible.  I therefore created a script that would take each word from the extracted page and textual apparatus fields and store this in a separate table, ensuring that words with apostrophes in them are split into separate words and for search purposes all non-alphanumeric characters are removed and the text is stored as lower-case.  I also needed to store the word as it actually appears in the text, the word order on the page and whether the word is a main page word or in the textual apparatus.  This is because after finding a word I’ll need to extract those around it for the KWIC display.  After running my script I ended up with around 3.5 million rows in the ‘words’ table, and this is where I ran into some difficulties.

I ran some test queries on the local version of the database and all looked pretty promising, but after copying the data to the server and running the same queries it appeared that the server is unusably slow.  On my desktop a query  to find all occurrences of ‘jour’, with the word table joined to the page table and then to the text table completed in less than 0.5 seconds but on the server the same query took more than 16 seconds, so about 32 times slower.  I tried the same query a couple of times and the results are roughly the same each time.  My desktop PC is a Core i5 with 32GB of RAM, and the database is running on an NVMe M.2 SSD, which no doubt makes things quicker, but I wouldn’t expect it to be 32 times quicker.

I then did some further experiments with the server.  When I query the table containing the millions of rows on its own the query is fast (much less than a second).  I added a further index to the column that is used for the join to the page table (previously it was indexed, but in combination with other columns) and then when limiting the query to just these two tables the query runs at a fairly decent speed (about 0.5 seconds).  However, the full query involving all three tables still takes far too long, and I’m not sure why.  It’s very odd as there are indexes on the joining columns and the additional table is not big – it only has 77 rows.  I read somewhere that ordering the results by a column in the joined table can make things slower, as can using descending order on a column, so I tried updating the ordering but this has had no effect.  It’s really weird – I just can’t figure out why adding the table has such a negative effect on the performance and I may end up just having to incorporate some of the columns from the text table into the page table, even though it will mean duplicating data.  I also still don’t know why the performance is so different on my local PC either.

One final thing I tried was to change the database storage type.  I noticed that the three tables were set to use MyISAM storage rather than InnoDB, which the rest of the database was set to.  I migrated the tables to InnoDB in the hope that this might speed things up, but it’s actually slowed things down, both on my local PC and the server.  The two-table query now takes several seconds while the three-table query now takes about the same, so is quicker, but still too slow.  On my desktop PC the speed has doubled to about 1 second.  I therefore reverted back to using MyISAM.

I decided to leave the issue of database speed at that point and to focus on other things instead.  I added a new ‘genre’ column to the texts and added in the required categorisation.  I then updated the API to add in this new column and updated the ‘browse’ and ‘view’ front-ends so that genre now gets displayed.  I then began work on the front-end for the search, focussing on the options for listing texts by genre and adding in the options to select / deselect specific texts or entire genres of text.  This required quite a bit of HTML, JavaScript and CSS work and made a nice change from all of the data processing.  By the end of the week I’d completed work on the text selection facility, and next week I’ll tackle the actual processing of the search, at which point I’ll know whether my database way of handling things will be sufficiently speedy.

Also this week I had a chat with Eleanor Lawson about the STAR project that has recently begun.  There was a project meeting last week that unfortunately I wasn’t able to attend due to my holiday, so we had an email conversation about some of the technical issues that were raised at the meeting, including how it might be possible to view videos side by side and how a user may choose to select multiple videos to be played automatically one after the other.

I also fixed a couple of minor formatting issues for the DSL people and spoke to Katie Halsey, PI of the Books and Borrowing project about the development of the API for the project and the data export facilities.  I also received further feedback from Kirsteen McCue regarding the Data Management Plan for her AHRC proposal and went through this, responding to the comments and generating a slightly tweaked version of the plan.