Tag Archives: NodeGoat

And the winner is…

Snippet of the network visualization for the 502 publication sample described in this post; mind the prominent position of Reland’s “Palaestina Illustrata” (big green dot left on top) and the unconnected component in the lower right corner.

… Adrien Reland’s 1714 Palaestina ex monumentis veteribus illustrata!

And so the winner is me, too, for I guessed it correctly.

A two weeks’ post

But one thing after the other. First, the date: this is my blog post for

Saturday, March 9th, 2019, for Friday no. 22;

I seem to acquire a bad habit of posting on Saturdays rather than Fridays. For today, my excuses are, in chronological order, a) I had to help out at my son’s school, fixing some shelves; b) I had to figure out how to clear some financial matters regarding how to pay my salary from this project in a way that some of it actually ends up with me in the end; c) my youngest daughter stubbornly refused to go to sleep this evening. Obviously I am not very good at scheduling my home office work hours, for although I started out at 8:00 in the morning, by 21:30 I had got down to a meagre seven hours of working time. And d) I made a tiny configuring mistake, messed up my calculations, and had to start all over again, ruining some two hours of work. I suspect I better had not started calculating only at 21:30, but a), b), and c) forced me to.

Going for some metrics, step 1: Gathering data

But the calculations are crucial, because that’s what I originally intended to show you. As I announced in my last post, I now have finished three-quarters of my projected sample of scholarly journals, which has taken me two weeks to complete, clean, and analyse now.

That is to say, I have gone through the digitized versions of the Journal des Savants, the Philosophical Transactions, and the Maandelyke uittreksels, of Boekzaal der geleerde waerelt (more info here), between 1700 and 1799. I have added all issues of these journals referencing one of my four protagonists in any way to my database. In numbers, this means 117 issues of the Journal des Savants, 9 of the Philosophical Transactions (yes, only nine, but I have already argued they are somewhat special here), and 89 of the Maandelyke uittreksels – or 205 journal issues in total. In going through these issues I have examined each page on which at least one of my protagonists was referenced, identified all other scholars and all publications on that page, and stored these data, too, as a basis for co-citation analysis to identify perceived epistemic communities and their changes over time. This netted me another 287 quoted publications, and another 739 scholars referred to and/or connected to quoted publications (as authors, editors, translators etc.). I must confess that I am a bit proud of having managed to completely identify close to 690 of these, or more than 90%. But today everything will be about publications, so the really interesting figure is that of 502 publications in this sample.

Going for some metrics, step 2: Setting a framework for analysis

The problem that I now got was how to deal with these data. Plain visualizing did not work out anymore to really map out the intriguing details. So it needed to be done the rough way, by calculating metrics for the sample, hoping for something interesting to turn up. The visualizations did help me, in the way of pointing out which ways to structure the relations to be investigated would likely not yield good results. I first thought I might try something like mapping ‘shared contributors’, that is, connecting publications by way of the same scholars contributing to them (as authors, editors, or translators). This turned out to be pretty useless because it only favoured large edited collections who assembled texts from many different authors. And that’s the reason why this post is not about persons but publications only; the real co-citation analysis still has to wait for a bit. So it seemed better to bypass persons for the time being and only to link publications to publications, and I did so by way of quotations, that is, two publications are seen as connected if at least one of them quotes the other. That brought some interesting results about.

Going for some metrics, step 3: Finally going for metrics!

With the framework set up to network this publication sample, it was high time (half past nine already!) to start calculating metrics. To facilitate calculation, I started with assuming the network edges to be undirected, that is, a link from publication A via a quote in A to publication B works both ways, from A to B and from B to A. I know that this might be a questionable simplification; but as this may be tested by comparison, I will set this matter aside for a rainy day when I don’t have any idea what to write in this blog for the week to have the opportunity to at least redo these calculations for directed edges. (And as I once started tracking time in this post, it is now 4:00 in the morning and high time that I get to bed to have at least some sleep. I’ll continue around noon.) Moreover, as the structure of connections within what one takes to be a network depends on what the question is, and I am interesting in tracing perceived epistemic communities here – that is, publications seen as belonging to shared domains of knowledge – edge directionality is less important than it might be for other questions to ask. (This is a piece of midnight reasoning, but it still looks sound now at 10:30 on the day after.) I could as well also have collapsed the journals into edges connecting the rest of the publications to more explicitly claim this, but I wanted to also get an impression of the position of the individual journal issues, and so I kept them as nodes. Admittedly, this model does have the drawback that relying on quotations only provides only a quite simplified picture of the interconnections structuring domains of knowledge, but it should serve to give a good first impression of the sample’s general structure. Having sample and model settled, I used the opportunity to test Nodegoat’s analysis functions and calculated some metrics.

A lot of numbers

To keep the scores comparable across different metrics, I chose to normalize them where possible. A short look at the visualization (see header for larger picture) made clear what was to be expected: the network is disconnected. Not all publications are referred to as situated in overlapping domains of knowledge; some are distinctly separate from the rest. This is important here because it directly impacts the calculations of the closeness metric. Closeness serves to indicate how close any given node is to all other nodes in the network. In mapping out shared domains of knowledge this might be quite interesting, as nodes with low closeness scores might be supposed to partake of many fields at once being referenced in differing contexts, and thus be important to look at. But closeness measures path lengths to calculate the average distance from node X to all other nodes, and this does not work in disconnected networks (because two nodes between which there is no path would be at undefined = infinite distance from each other). Nodegoat provides a workaround to that by substituting a maximum distance for path length between unconnected nodes, this maximum being equal to the total number of nodes in the network.[1]

First: Betweenness

The first measure I calculated was betweenness[2] (because of the problems with closeness). Betweenness works on disconnected networks and thus was the obvious first choice. Roughly put, betweenness serves to indicate whether a certain node may be considered a gatekeeper, that is, if it is situated at a vital connection point in the network. In assuming an undirected graph, for mapping shared domains of knowledge this resembles closeness: it should point to publications situated at the intersection of several domains, and thus of potential importance for each of these. I must qualify this as ‘potential importance’ because only being situated between different fields of knowledge does not equal making important contributions to any of these fields; this will have to be cross-checked with other metrics, then.

Top 10 Betweenness

These are the top ten results in terms of betweenness for the whole of the network:

And those are the respective results for the top-scoring publications of/connected to:

Adrien Reland, by Betweenness

Eusèbe Renaudot, by Betweenness

Johannes Braun, by Betweenness

Thomas Gale, by Betweenness

What becomes directly apparent is that there are a lot Reland’s publications in the network, not only on vital connection points but also in total. In fact, 37 out of 287 non-journal publications have Reland as their main author/editor, or 12,9 %; the number does matter because I only added publications to this sample when coming across a quotation within the sample. Compared to 2.8 % for Eusebè Renaudot (8 publications), 1.7 % for Johannes Braun (5 publications), and 1.4 % for Thomas Gale (4 publications) this looks even more impressive. But a large output as such does not tell anything about the quality or the reception of that output. There also is one publication by Eusebè Renaudot among the top ten in betweenness, too.

Second: Closeness

As already said, the closeness scores for this sample are to be taken with a grain of salt because of the disconnectedness workaround implemented in Nodegoat. But the prediction I theoretically made when thinking about betweenness in this network – that it would line up quite closely with closeness because pointing to structurally similar positions within the network – did come true in looking at the results for closeness, so I am tempted to regard these results as usefull still. Important: The first score in the “A” for Analysis column is always the one discussed!

Top 10 by Closeness

The top ten publications of the network in terms of closeness scores are:

This again underscores the relevance of Reland’s publications within the network as a whole, and especially of his Palaestina Illustrata.

And those are the respective results for the top-scoring publications of/connected to:

Adrien Reland, by Closeness

Eusèbe Renaudot, by Closeness

Johannes Braun, by Closeness

Thomas Gale, by Closeness

Third: Degree

In undirected graphs such as this, Degree just counts the number of connections any given node in the network has. To be precise, Nodegoat counts the number of all edges starting from and ending at any given node, allowing for duplicates edges, thus producing high scores on average.[3] For an analysis of shared domains of knowledge as proposed here Degree should be relevant as a corrective. Whereas Betweenness and Closeness both point to structural positions of publications in regard to their location between domains of knowledge, Degree points to the importance of a given publication at this structural position. Or, to put it more precisely, to the attention generated by the publication in question, as I am focusing on quotations made by scholarly journals to this publication; and many such quotations do not necessarily point to the scholarly but in any case to the public impact a work made.

Top 10 by Degree

Looking at the Degree scores from this perspective makes clear that while Reland’s publications might share in more domains of knowledge, within their respective domains Renaudot’s publications obviously attracted comparable attention. And looking at the respective results for the top-scoring publications of/connected to Johannes Braun and Thomas Gale in comparison makes clear that they were not only tied more closely to special domains of knowledge but also attracted less attention by the journals in question, perhaps because of this more stringent specialisation. So here are the Degree scores for publications divided by individuals:

Adrien Reland, by Degree

Eusèbe Renaudot, by Degree

Johannes Braun, by Degree

Thomas Gale, by Degree

A cautionary note: It does not pay to put too much trust in Degree scores, because they tend to push publications situated within certain reference patterns. Consider the example of Mathurin Veyssière de la Croze’s Lexicon aegyptiaco-latinum, the Coptic dictionary edited and published by Karl Gottfried Woide in 1775 which has already featured in one of my posts.

With a Degree of 33 it scores shortly below a top 10 place in Degree which, following the reasoning laid out above, should indicate its importance for its particular domain(s) of knowledge – it attracted a lot of attention, because it has a lot of quotes. But all of these quotes are in fact derived from one singe journal issue, the June 1774 issue, part one, of the Journal des Savants, as becomes visible from the cross-references section of its database entry.  

And there it assembles all of these quotes because it features in many different respects in the announcement written by Woide himself to highlight his upcoming publication, which makes the reference section of this piece look a bit monothematic:

Snippet from the references contained in Woide’s publication announcement

So while Degree may be taken as an indicator of importance within a given field by capturing public attention, this is easily manipulated, and has to be cross-checked against another metric for validation.

Fourth: Pagerank

Nodegoat allows for calculating Pagerank scores, following the original Google algorithm. As Pagerank was originally invented to determine the relative importance of information sources (in this case, websites) in a network through which a user might randomly move, this model might also be applied to domains of knowledge in a web of publications, treating quotations as paper-borne hyperlinks. Random movement through the graph is facilitated by its undirectedness, so please keep in mind that modelling it as undirected may be a questionable decision, and don’t trust these metric too much. The good thing about Pagerank is that allows for determining both the relative importance of quoted publications and of quoting journals in this particular configuration, so I would like to use it to counterbalance the shortcomings of Degree pointed out above. In running Pagerank, Woide/la Croze’s dictionary disappears from the leading places all of a sudden, so this seems to work. The top publications according to Pagerank thus are:

Top 10 by Pagerank  

Surprise, surprise: The main hubs for distributing quotes – and thus sorting domains of knowledge – are journals! Makes me almost wonder why I chose to go by them in the first place… But more interesting in here is that there one publication from the 287 quoted works in the sample which managed to get a place in the top 10, and that is – surprise again! – Reland’s Palaestina Illustrata. This in turn is due by it being quoted by a number of journal issues (from all three journal series) which are themselves important quotation hubs, as becomes evident when looking at the cross-references to Palaestina Illustrata, that is, the publications in the sample linking to it.

This looks as if Pagerank indeed might be a good tool to determine the importance of a publication at its respective structural position, at best in combination with Degree (and Palaestina Illustrata scores high on both). So what about the top scores for the rest of the field?

Adrien Reland, by Pagerank

Eusèbe Renaudot, by Pagerank

Johannes Braun, by Pagerank

Thomas Gale, by Pagerank

Combining metrics

Well, that was a lot of tables, numbers, and scores. Well done! Only a few more to go. Now the last thing to do is to find a way to purposefully combine the different metrics results assembled so far to draw something relating to my larger research question from it. To do so, I ventured for a first try to identify the top 5 publications of each of my protagonists by comparison of the results I presented above. This yielded the following table[4]:

Top 5 by: Betweenness Closeness Pagerank Degree
Reland Palaestina ex monumentis veteribus illustrata Palaestina ex monumentis veteribus illustrata Palaestina ex monumentis veteribus illustrata Palaestina ex monumentis veteribus illustrata
Reland De nummis veteris Hebraeorum De nummis veteris Hebraeorum Antiquitates sacrae vet. Hebr. (4th ed.) De nummis veteris Hebraeorum
Reland De religione mohammedica De religione mohammedica Antiquitates sacrae veterum Hebraeorum De religione mohammedica
Reland Antiquitates sacrae veterum Hebraeorum Antiquitates sacrae veterum Hebraeorum Verhandeling van de godsdienst Antiquitates sacrae veterum Hebraeorum
Reland Encheiridion studiosi Dissertationum Miscellanearum Oratio de galli cantu Hierosolymae Decas exercitationum […] nomine Jehovae
Braun Commentarius in epistolam ad Hebraeos Commentarius in epistolam ad Hebraeos Leere der verbonden (4th ed.) Doctrina foederum
Braun Leere der verbonden (4th ed.) Vestitus sacerdotum Hebraeorum Avertissement necessaire aux eglises Leere der verbonden (4th ed.)
Braun Avertissement necessaire aux eglises Doctrina foederum Commentarius in epistolam ad Hebraeos Commentarius in epistolam ad Hebraeos
Braun Doctrina foederum Avertissement necessaire aux eglises Vestitus sacerdotum Hebraeorum Avertissement necessaire aux eglises
Braun Vestitus sacerdotum Hebraeorum Leere der verbonden (4th ed.) Doctrina foederum Vestitus sacerdotum Hebraeorum
Renaudot Historia patriarcharum Alexandrinorum Historia patriarcharum Alexandrinorum Liturgiarum Orientalium Collectio Liturgiarum Orientalium Collectio
Renaudot La perpetuité de la foy, 4 Liturgiarum Orientalium Collectio La perpetuité de la foy, 4 Defense de l'”histoire des patriaches”
Renaudot Liturgiarum Orientalium Collectio La perpetuité de la foy, 4 Anciennes relationes des Indes Anciennes relationes des Indes
Renaudot La perpetuité de la foy, 5 La perpetuité de la foy, 5 La perpetuité de la foy, 5 La perpetuité de la foy, 4
Renaudot Anciennes relationes des Indes Anciennes relationes des Indes Historia patriarcharum Alexandrinorum Genadii patriarchi homiliae
Gale Antonini iter Britanniarum commentarius Antonini iter Britanniarum commentarius Antonini iter Britanniarum commentarius Antonini iter Britanniarum commentarius
Gale Jamblichii de mysteriis liber Jamblichii de mysteriis liber Historiae poeticae scriptores antiqui Historiae Anglicanae scriptores V.2
Gale Historiae Anglicanae scriptores V.2 Historiae poeticae scriptores antiqui Historiae Anglicanae scriptores V.2 Historiae poeticae scriptores antiqui
Gale Historiae poeticae scriptores antiqui Historiae Anglicanae scriptores V.2 Jamblichii de mysteriis liber Jamblichii de mysteriis liber

Creating a Shortest Path Matrix

To fuse this into something more informative I thought I might utilize a special function of Nodegoat’s, and that is its ability to calculate shortest paths between given selections of nodes. In this case, this meant that I first settled on a matrix of four times four publications: The top 4 publications after comparing the scores of all metrics for each of my four protagonists. I chose to use the top four because only four of Gale’s publications made it into the sample, and this provides better comparability then. Now I used the Shortest Path function to calculate the length of the shortest path through this network from each of these publications to each other, and set the results down in form of a matrix.[5] It looks like this.

Conclusions

What does this tell me now? Well, first of all it highlights the isolated position of some of these publications, which obviously do not share domains of knowledge with others; this is true for the five marked with grey lines which have no connections to the rest of the matrix. It moreover points to the broader diversity of Reland’s publications compared to the rest of the network: His four publications all have connections within the matrix, while for Renaudot one publication does not and for Braun and Gale two publications each. Reland’s and Renaudot’s oeuvres and positions are similar in that both are internally coherent – the smallest paths to those of their own publications connected to the rest of the matrix are two each – and well-connected: their publications are not only connected to more of the rest of the matrix, they are also connected by shorter paths, making their works appear to be more central. Braun and Gale on the other hand are similar in having less many publications connected to the rest of the matrix, and in those publications being situated in strongly differing fields, as indicated by their larger internal distance from each other compared to Reland and Renaudot. And both of them are on the outskirts of the network rather than in the centre as indicated by the long shortest paths to other publications in the matrix.

This buttresses the claims I have brought forward based on other impressions already: That Reland and Renaudot form a comparable pair of actors, as Gale and Braun do; and that Reland seems to be the most versatile of all four, which might be the reason why he was the last of the four to get forgotten. And, as I already suspected last week, that his Palaestina Illustrata might be more relevant for this process than his other works, even if they are better known today (as his 1705 De religione mohammedica for instance).

But this has been a fairly static picture of a phenomenon spanning one century, so the next task will be to dynamize it. Work to do!


[1] So in looking at the closeness scores in the following, always keep in mind that the maximum path length taken into consideration is 502.

[2] With duplicated edges taken into account and weighted according to distance, that is, duplicates add edge length.

[3] And Nodegoat does not automatically normalize Degree centrality. But this is an easy operation: If you want to do so, just divide any absolute Degree score by the total number of edges in the network, in this case, 2577.

[4] Publications marked in red only appear once in the table and are therefore discounted from further  comparison.

[5] Please keep in mind that shortest paths are elongated in my setting because two quoted publications A and B are (almost) always connected via a journal in between, so the usual shortest path possible between A and B is two links (A –link one– Journal  –link two– B).  

One Week with Publications

Friday No. 5, November 2nd, 2018

Last friday I decided that my data model needed a bit more testing than possible by 31 out of 68 JSTOR entries retrieved by searching for “Relandus”. This week therefore was devoted to entering data – and having done that, I can definitely say at least one thing: I need a break. I hope I’ll make to the archive again next week. But before I’m off, are there any other results?

From 31 to 82

I projected to complete my first package of 68 JSTOR hits retrieved by “Relandus”, four which actually pertained to something else, so that made 64. And to see if this is representative data set I set up a search with the same parameters for “Braunius” to get articles related to Johannes Braun, which returned 67 hits. Looks like a good match, so I had a go at it. Deducing anything unrelated to entering data I spent roughly 35 hours this week on processing these results, which took me through all 33 remaining Reland hits and through the first 25 Braun hits (seven of which were related to something else, so 18 remained). Adding the 18 hours I did last week on 31 items from the Reland set, this makes 82 retrieved JSTOR items processed in 53 hours. On average this means that entering one JSTOR items took me about 40 minutes. This should be a bit disappointing as I only needed 30 minutes each for the first 31 and had expected the average time needed to sink rather than to rise. But to put this into the right perspective, the first 31 items netted me 93 references in total, or three per item. From the 51 items I processed this week however I made 343 references altogether – or 6.7 per item, more than twice as much as in the first part of the set. So I am not entirely unhappy with spending 30% more time per item to get more than 200% more results. In principle, at least…

Numbers, numbers, numbers

But first of all let’s take the counting one step further and see how working through this set has played out in numbers of database objects. In the course of working through these 82 JSTOR entries I added to the database:

Persons: 229; Publications: 169; Publishing Houses: 89; Institutions: 45; Web Sources: 13; and Families: 7.

In total: 552 new objects, or about 6.7 per item (yes, again…). And to put these numbers in perspective also, this meant a rise in each category by:

  • Persons: 28%
  • Publications: 30%
  • Publishing Houses: 39%
  • Institutions: 31%
  • Web Sources: 19%
  • Families: 17,5%.

Or, to put it simply, my database has just grown by more than a quarter in eight days. Sounds nice. But do these data tell me anything new?

(Dis)Connected references…

Not that much, I must confess, because I haven’t yet analysed them very thoroughly. But what is directly visible is, first of all, that from the point of the references the Braun set and the Reland set are quite distinct and share only a few interconnections, which is interesting.

Set “Relandus/Braunius” visualized by publications (green) connected to persons (red) by references

And while the Reland set contains more direct references to Reland himself (he’s the big red dot in the middle of the larger wheel), the Braun set contains those publications with more references at all (the two big green dots within the smaller wheel). Of course the comparison is a bit unfair because the Braun set is much smaller and not yet completely processed. But it points in the direction of disciplinary foci as the main cause for such divergences.

… and intermittent references…

And, if I may come back to my concept of a pattern of intermittent referencing as a visible marker for being structurally forgotten, so far both sets dovetail with this quite nicely.

Set “Relandus/Braunius” visualized chronologically

Although an aggregated chronological visualization makes the impression of a continuous referencing pattern with ups and downs, in a more detailed view the three main clusters (1878-1908; 1918-1948; 1968-2008) break down into singular spikes and large plateaus.

Set “Relandus/Braunius” visualized chronologically, close-up

… and the catch

Of course there is a catch. If this would be all there is to it, were would the challenge be? Well, the catch is that this is just the tip of the iceberg. If I perform the same search for “Reland” instead of “Relandus”, JSTOR returns 596 results instead of 68.

JSTOR hits for “Reland NOT Ireland”

And querying just for “Braun” is plain nonsense. Even with “Johannes Braun” it’s still tricky to sort out mishits. The same goes for “Thomas Gale”; “Eusebè Renaudot” works better, but returns a corresponding number of hits. Taken together these searches amount to about 700 or 800 real hits, roundabout ten times as much as I have now processed in eight days. And even if I chose to spent 80 days only working through those JSTOR results, this is only one platform of many to be queried. Which means am back from where I started. It will not work out like this. I have to find another way to deal with this. When I’m back from the archives.

Three Days of Publications

Friday N°4, October 25th, 2018

After having postponed my visit to Groningen because first of all I would have had to go to Leeuwarden anyway, and second because of the sheer wealth of the Boeles family archive (someone out there searching materials to write a niece solid biography with?). This prompted me to first get a better grip on Johannes Braun as to know what I really would want to look at among Boeles’ papers – and after a not that successful visit to the Utrechts archive on that behalf (well, at least I know now what Braun’s heterodoxy was, he was suspected to be a Unitarian[1] and an enemy of the social order[2]) – I decided that this week should be used to test my data model a bit more thoroughly.

What’s to be tested?

The basic idea behind the model is that is shall be able to diachronically capture reception processes. Therefore I was intrigued by NodeGoat from the start, because this is a core feature of the program, and have kept using it to my great satisfaction since (thanks a lot, Pim and Geerd!) To be able to capture these reception processes I first introduced the object category of “publications” for everything which was intended to be published.

 

The “publication” category in my NodeGoat database

For tracking references more specifically than just by a global descriptor “Refers to” I then introduced the sub-object category of “Reference” which allows me to capture who is referenced, which publications is quoted (if any), and whether a journal or other serial source is referred to (if any). To be able to run co-citation analysis later on, each reference is assigned the page number on which it occurs in the original publication.

A publication with its references

I decided to keep references as simple as possible. This means they are basically singular: Each reference is made to exactly one person, up to one publication, and up to one journal. They are not unique as there may be two references containing exactly the same data, which can happen if someone is quoted with the same work more than once on the same page. While being able to capture most of the relevant details very well, admittedly this model is a bit prone to proliferate references whenever things get complicated. If, for instance, a person such as Adrien Reland is referred to with two editions of his book “De religione Mohammedica” (that of 1705 and that of 1718) in one sentence, in the language of the model this translates into two references to Reland on the same page, one to the 1705 edition and one to the 1718 one. Especially in cases where there are many people and works referred to on the same page, it can get quite tiresome to enter all these data.

Named Entity Recognition, 100% handcrafted

And this is due to the fact that I formulated this concept while working with 18th century learned journals, knowing for sure that none of the data I needed for my analysis was available in standardized linked open data. So while working with databases all the time, which supply me with the digitized images and texts and sometimes even allow for full-text searches pointing me to the references to be picked, entering the data and identifying persons, places, and texts remains work to be done by hand. As this is really nothing else than plain Named Entity Recognition, in theory it might be done automatically. But I suspect that I would need a fairly advanced AI to be able to process data from these source matters, as they abound with non-standardized orthography, obsolete name variants, arbitrary abbreviations, misspellings and even outright mistakes such as mistaken identities or confusion of dates.

Starting from the other end of time

So what I did this week was trying to test the methodology and the model on a sample of a more recent variety of texts I also have to process, those that relate to my four main persons from today, or from not-so-long-ago (compared to the 18th century, at least). Starting from the other end of time I simply used JSTOR , with an advanced search set to “All content” and “Relandus” as search parameter. This returned 68 results, 53 of which I can with my JSTOR account access in full-text and 15 which I can’t. With what was left of the week after finishing all other tasks scheduled for these days I ended up with around 18 hours for a first go at these results. This took me through 31 of the 68, not quite 50% of the sample. The average time spent on each item was around 34 minutes, which I would consider as not too bad for the start. For the start because as in each new data set to be entered there were much more new identities to be identified than already known ones to be just related to the new items. These 31 items, most of which were journal articles from between 1883 and 2010, netted me a total of 88 new persons to be identified and added to the database along with another 11 new publications cited within the items. They also brought with them 23 new institutions such as the journals they were published in, and 25 new publishing houses. All in all, 31 items from the 68 search results netted me a total of 178 new database objects, or more than 5 per item (on average). Which means that it now takes me about 6 minutes to identify an unknown entity and add it to the database, at least in a form that I can work with.  As related items usually share at least some of their entities – which is why they are related, of course – over time the percentage of new objects to be added to the database declines against those already known, which should speed up the processing. Well, I’ll know better next week after having gone through the remaining 37 search results…

And what about references?

One of the publications with richer reference profiles

But if references are the key relational feature of the data model, how many references did I get out of these 31 search result items? That’s the really interesting number, isn’t it? And it is… 93. On average exactly three useful references, but in this case the average is deceptive. The majority of these texts made only one reference at all, and then there was a smaller group counting between 7 and 11 references each. Which is of course due to some publications centring on a special field of inquiry, in this case studies of early modern orientalism and/or early modern Dutch academia, and others focusing on more distantly related fields, such as – in this case – linguistics or biblical geography. This is not bad as such, and already produces useful visualizations. But it also means that, on average, 11 minutes of work are necessary to generate 1 meaningful reference. So this might be a point to ponder again if the method as it is now is really suited to the project. Will it work out like this? Seems like a bit more testing is needed. Looks like next week has just got a new objective…

Sample of items, linked by references to other objects in the database


[1] Utrechts Archief, 17.36, Uittreksel uit de resoluties van de Staaten van Groningen betreffende de theologische geschilpunten tussen de Groninger professoren Samuel Desmarets en Jacobus Alting, 1669, en Johannes à Marck en Johannes Braun, 1681–1690, p. [3]: March 1st, 1689.

[2] Utrechts Archief, 17.36, Uittreksel uit de resoluties van de Staaten van Groningen betreffende de theologische geschilpunten tussen de Groninger professoren Samuel Desmarets en Jacobus Alting, 1669, en Johannes à Marck en Johannes Braun, 1681–1690, p. [1]–[2]: May 14th, 1686.