5.1.1 Query explanation https://api.triplydb.com/s/l6M4n5Eff

§21 The query (Figure 3) begins with a SELECT statement, which identifies the variable values to be returned by the query. The SELECT statement here (lines 9 to 10) includes variables that will return only distinct, or different, manuscript values and production place values. Also included are production timespans, though the timespan is not essential to the original query. Multiple production place and production timespan values associated with the same manuscript value are concatenated to avoid showing the duplicated values within the same manuscript record.

Figure 3

SPARQL query for Query 1.

§22 The places are limited to those associated with the Getty’s Thesaurus of Geographical Names (TGN) identifiers for Northern Italy (tgn_4005363) and Lombardy (tgn_7003237) (line 13). The predicates in line 15 (ecrm:P108_has_produced/ecrm:P7_took_place_at/gvp:broaderPreferred*) allow the capture of manuscripts associated with these places as well as all other places expressed within the hierarchy of the TGN terms. The asterisk symbol at the end of the predicate gvp:broaderPreferred* tells the query to include all results that equal the Getty Thesaurus of Geographic Names URIs for Northern Italy and Lombardy as well as any places that are nested within them. Lines 18 to 21 make optional the association between a value in production place and a manuscript, and lines 22 to 24 do the same for the production timespan.

5.1.2 Results

§23 The query returns 1,702 instances of manuscripts, or manifestation singletons, in the combined dataset that contain the TGN IDs for Northern Italy (tgn_4005363) and for Lombardy (tgn_7003272) as a production place value. The predicate gvp:broaderpreferred* in line 15 of the query also enables the capture of cities and sites within Lombardy without having to identify and enter all TGN IDs associated with Lombardy. For example, Results 3 to 6 show “manifestation singletons,” which is how the FRBRoo ontology defines a manuscript object, with Milan as the production place because Milan is contained within Lombardy in the TGN hierarchy (http://vocab.getty.edu/tgn/7003150).

§24 The results also show manifestation singletons with multiple production places. These results indicate more than one place attribution has been assigned to a particular manifestation singleton. There are two reasons for this result. The first has to do with the way that manuscripts are often described: a source description identifies two or more possible places of production either because a cataloguer is hedging bets, for example, a manuscript could be described as from “Austria or Northern Italy” (http://ldf.fi/mmm/manifestation_singleton/sdbm_24767), or because a manuscript contains two component parts that were produced in different places and later bound together. The second reason is due to the data modelling: two or more of the sources could give two different places, as in this example in which the Bodleian record gives one place of production and the SDBM gives another: http://ldf.fi/mmm/manifestation_singleton/bodley_manuscript_2010. In the case of the SDBM, a manuscript record may contain two or more entries that give different location data.

5.1.3 Lessons learned

§25 Some general conclusions can be drawn about the interpretation of the dataset based on these results. The results highlight inconsistencies inherent to manuscript description dependent upon human observation: differing opinions (Austria or Northern Italy?) or knowledge changes across time (it was considered to be made in Northern Italy, but recent studies now indicate that it may have been produced in Siena), and inconsistencies in data entry (production place was not provided in the source data). (The SDBM draws its data from catalogue sources that can vary widely in the amount of detail provided in manuscript description, from simple identification of author, title, and date to full codicological descriptions; it is common therefore for many details relating to the physical description of a manuscript not to be provided.) The query results therefore cannot be taken at face value and researchers must navigate through the manuscript links in the MMM record for further exploration and discovery.

§26 A review of the results for this query raises the question: are the SPARQL results better than the results from a similar query in the MMM portal or separate queries in the original source datasets? The MMM portal and all three source datasets represent places hierarchically based on LOD authorities, including TGN. Querying the original data sources would obviously lack the efficiency of the aggregated dataset, but a search in the MMM portal returns the same results as the SPARQL query (https://mappingmanuscriptmigrations.org/en/manuscripts/faceted-search/table?page=0), and the visualization tool allows drilling down in the search results much more effectively. Thus, this particular SPARQL query offers only limited advantage over more direct searching in the source datasets and no advantage over the portal.

§27 While the query did not improve on the results provided by the MMM portal, the process of building the query gave shape to the data and insight into the limitations and character of the source data. This exercise, along with other early, relatively simple queries the group created, introduced the building blocks for SPARQL queries, like place and timespan techniques, that were returned to time and again.

5.2.1 Query explanation

5.2.1.1 Step 1 (Figure 4): https://api.triplydb.com/s/GfPEtMgxX

Figure 4

SPARQL query for Query 2: Step 1.

§30 This stage of the query finds manuscripts that were produced in Castile and contain texts written in the Spanish language. Following the SELECT statement, Line 10 uses a VALUES clause to assign the specific URIs for the ?place variable, including the general region of Castile and more specific locations, both historical and modern, within the region (Valladolid, Toledo, Burgos, Madrid, Ciudad Real, Ávila, and Guadalajara). Lines 11 to 13 include statements defining the ?manuscript variable as a “manifestation singleton” (efrbroo:F4_Manifestation_Singleton). This variable links to the ?expression variable and returns the human-readabe label for the manuscript (?manuscript_label). The ?expression variable is the text within a manuscript, following the FRBRoo conceptual model that defines the relationships between works, expressions, manifestations, and items in bibliographic records. Line 15 collects the label of the expression. Line 16 states that all expressions included in the results must be written in the Spanish language, encoded as the URI <http://ldf.fi/mmm/language/sdbm_8>.

§31 In MMM, manuscripts are linked to information about their place of production via a production event class. Lines 18–19 return information about these production events by stating that production events (represented by the ?production variable) are linked to manuscripts via the ecrm:P108_has_produced property, and occurred at a specific place (represented by the ?place variable). This variable is the same variable defined in line 10; all manuscripts included in the results will thus have a production place that matches one of those values. Line 21 returns the human-readable label for the ?place variable by using the skos:prefLabel predicate to return the label for the ?place variable.

5.2.1.2 Step 2 (Figure 5): https://api.triplydb.com/s/C83qkzA_h

Figure 5

SPARQL query for Query 2: Step 2.

§32 In the next step, we modified the previous query to include “produced for” information. Commissioning data is part of the production event class, so we add the ?commissioner variable at line 21 by linking it to the ?production event variable via the predicate mmms:carried_out_by_as_commissioner. Line 23 collects the labels for the ?commissioner. This modified query produces zero results, however, which tells us that no commissioning data is available for this group of manuscripts in the MMM dataset. We were thus not able to continue the query to find out more about later ownership.

5.2.2 Results

§33 The initial query produced a list of 286 texts, or “expressions,” in manuscripts which were produced in Castile and written in the Spanish language. Amending the query to look for the person or organization who commissioned the production of these manuscripts produced no results. Further exploration of the data showed that the MMM-specific property “carried_out_by_as_commissioner” is only relevant to the Bibale data. The SDBM identifies provenance agents but does not distinguish whether former owners could also be commissioners. The Bodleian records sometimes include ownership information, like the presence of a coat of arms, that suggests a manuscript was commissioned, but this encoding is not expressed as structured data that can be mapped to the MMM data model. A separate query to show which manuscripts in the whole dataset have a named commissioner (https://api.triplydb.com/s/1tQPkY-au) returned 234 records from Bibale. These results have no overlap with the manuscripts produced in Castile.

§34 As a result, this research question cannot be answered directly. An alternative would be to find the earliest owners of Castilian manuscripts as a proxy for potential commissioners. In the course of investigating this problem, the team produced an ancillary query to locate the distinct places of production and the owners of Spanish-language manuscripts produced in Castile: https://api.triplydb.com/s/M5lTr-KYy. The results can be inspected manually to find religious houses as the earliest owners, since the MMM data do not specify types of institutions. This approach avoids the dead-end of the commissioning relationship and can then be refined to look for 19th-century English owners and present-day American owners. This query in fact finds three Spanish-language manuscripts produced in Castile with religious houses as their (presumably first) owners: two from Madrid, (http://ldf.fi/mmm/manifestation_singleton/bibale_40694 and http://ldf.fi/mmm/manifestation_singleton/sdbm_23689), and one from Burgos (http://ldf.fi/mmm/manifestation_singleton/sdbm_5013). One of these was later owned by a 19th-century British collector (Thomas Phillipps), while another is now in a North American library (University of California Berkeley).

5.2.3 Lessons learned

§35 This research question, which combined place of origin, text language, script, type of commissioner, and then added the later location and ownership of manuscripts, was designed to test the limits of the dataset and is plainly artificial. A pattern emerged during the development of this query that we saw frequently. Often, the source datasets do not contain the specific information sought in the question, or do not encode it in a way that can be mapped to the specific MMM property. The Bodleian catalogue includes 78 cases where persons have the role statement “commissioner, dedicatee, or patron,” including MS. Lat. class. d. 38, a Latin manuscript containing the arms of King Alfonso V of Aragon (https://medieval.bodleian.ox.ac.uk/catalog/manuscript_6383). In the MMM data, he is encoded as an owner of this manuscript, but is not linked to its production event. This suggests that some re-thinking of the transformation and mapping of personal role statements from the Bodleian data in particular might be worth considering.

§36 Both “Castile” and “Spanish” are also problematic in this query. Historical regions like the Kingdom of Castile are not reflected in the TGN hierarchy of places, which is based on current administrative and jurisdictional boundaries, so the property gvp:broaderpreferred cannot be used. For this query, the ?place variable had to be bound to a list of specific place URLs from the TGN that was roughly comprehensive. The lack of availability of geographical hierarchy information, and the fact that historical boundaries change over time, mean that there is no simple method for capturing places within historical regions. Records that represent Castilian manuscripts may simply list Spain as the place of production, but there is no way to determine more specific locations within Spain in the query.

§37 The term “Spanish” for language is also ambiguous. Spanish in its modern sense is a post-medieval phenomenon (Penny 2002); the MMM data sources are inconsistent in their encoding of medieval languages from the Iberian peninsula. The fullest and most accurate way of constructing this query would involve inspecting all these varieties of languages and places in the data sources, seeing the extent to which they are reflected in the MMM data, and ascertaining how best to specify them in the SPARQL query. Even a cursory look suggests a significant level of inconsistency in the source data. These considerations would still apply if the question was made much more specific along these lines: Which manuscripts containing texts in a vernacular language were produced in the Kingdom of Castile as it existed in 1217?

5.3.1 Query explanation

5.3.1.1 Step 1 (Figure 6): Provenance events occurring in France: https://api.triplydb.com/s/ZWE5m487i

§39 The first step of this query aims to identify all provenance events (dates optional) that occurred in France. Following the SELECT statement, Line 9 assigns a specific value to the ?event_type_uri variable, ecrm:E10_Transfer_of_Custody, by using the VALUES clause. Thus, every event type returned in the results will be a provenance event involving the transfer of a manuscript from one owner to another, as opposed to more generic provenance events where a direct transfer of ownership is not necessarily known or confirmed by the data. Line 11 states that every location returned in the results (represented by the ?place_uri variable) must be within the boundaries of France using the same predicate gvp:broaderPreferred* that was used in the first case study. Line 14 introduces the ?event_uri variable, stating that every ?event_uri must have occurred at the places assigned to (ecrm:P7_took_place_at) the ?place_uri variable in Line 11. Lines 16–17 further define the types of information we return about events. In line 16, the symbol a is a shorthand for the rdf:type predicate to indicate that the ?event_uri variable is an instance of the ?event_type_uri class, which we defined in line 9 as a transfer of custody event. Line 18 is an optional clause that includes the date that an event took place, if that information is present in the data.

Figure 6

SPARQL query for Query 3: Step 1.

5.3.1.2 Step 2 (Figure 7): Manuscripts and their provenance events (dates optional) that occurred in France: https://api.triplydb.com/s/L1Pd3P9ZM

Figure 7

SPARQL query for Query 3: Step 2.

§40 Building on the above query, the second query’s results include all the manuscripts associated with provenance events that occurred in France, with the dates on which they occurred if known. Line 19 states that the ?event_uri variable is linked to the ?manuscript variable via two potential provenance event predicates: either transfer of custody events or observed manuscript events, which are provenance events where a direct transfer of custody is not confirmed in the data.

5.3.1.3 Step 3 (Figure 8). Manuscripts with their titles (optional), that had a provenance event that occurred in France in the 17th century: https://api.triplydb.com/s/WVeDNDp7V

Figure 8

SPARQL query for Query 3: Step 3.

§41 This query expands and refines the results further by adding the titles of works within the manuscripts and limiting the timeframe of provenance events to those that occurred in the 17th century. Lines 24–25 feature an OPTIONAL clause to retrieve the works included in the manuscripts (if known) and the labels of those works, represented by the variable ?titles.

§42 Lines 29–34 include statements related to the dates when a provenance event took place. For this research question, we are interested in events that occurred in the 17th century. The range of timespans included in the results need to have begun after the year 1599 but before the year 1700. To specify these parameters in SPARQL, we take the beginning of the timespan specified in each event (the ?begin variable), use the BIND and YEAR functions to extract the year from each timespan and assign each year to a new variable, ?year, and then FILTER the results to include only those years that are less than 1700 but greater than 1599.

5.3.1.4 Step 4 (Figure 9). Manuscripts with texts by authors who lived between 450–1500, with provenance events that occurred in France in the 17th century: https://api.triplydb.com/s/_9cC7UFM-

Figure 9

SPARQL query for Query 3: Step 4.

§43 This query adds information about authors and their life dates to the results. The strategy for limiting authors by their life dates is similar to the route taken in the previous query to find 17th century provenance events. Since the research question is interested in works composed by medieval authors, our results need to be limited to authors who lived during the medieval period, which we defined as between 450–1550 CE. Works are linked to their authors via the mmm:carried_out_by_as_possible_author predicate, as seen in lines 33–34. Authors with known life dates will have their birth and/or death dates (which could each vary widely in specificity from a precise date to a range of time) stored separately in the database, so we need to filter on birth and death dates separately. The parameters for the authors’ births are stated in line 35–39. We link from the author to their birth event (line 35), from that birth event to the timespan for that event, and then to the beginning of the timespan (?author_birth_begin). Just as in the previous query, we use the BIND and FILTER functions on lines 38–39 to extract the year from the timespan and then filter to include only years that are less than 1500 but greater than 450. We use the same process to limit authors’ death dates to the medieval period in lines 41–45, by using predicates specific to author death events and limiting the dates to between 500 and 1550.

5.3.2 Results

§44 Query 3: Step 1 returned 1,765 results. Event dates ranged from “after 877” (e.g., <http://ldf.fi/mmm/event/bibale_transfer_association:3972>) to “2020-04-01 – 2020-04-03” (e.g., <http://ldf.fi/mmm/event/sdbm_source_observation_260557). About 20% of these records had no associated date. The second query returned 47,805 results. More than 96% of these were generic “manuscript-related events” rather than transfers of custody. About 50% of them had no associated date. The third query reduced the number of records drastically. Only 1757 records were identified as “transfer of custody” or “observed ownership” events that could be localized to 17th-century France.

§45 When authors’ birth and death dates were added to find “medieval” authors in the fourth query, the number of results was further reduced to 1,262 records. This list contains all the possible combinations of authors, works, dates, and manuscripts. The query can be analyzed to reveal that the list contains 264 manuscripts, 757 distinct works, and 153 different authors. Because the titles of works have not been harmonized across versions in different languages—and also because of the way in which the SDBM records multiple works contained in a single manuscript—it is impossible to say with any certainty which work occurs most frequently. (In the SDBM, multiple works and multiple authors occurring in the same manuscript are listed separately and are not linked to each other. This means that the MMM mapping has to describe each author as the “possible author” of each work, even though they may only be the author of one of the works in question.) But the most frequently occurring authors are clear: Isidore of Seville (25 manuscripts), Bede (22), Bernard of Clairvaux (13), Anselm of Canterbury (12), and Boethius (11).

5.3.3 Lessons learned

§46 The complexity of this research question meant that we had to break it down into parts, write queries for each part, and then assemble these into a single SPARQL query. It also revealed that questions can be expressed in a form that is difficult to map to the terms and relationships used in the data model and the aggregated dataset. To approach a set of results that could be used to answer the question “what was the most popular text?” meant tackling a series of definitional problems and making choices about how best to define them in the context of the MMM data.

§47 The phrase “most popular text” is ambiguous, for a start. It could mean the most-read text, the most-quoted text, the most-owned text, or the most-circulated text. Only the latter two have any relevance to the MMM data, since they can be expressed respectively in terms of “the text in those manuscripts with the most recorded owners” in 17th-century France, or “the text in those manuscripts with the most ownership events” in the same period. Does the question refer to manuscript owners associated with France, or manuscript provenance events which occurred in France? Does “medieval author” cover anonymous or pseudonymous works and expressions as well as those with known authors? If so, how do we identify anonymous “medieval” texts, since works and expressions do not have dates directly associated with them?

§48 Whatever choices were made in relation to these definitional difficulties, the important point was to ensure that those choices were documented and explained. It might also have been possible to consider reframing the question in a less prescriptive way: “Which manuscripts with medieval texts were owned by French collectors in the 17th century?” This could have been addressed by identifying owners living in France in the 17th century and looking at the manuscripts they owned and the associated works.

§49 As mentioned earlier, one factor affecting these results significantly is that titles of works have not been harmonized across translations in different languages. There is little in the way of authoritative Linked Open Data vocabularies and identifiers for medieval and Renaissance works, and the absence of consistent conventional titles for works in this period makes the process of reconciling them between their occurrences in different manuscripts extremely difficult (Sharpe 2003). Without this kind of reconciliation, we cannot easily construct a query that takes a work and looks for all manuscripts containing that work. We should either try to identify all the variant titles of a work and include them in the query, or focus on manuscripts and authors instead. The way in which the SDBM treats multiple works in a single manuscript (as described above) also has a significant effect on queries of this kind.

5.3.4 New research questions and wider explorations

§50 The three case studies considered so far attempted to apply research questions devised before the data model was designed and implemented. The results of each query were mixed. While the simplest query produced the expected results primarily because of its simplicity, it did not really test the ability of the dataset to return results. The more complex questions with a significantly greater degree of ambiguity were much harder to translate into the elements and relationships expressed in the data model. They revealed, amongst other things, that some queries could be too specific or too complex in their combination of criteria to produce meaningful results. They also revealed that some relationships in the MMM data (e.g., between authors and works) were too ambiguous to produce reliable results. And they showed that questions involving pre-modern languages or pre-modern political and administrative jurisdictions needed careful mapping to modern authoritative vocabularies for places and languages. But they helped to teach some of the intricacies of SPARQL in the context of a relatively complex data model and a dataset that contains important ambiguities.

§51 Moving the SPARQL queries beyond the initial set of research questions became an important goal and the focus of more recent workshop sessions. For this second round of investigations, we looked particularly at ways of visualizing data in response to comparative quantitative and exploratory questions. We also examined ways of extending the reach of questions by using data sources outside MMM to add missing contextual information. The questions were mostly derived from active research projects into the history of manuscript collecting, a topic for which the MMM data should be particularly relevant.

5.4.1 Query explanation

5.4.1.1 Step 1 (Figure 10): Manuscript production year averages and ratios of height: width: https://api.triplydb.com/s/nfhgCrlyB

Figure 10

SPARQL query for Query 4: Step 1 (lines 1–24).

§54 This query begins with a very simple SELECT statement that includes only two variables: one representing a manuscript’s production year average, and another representing the ratio of a manuscript’s size. We defined this ratio as a manuscript’s average height divided by its average width. In MMM, manuscripts often have multiple different values for their heights, widths, and production years because our data about them comes from many different sources created over time. This necessitates that we use the averages of these values.

§55 Calculating averages requires a subquery nested within the WHERE clause of our main query, beginning on lines 12–14. The SELECT statement in the subquery begins with the ?manuscript variable, followed by three instances of the average aggregate function (AVG) that will calculate the averages of the ?height_mm, ?width_mm, and ?production_year variables. The WHERE clause beginning on line 15 defines the desired triple (i.e., subject–predicate–object) patterns in these variables. Lines 16–19 pertain to the ?manuscript variable, defining it as a manifestation singleton and returning height, width, and work data. Lines 21–24 refine the height and width information by returning the value of those fields and defining that value as a unit of length in millimeters.

§56 To narrow the results of this query to only include liturgical manuscripts, we can filter the manuscripts based on their text titles, which are modeled as work labels in MMM (Figure 11). Line 26 includes a FILTER clause that refines the results to only include manuscripts that contain works that include the characters “missal,” “gradual,” “breviar,” or “antiphon” in their titles. The character strings will be matched exactly, so the work labels are abbreviated in order to accommodate the various spellings found in MMM’s data sources. Likewise, each CONTAINS function also includes the LCASE function to convert the values in the ?work_label field to all lowercase letters so as to include both upper and lowercase title variations in our FILTER clause.

Figure 11

SPARQL query for Query 4: Step 1 (lines 25–35).

§57 Lines 28–31 construct the ?production_year variable. Production event information is its own class in MMM, which links to specific manuscripts via the ecrm:P108_has_produced predicate (line 28) and timespan data via the ecrm:P4_has_time-span predicate (line 29). MMM timespans follow the CIDOC-CRM model for modeling the range of a timespan. For this query, we’ve elected to use the “beginning of the begin” of the timespan data (the terminus post quem) as a manuscript’s production date (line 30). Line 31 uses the BIND and YEAR functions to extract the year information out of the production timespan and assign it as the ?production_year variable. This concludes the subquery. As a last step, we group our results according to the ?manuscript variable on line 33 to ensure that our results table displays information for only one manuscript per row.

5.4.1.2 Step 2 (Figure 12): Revised query to filter results for manuscripts produced after 700: https://api.triplydb.com/s/-9C8qoZtb

Figure 12

SPARQL query for Query 4: Step 2 (lines 21–37).

§58 This query is nearly identical to the previous query, except that it includes three extra FILTER functions to refine the results further. On lines 30–31, two FILTER functions state that all height and width measurements included in the calculations must be greater than 39 millimeters and less than 500 millimeters. Filtering the results in this way helps ensure that our results do not include typos or other data entry mistakes that sometimes appear in the measurement data. Line 37 filters the production year results to include only manuscripts produced on or after 700 CE. The choice to filter by this production year stems from a cosmetic need to produce a chart of the results that is easier to read. Since few manuscripts in the MMM dataset were produced before 700 CE, removing those manuscripts from the results creates a more efficient x-axis and greater legibility of the individual data points in the chart.

5.4.2 Alternative Query 4 (Figures 13a, 13b, 13c): Comparing ratios of different liturgical books: breviaries and missals: https://api.triplydb.com/s/qrzY6bd0e

Figure 13a

SPARQL query for Alternative Query 4 (lines 10–14).

Figure 13b

SPARQL query for Alternative Query 4 (lines 15–40).

Figure 13c

SPARQL query for Alternative Query 4 (lines 40–65).

§59 This alternative query copies the basic structure of the previous query to produce results that compare the average ratios of missals to the average ratios of breviaries. The SELECT statement includes two different sets of ratios, one for missals (line 12) and one for breviaries (line 13).

§60 To calculate these two different ratios, we use the same subquery strategy as employed previously, but a UNION clause (line 40) allows the results to be displayed together. The first subquery (beginning on line 17) calculates the data for missals, using the FILTER function to isolate those manuscripts that have the characters “missal” in their work label (line 29).

§61 This exact structure is copied in the second subquery (beginning on line 41), except in this case the FILTER function finds works containing the characters “breviar” (line 53). To distinguish the two results, the averages related to breviaries are called ?b_height_mm_average and ?b_width_mm_average.

5.4.3 Results

§62 Step 1 of the original query visualizes the height-to-width ratios for 4,513 liturgical manuscripts (Figure 14). It includes missals, graduals, breviaries, and antiphonaries, but the ratios are not distinguished by type of manuscript. There are no limits on the date of production, or on the size of the ratios. Because there are four outlying ratios between 8.636 and 30.831, as well as a small number of early production dates, the other results are heavily compressed, and the details of the other ratios cannot easily be seen.

Figure 14

Height-to-width ratios of liturgical manuscripts.

§63 Step 2 of the original query visualizes the height-to-width ratio of 4,030 liturgical manuscripts by limiting the production period to 700 to 1800 CE (Figure 15). The variation in ratios is also limited by the exclusion of manuscripts larger than 500mm or smaller than 39mm. It includes missals, graduals, breviaries, and antiphonaries, but the ratios are not distinguished by type of manuscript. The results are clustered around 1.25 to 1.6; most manuscripts were produced in the 14th or 15th centuries. The clusters of results for the years 900, 1000, 1100, 1200, 1300, and 1400 reflect the use of start dates for estimated production year ranges. Another version of this query makes use of end dates as well, to smooth out this kind of clustering: https://api.triplydb.com/s/uG86O-AIC.

Figure 15

Height-to-width ratios of liturgical manuscripts produced between 700-1800 AD.

§64 The alternative query compares the height-to-width ratio of two different types of liturgical manuscripts produced during the period 700 to 1700 CE (Figure 16). The total number of manuscripts involved is 12,169. Missals are shown as blue dots and breviaries appear in red. Most of the manuscripts fall within the range 1.0 to 2.0, though the majority fall between 1.25 and 1.6. There is considerable similarity between the two different types. Relatively few manuscripts have ratios less than 1.0 (i.e., with their width greater than their height).

Figure 16

Height-to-width ratios of breviaries and missals (outliers removed).

5.4.4 Lessons learned

§65 Neither MMM nor the source datasets provide information about the categories or subjects of works, so liturgical manuscripts had to be identified by keyword searches on uniform titles. Fortunately, these are generally common to Latin, English, and French, such as missal/missale, antiphonal/antiphonarium, breviary/breviarium, and so on. The initial query produced a single set of ratios regardless of the specific type of liturgical manuscript; later refinement visualized the ratios for the specific types separately, enabling comparisons between them.

§66 Dimensions are likely to have multiple values in the SDBM, reflecting different descriptions from different observations of the same manuscript. The same kind of variation can also be found for the same manuscript in two or three of the data sources. We dealt with this by averaging the height and the width across the different values.

§67 Some problems were identified with the source data, including records that had height but not width, and some cases where mm and cm measurements were mixed together. These could produce incorrect ratios, since the query works by adding up the raw figures and then dividing by the number of values.

§68 Production date ranges are often approximate, for example, “1300–1400” or “1225–1250.” We dealt with this initially by taking the earliest date in the date range, that is, “1300” and “1225” in these two cases. Further refinement of this query involved calculating an average for production date ranges (e.g., 1400–1450 as 1425), to avoid results bunching together at 1400 for 15^th-century manuscripts.

§69 Several outliers were noticeable in Figure 16, including one with a ratio of 30 (not shown). These were checked to see if they reflected an error in the source data, but the extreme outlier was found to be a roll rather than a codex, an unexpected result that could challenge assumptions about the use and readership of liturgical manuscripts in the Middle Ages. Our choice to remove outliers from the results meant that a more granular display of results in Yasgui became possible, but at the expense of a fuller and more accurate representation of variations in the data as the roll breviary indicates. Further, excluding outlying values for height and width actually affected the ratio calculations for some manuscripts and produced incorrect values. Excluding outlying ratios might be a better way of achieving this goal.

§70 As originally formulated, the query obscured whether height or width was the larger dimension, since the ratio was constructed by dividing the larger dimension by the smaller one, regardless of which was the height or width. The resulting ratios were always 1.0 or greater. A different formulation of the query was required to show the ratio of height to width consistently; the results then included ratios lower than 1.0, in cases where a codex was wider than it was long. Choosing between these queries depends on the ultimate goal of the research: is it simply to find the average relative proportions of a manuscript, or is it examining the orientation and layout of the pages as well?

§71 The resulting scatter plot showing ratios for 12,169 individual manuscripts, coloured according to their type, provided a very effective visual representation of a relatively large body of data. But these queries also made clear the importance of consistent approaches to recording this kind of data and documenting the assumptions made in analyses of the data.

5.5.1 Query explanation

5.5.1.1 Step 1 (Figure 17): Manuscripts sold by Tregaskis, their dates of transfer, their transfer counts, and the number of days they stayed in Tregaskis’ stock: https://api.triplydb.com/s/euJRw2LfK

Figure 17

SPARQL query for Query 5: Step 1.

§75 This query includes several calculations in its SELECT statement to determine the amount of time manuscripts remained in Tregaskis’ stock. The MIN aggregate function extracts the earliest date in a manuscript’s transfer timespan (MIN(?timespan_datetime) AS ?earliest_date). An identical strategy calculates the last date in the same timespan with the MAX function (MAX(?timespan_datetime) AS ?last_date). With these two new variables, ?earliest_date and ?last_date, the DAY function can calculate the duration of time a manuscript remained in Tregaskis’ possession (DAY(?last_date - ?earliest_date) AS ?duration). The COUNT function calculates the number of times each manuscript appeared in a Tregaskis catalogue as the ?transfer_count variable.

5.5.1.2 Step 2 (Figure 18): Duration and transfer count of Quaritch stock https://api.triplydb.com/s/0BBppvlWj

§76 Step 2 mirrors the process used in Step 1 to find transfers associated with Bernard Quaritch Ltd., but reduces the amount of information displayed so that a scatter-plot visualization becomes possible. The SELECT statement is reduced to two calculated variables: duration and transfer count (line 9). The MAX and MIN calculations are included within the DAY function to calculate duration. The URI for Quaritch is swapped for Tregaskis in lines 12–13, and the transfer count is limited to those manuscripts with 2 or more transfers (line 25).

Figure 18

SPARQL query for Query 5: Step 2.

5.5.1.3 Step 3 (Figures 19a, 19b): Tregaskis and Quaritch duration and transfers compared https://api.triplydb.com/s/RY-FOOqM4

Figure 19a

SPARQL query for Query 5: Step 3 (lines 9–25, relating to Tregaskis).

Figure 19b

SPARQL query for Query 5: Step 3 (lines 24–43, relating to Quaritch).

§77 Step 3 of the query brings together the results of the previous two queries for easier comparison. This involves creating two similar sub-queries—one for Tregaskis (lines 11 to 27) and one for Quaritch (lines 29 to 43), combining them with a UNION command (line 28), and displaying the duration and the transfer counts from the two sub-queries in an overarching SELECT statement (line 9). The transfer count in each sub-query is limited to those manuscripts with 2 or more transfers (lines 26 and 42).

5.5.1.4 Step 4 (Figures 20a, 20b): Comparison of Tregaskis and Quaritch stock between 1901–1920 https://api.triplydb.com/s/syyzeyQ_q

Figure 20a

SPARQL query for Query 5: Step 4 (lines 10–29, relating to Tregaskis).

Figure 20b

SPARQL query for Query 5: Step 4 (lines 28–47, relating to Quaritch).

§78 Step 4 is designed to limit the comparison between Tregaskis and Quaritch to a period when they were both active: between 1901 and 1920. This is done by adding a statement to each sub-query (at lines 25 and 43) to filter the timespan for values after 31 December 1900 and before 1st January 1921: FILTER (?timespan_datetime > “1900-12-31”^^xsd:date && ?timespan_datetime < “1921-01-01”^^xsd:date)

5.5.1.5 Step 5 (Figure 21): An improved scatter-plot visualization https://api.triplydb.com/s/qyGoY07li

Figure 21

SPARQL query for Query 5: Step 5.

§79 Step 5 of this query is designed to address a significant limitation in the scatter-plot visualizations: one coloured dot could hide several manuscripts with the same duration and number of transfers (e.g., two transfers and zero days duration). We wanted to use a bubble chart to show the relative frequency of each combination of duration and transfers. This involved re-working the query to match the pattern of variables required for a bubble chart: (1) Text – the label for each bubble; (2) Numeric – X axis; (3) Numeric – Y axis; (4) Text – determines the colour of bubbles; (5) Numeric – determines the relative size of bubbles.

§80 The query uses two sub-queries to find transfers associated with Tregaskis or Quaritch, and binds the relevant name as the seller (lines 14 to 19; 21 to 26). The sub-queries are joined with a UNION command (line 20). We then find the manuscripts involved in these transfers and the dates of the transfers (lines 28 to 33). The results are limited to those with a transfer count greater than one, and a duration greater than zero days (line 36). The calculation of transfer counts and durations is done in a SELECT statement at line 12.

§81 To construct the pattern of variables required for the bubble chart, an outer SELECT statement is added (line 9). This also counts the number of manuscripts with the same combination of duration and number of transfer counts. The manuscript names, although required for the bubble chart, have been replaced with a blank space enclosed between quotation marks, since their inclusion would make the chart unreadable.

5.5.2 Results

§82 Step 1 of the query produces 87 results, with transfer counts ranging from 2 to 8, and durations ranging from zero to 3,927 days. The transfer dates range from 1900 to 1935.

§83 Step 2 of the query produces 750 results, with transfer counts ranging from 2 to 12, and durations ranging from zero to 36,159 days (99 years). The results can now be visualized as a scatter plot (Figure 22).

Figure 22

Multiple catalogue listings by Bernard Quaritch Ltd.

§84 Step 3 of the query produces 837 results, with transfer counts ranging from 2 to 12, and durations ranging from zero to 36,159 days (99 years). This is a simple addition of the separate Tregaskis and Quaritch results, which can now be distinguished and compared on the same visualization, with Tregaskis manuscripts shown in blue and Quaritch in red (Figure 23).

Figure 23

Tregaskis and Quaritch transfer counts and durations compared.

§85 The durations between first and last listings are much greater for Quaritch, as are the number of listings, but does this reflect anything more than the much longer time period over which this firm has operated? In some cases, Quaritch had bought back (and re-sold) a manuscript originally sold by the firm some decades earlier, so the manuscript was not actually kept in stock for the whole period in question.

§86 Step 4 of the query produced 203 results for manuscript transfers between 1901 and 1920. The maximum duration was 6,605 days (18 years), and the maximum number of transfers during these 20 years was eight. This visualization makes it clear that Tregaskis was likely to list the same manuscript many more times than Quaritch during this period, and usually within a significantly shorter period of time (Figure 24).

Figure 24

Comparison between Tregaskis and Quaritch 1901–1920.

§87 Step 5 of this query produces a bubble chart in which each bubble shows the duration, the number of transfer events, the seller (Tregaskis in red, Quaritch in blue), and the number of manuscripts with that combination of variables (in the size of the bubble). The most common combination is visible in the largest blue bubble in the lower left of the chart: a duration of 792 days and a transfer count of 2, with Quaritch as the seller. A total of 30 manuscripts have this combination. The configuration of the bubble chart has been used to limit the maximum duration shown to 5,000 days, for the sake of visibility (Figure 25).

Figure 25

Bubble chart comparing Quaritch and Tregaskis.

5.5.3 Lessons learned

§88 SPARQL can be used with the MMM data to find and compare patterns in the stock retention and catalogue listings of manuscripts by dealers like Tregaskis and Quaritch over multiple years. Visualizations in the form of scatter plots and bubble charts are a valuable way of displaying this information; in the case of bubble charts, four different variables can be combined in the same chart. This cannot be done with the Sampo-UI interface to MMM, nor in the interfaces of the three source datasets.

§89 Nevertheless, these visualizations—and the underlying SPARQL results—need to be treated with some caution, since they conceal various assumptions about the data. There is no way of distinguishing manuscripts that were sold, bought back, and sold again by Quaritch from those that were kept in stock for a number of consecutive years and advertised in multiple catalogues during that period. The duration in stock is simply calculated from the earliest listing to the last recorded listing. Manuscripts with a duration of zero days between two listings may have been advertised twice in the same year, without a specific day or month being recorded, but these entries may also reflect two versions of the same catalogue or stock list entered separately in the Schoenberg Database.

5.6.1 Query explanation

§91 Query 6 (Figure 26): https://api.triplydb.com/s/44t3wQfOg. This federated query combines information from the MMM and Wikidata datasets with a single SPARQL query sent to the MMM endpoint. Line 15 limits the geographical scope of this query to England. We then find the actors associated with events occurring in England (lines 17–18), together with their death events and names (lines 20–21). The dates of these death events are then filtered for those occurring before 1900 and after 1800 (lines 23 to 26). Actors are then limited to those with VIAF identifiers (lines 28–29). Line 28 equivocates the ?actor and ?identifier variables by using the owl:sameAs predicate, which is how the query will connect the VIAF data between the MMM and Wikidata datasets. These VIAF identifiers are then passed to Wikidata using the SERVICE keyword. To find the corresponding “person” record in Wikidata, lines 31–32 return Wikidata resources that have VIAF identifiers that match the identifiers returned for the MMM actors above, along with their occupations. The FILTER on line 35 returns the occupation labels in English, rather than any language that may appear in the Wikidata data.

Figure 26

Query 6.

§92 The results show each actor’s MMM ID and name, together with their year of death, their VIAF identifier, and their occupation identifier and occupation (the latter two from Wikidata).

5.6.2 Results

§93 The query produces 205 results with 91 distinct names of people with death dates in the 19th century, who have an average of two occupations, though some have many more than this: 19 in the case of William Morris! There are a total of 83 different occupations. Politicians (21) and writers (21) are the most common, though there is also an astrologer, a brewer, and at least three slave holders. (Readers can find these results by clicking on the Yasgui link for Query 6.) We thus are presented with a cross section of occupations associated with those with the means and motivations to collect manuscripts in the 19th century.

5.6.3 Lessons learned

§94 As this query shows, Wikidata can be a valuable source of additional information about people and institutions in the MMM dataset that is not otherwise captured by the source datasets, in this case, the occupations of individual collectors. The results show that the personal, professional, and academic interests of collectors of premodern European manuscripts in the 19th century are diverse and sometimes surprising, including “singer-songwriter” or “science fiction writer.” The results may also show a certain bias. For example, why are there so many occupations associated with William Morris compared to other collectors? Is it because he was that much more active than anyone else, or that as a seminal figure in the Arts and Crafts movement, we have simply collected more data about him than other 19th-century manuscript collectors?

§95 The question of bias cannot be ignored as it has implications for how we collect data and, in this case, data about people. Well-known people or institutions will have more data about their lives associated with them in online resources. But it is also interesting to note that women are not included in these results, though we know that there were women involved in the book trade in Britain with death dates before 1900. (For example, Henrietta Katherine Burrell, recorded in the SDBM Name Authority: https://sdbm.library.upenn.edu/names/40365/.) Why is this so? The simple answer is that the overlap of persons with the same VIAF identifiers in both Wikidata and MMM is small. Indeed, while there are 56,685 actors (persons and organizations) in MMM, only a fraction have VIAF identifiers. At present, MMM has more than 15,300 VIAF identifiers for actors, but only 4,400 Wikidata identifiers.

§96 This lack of representation in VIAF could be due in large part to a systemic lack of recognition for the contributions that women have made in the book trade in the 19th century and in society in general. Following these results, we performed a similar query that asked to return actors with the same VIAF number but were identified in Wikidata as female. The best set of results was found among women collectors in the United States born between 1900 and 1950: https://api.triplydb.com/s/OZlC0ieHo, which returns 14 results showing 9 different women, with occupations ranging from librarian, book collector, and archaeologist to politician, statistician, and lawyer, among others.

§97 As these results show, this query strategy requires both the MMM person and the Wikidata person to have a shared VIAF identifier to return results. Our results point to the broader problem of the lack of representation of a large number of actors in available authorities and LOD resources. A systematic import of Wikidata identifiers into MMM (or into the source datasets) would increase results, but the problem will not be fully addressed until actors in underrepresented social groups and minorities are given better data representation in these resources.

Recommending editors:

Mike Kestemont, University of Antwerp, Belgium

Recommending referees:

Tiziana Mancinelli, Ca’ Forscari Università Venezia, Italy

Roman Bleier, University of Graz, Austria

Section/copy/layout editors:

Morgan Pearce, The Journal Incubator, University of Lethbridge, Canada

Christa Avram, The Journal Incubator, University of Lethbridge, Canada