dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparing one part of a first name with all other parts, also seems acceptable. However, the degree of overlinking probably will be higher in other parts of the Netherlands as about half of the Zeeland population had only one first name. We may expect that the degree of overlinking grows exponentially when the entire population has multiple first names, especially higher social groups and the Roman Catholic part of the population which was very generous in giving multiple first names  (Bloothooft & Onland, 2016; Gerritzen, 1998). On the other hand, less precision can be acceptable when all types of certificates are linked to produce family reconstitutions that can be used to test the integrity of the whole family (see  Bloothooft, van Boheemen, & Schraagen, 2016; van Boheemen, 2016). We can conclude from the results in Table  6  that about 199,000 matches is the maximum that we may expect from this dataset.
We calculated a theoretical total of 339,000 matches. This implies that about 140,000 (42%) of the parents married outside Zeeland during the period from ca. 1810 till 1910. We may test this in future 1) by linking the certificates of Zeeland with certificates covering the rest of the Netherlands and 2) by adding matching algorithms working with root names. We expect more matches using roots for the first name, because they will take into account abbreviations, such as Jan instead of the "Johannes", and translations, such as "Guillaume" instead of "Willem"  (Oosten, 2008). We will also select cases to be examined manually to find software bugs and dataset errors, such as certificates that have been entered twice in the original dataset, and to determine if some double links are really couples who married each other for a second time.
Future versions of LINKS will keep using the number of matches and overlinks as proxies for recall and precision. The optimal settings will probably differ by region and time period, but the ratio will help determine the optimal settings for each context. In the foregoing, we explained how LINKS operates in cleaning and matching the civil certificates from the WieWasWie indices. However, matched certificates are only the basis for a research dataset. As this is being written, 40 datasets have been constructed, of which five can be downloaded directly (https://datasets. iisg. amsterdam/dataverse/hsndb-links). For a full overview of all releases, see https://iisg. amsterdam/en/hsn/projects/links/links-releases. Most of these releases covered only parts of the country, dependent on the availability of indices, the transcription of occupational titles and specific requests from researchers.
Over half of these releases were 'forerunners', to be used by researchers for testing the quality of the data or developing the program to construct the dataset for analysis or doing preliminary analyses. Until 2010 only the marriage certificates were indexed completely enough to link them and to make meaningful releases (of pedigrees). It took more time to index the other certificates, and the birth certificates were lagging behind. The first more or less completely indexed provinces were Zeeland, Limburg and Groningen/Drenthe. After 2010 the index improved enormously in quantity (see Table  1 ) and it became possible to link the country as a whole.
INTRODUCTION which resulted in two large releases at the national level: all marriage certificates linked into pedigrees  (Mandemakers & Laan, 2020b ) and all birth certificates linked with the marriage certificates of the parents  (Mandemakers & Laan, 2020a). Ultimately, the main goal of LINKS is to deliver complete integrated datasets of births, marriages and deaths, creating families and multigenerational links. So far, this kind of dataset is only realized for the provinces of Zeeland, Limburg and Groningen/Drenthe separately  (Mandemakers & Laan, 2017 , 2018 , 2019). Researchers have used these datasets to create two types of datasets suitable for statistical analysis. The first one was a rectangular data structure constructed within the context of the project Genes, Germs and Resources  (Mourits et al.
MARRIAGE CERTIFICATES Figure 7 Relative number of linked certificates per marriage line,  Netherlands, 1812 Netherlands, -1941  Source: LINKS dataset linked marriages  (Mandemakers & Laan, 2020b). Table  7  presents an overview of the matching results. In total, 59. 8% of the brides and grooms were matched with a parental certificate. Parental marriages before 1812, mostly explain why 28. 1% of the certificates were not matched. The remaining 12. 0% did not link for other reasons, such as lacking indices, ambiguous matching results or foreign marriages. Brides or grooms linking with more than one parental certificate are marked in the dataset and not linked. An exception are cases with two alternatives of which one match was almost exact and the other one had a relatively high score on Levenshtein.
On this basis a meagre 0. 1% could be added to the linked results. These decisions are marked in the release tables, so a user may decide not to use these ambiguous links. Source: LINKS dataset linked marriages  (Mandemakers & Laan, 2020b). Source: LINKS dataset linked marriages  (Mandemakers & Laan, 2020b). The sum of Levenshtein distances is also made explicit for each matched certificate (see Table  8 ). Of all matches 79. 4% proved to be an exact match and only 0. 5% were matched with a total Levenshtein value of three and more. This clearly indicates the good quality of the Dutch certificates and the matching operation. That 2. 6% of the certificates with more than one match remained to unresolved (as shown in Table  7 ) is because most of these matches are of very good quality in terms of Levenshtein distance.
BIRTH TO PARENTAL MARRIAGE LINKAGE Figure 8 Relative number of births linked with parental marriages,  Source: LINKS dataset linked births and parental marriages  (Mandemakers & Laan, 2020a). Figure  8  shows the share of matched birth certificates per year. After 1850 one could expect that each birth with two parents will match with a marriage certificate. This is not always the case for reasons already mentioned. Around 1850 the percentage is about 75%, climbing to 92% for the period 1898-1912, dropping in the years of the First World War to 87%. Looking at the Levenshtein distances, we found more or less the same results as presented in Table  8  for the linking of the marriage certificates. Of all matches, 79. 6% proved to be an exact match and only 0. 4% matched on the basis of a total Levenshtein value of three and more.
Secondly, we will elaborate on the linking process, going into the several types of matching that needed to be made. Thirdly, we will explain how the persons in all the certificates were synchronized into unique persons. In the last section we will elaborate on the outcome in terms of unique persons and families. The resulting dataset consists of a system of four interlinked tables. See Figure  9  for the table structure and the relationships between the tables and the identifying keys. 10 10 For the sake of clarity, the names of some keys have been changed in comparison with the published documentation (Links_key_reg_mar in BIRTH_DEATH became Id_mar_parents and Id_mar_parents in MARRIAGE_LINES became Id_mar_parents_intern).
STRUCTURE OF THE DATASET For a complete construction of unique persons, we need five types of links: a) Marriages and parental marriages (pedigrees) The linking process contained only one step: The marriage certificates were linked on the basis of a link between pairs: the parents of a bride or a groom with a bride/groom couple, see Section 6. 2. 1 for the details of this matching. Links could not be established in case the parental marriage originates from the period before 1812.
IDENTIFICATION PROCESS OF UNIQUE PERSONS All these matching operations created multiple links (so-called overlinks). Certificates with multiple links were flagged and not linked, unless the composed Levenshtein values showed significant differences (e. g. , 1 compared with 7 or 8). In that case a choice was made for the match with the lowest Levenshtein value and flagged as such. All persons from the civil certificates entered the dataset with their own identifiers which are always kept in the release as well. However, it may occur that the linkage information tells us for example that the person number 1203048 in the birth certificates is the same person as the person with number 42382209 in the marriage certificates. Then, we need to synchronize the identification numbers and create a kind of global identifier, Id, which is for each release.
Synchronizing persons from the marriage certificates To make the synchronization process feasible two extra steps are necessary: a) the pedigree system has to be transformed into a family tree system, and b) remarried children need to be identified. The conversion from a pedigree system into a family tree system was done by way of the following steps: 1 Define the level of the family tree as generation "1" if there is no link with a parental marriage; 2 Define the family tree as generation "2" if there is a link with a previous marriage with generation level "1"; 3 Repeat step 2 up to generation level 7 or more, which is the limit and occurs only four times in the Zeeland dataset. This procedure looks more straightforward than it is. One needs to realize that although a bride or a groom has only one parental couple, they have two grandparental couples, four great-grandparental couples etc.
Another issue is that remarried children need to be identified. There are brides and grooms who marry more than once and initially have different identification numbers. If they are not identified as one and the same person they will be seen as siblings in the dataset. At generation level 2 and higher remarried persons are identified within the context of the parental marriage, in which they can be matched through their first name. In case of equal first names, the identification number is synchronized. At generation level 1 we created 'shadow marriages' (see Section 6. 3. 2, but now on the basis of marriage certificates) on which basis we could match on first names and synchronize the identifiers of remarried persons. In a final step the identifiers of the bride and groom of generation 1 replace the identifiers of the parents of generation 2, etc.
SOME REMARKS ABOUT THE RESULTS In the releases, several fields are included that describe the way the data have been matched. These flags may be used to make selections from the dataset to test how robust the outcomes of the statistical analyses are. Ultimately, it is the researcher who is responsible for the way the data are used. Since the marriage certificates are linked with both birth and death certificates, it is possible to check on triangle problems. It turns out that many death certificates are linked with marriage certificates and not with birth certificates, where these birth certificates are linked with the same marriage certificate. In the case of death certificates of persons born before 1850, in which names of parents are often of poor quality if mentioned at all, they could easily be linked to their own marriage certificates on the basis of the names of partners.
Reshaping the pedigrees into family trees, is a kind of toppling of the pedigree system. It is an essential step because most of the generational analysis should be done from the perspective of the beginning of a family line (not of the end), especially when the system is to be extended with other certificates (e. g. , of the children of the couples). There is also a practical problem: because each line in the pedigree will result in a different generation level for the last generation, one cannot simply fix the generation level for one marriage line in a marriage certificate. The more generations are involved in such a system the more complicated this will become. The integrated datasets of births, marriages and deaths with created families and multigenerational links are not sufficient to be immediately usable for research.
For the Zeeland release two types of datasets suitable for statistical analysis were created. The first one was a rectangular-type structure that was constructed within the context of the project Genes, Germs and Resources (https://www. nwo. nl/ en/projects/360-53-180-0;  Mourits et al. , 2020). The second was a conversion of the format of the Zeeland release into the format of the Intermediate Data Structure  (Alter & Mandemakers, 2014). The database which was constructed for the project Genes, Germs and Resources (LINKS-gen; see  Mourits et al. , 2020) , served several goals. The first one was to create more explicit family links than were provided in the Zeeland release and to improve and extend dates of birth, last observation and other variables. The second one was to reformat the design into a so-called pedigree format.
Through better integration of unlinked newborn and deceased persons that were linked to the same parents, a more consistent dataset could be created. Also, several data improvements were applied. For example, the conversion of ages at a specific moment into birth ranges, fields were created for up to five marriages and newborns who were reported dead on registration lacked a date of birth which was included as the date of death. Other improvements made twins explicit, added dates of last observation and flagged complete cases. To retain all data and relational information of a person on one record the database was restructured into a so-called pedigree structure. This format structures the data in such a way that each record includes the identification number of a person, the identification numbers of his or her parents (if known), the sex and all other variables.
Families and familial relationships are defined through the father and mother. Through restructuring the dataset, hidden links between persons were also made explicit. By this operation a new structure was created, making it also much easier for researchers to select their case for a specific analysis  (Mourits et al. , 2020). The conversion of the Zeeland release into the IDS-format was relatively easy, mainly because the number of variables, or types in IDS-grammar, is quite limited  (Mandemakers & Laan, 2017, IDS version). In the INDIVIDUAL table we have, sex, occupations, and the date and location of birth, marriage and death. Relations that are established in the INDIV_INDIV table are those between children and parents (including in-law relationships) and marriage couples. The nature and location of the certificates were used as the lowest level in the contextual system.
An alternative could have been the use of the "Union" concept as lowest level as Klancher Merchant and Alter (2017) have done, but this approach was not necessary given the nature of the research for which the IDS dataset was developed. It concerned research into intergenerational effects of infant mortality in which four other databases were involved. All were structured into the IDS and reshaped in datasets ready for statistical analysis by a common Stata script  (Quaranta, 2018; van Dijk & Mandemakers, 2018).
DATASETS FOR ANALYSIS In this paper we explained the construction of the LINKS database using the indices of the civil certificates as collected by the Dutch Family Center and published on the website WieWasWie. Presently, over 40 million certificates and 120 million appearances of persons have been included in this index by hundreds of volunteers working over the last twenty years to create an electronic index of the civil certificates as soon as they become public. Two matching systems have been developed within the HSNDB environment. The first one is a query system based on SQL queries selecting the data from the MySQL-database in which the matching queries are only part of a wider environment directed at the standardizing, cleaning, enriching and outputting of the LINKS data. Using the Zeeland marriage certificates as an example, we showed the excellent quality of the data material in general.
The difference between the result of the exact matching and the least restricted, yet acceptable alternative in matching was only 38,338 matches (23. 5%). Moreover, 80% of all matches were exact matches, thanks to the legal structure of civil registration that standardized the names and a legal and administrative structure that kept the birth name of the females alive with and after marriage. However, on large datasets these matching queries are slow and the end user has no direct influence on the matching alternatives, which are set by the database manager unless special requests are made. For these reasons a second matching system, burgerLinker, was developed, based on knowledge graphs which can be run independently of the LINKS environment. In both cases, matching is a first step for the creation of a linked dataset that can be used for research.
We explained the construction of several relatively recent data releases. On a national scale, we released a pedigree system based on all marriage certificates and a dataset in which the births are linked with the marriages of their parents, forming families. On a regional level we created three separate releases for the provinces of Zeeland, Limburg and Groningen/Drenthe. Here, we combined birth, death and marriage certificates to create three-generation families. One of the issues for which we found a solution was the identification of unique persons in this three-generation system. The LINKS project started in 2010. Since then, over 40 releases have been produced resulting in over 50 publications including several dissertations  (Mandemakers & Kok, 2020). We expect that in the future, more releases will be made by HSNDB or by individual users of burgerLinker.
" Corresponding standard values are written to LINKS cleaned. If the content is not known in the reference table, it will be written in LINKS cleaned and a new record will be made in the reference table where it awaits standardization. After a new round of standardization, cleaning is run again on every field. If the value is considered invalid, it is included in the error table and reported to the originating archive (see Appendix A for these messages). Reference tables are part of the existing standardization schemes in the HSNDB domain. With each new release, all new values in the LINKS database are standardized and coded in a manual and/or semi-automatic way and added to the reference table after expert review. Table2gives an overview of the content of the main reference tables as of July 2021.
The first column gives the number of original values, the second column the number of standardized values, the third column the number waiting to be standardized, and the fourth column the values that were considered invalid. The fifth column presents the resulting unique values. These tables are kept in one system together with other databases that form the HSNDB environment. Additionally, there are smaller reference files for data such as suffixes, aliases, sources, roles and source types. Civil status quite often also implies an indication for the sex of a person (bride, widower etc. ), hence a combined table Status_sex was developed. The table Prefixes includes text that may precede a family name as part of the family name (for example "de Boer" instead of only "Boer", which is quite common in the Netherlands), or as a title.
Statistical models Remi Monasson (Ecole Normale Supérieure, Paris, France) opened the meeting with the talk 'Data-driven model for neoantigen/T-cell presentation and recognition: the case of pancreatic cancer'. Remi discussed how Restricted Boltzmann Machines (RBMs), one of the simplest generative models for unsupervised learning, are preferred to deeper architectures in many biological applications, where datasets are typically small. Besides being interpretable, RBMs do not enforce any specific distribution on the latent variables, unlike other commonly used generative models such as variational autoencoders. Remi discussed how RBMs are helpful to model antigen presentation by the major histocompatibility complex (MHC) and to predict immunogenicity, i. e. the capacity of the presented antigens to elicit T-cell responses.
The challenge of predicting immune receptor cross-reactivity, i. e. the ability of antibodies and TCRs to bind multiple targets, was also discussed. Predicting cross-reactivity is difficult as sequence similarity does not guarantee similar binding profiles. To identify the motif vocabulary of paratope-epitope interactions that govern antibody specificity, Victors's team analysed paratope-epitope binding patterns of ~1000 Ab-Ag structures publicly available, finding that paratope and epitope motifs have high specificity and low cross-reactivity. This work suggested that although the number of antibody-binding motifs is relatively restricted, the sequence and structural rules that govern the antibody-antigen interaction can be learned  [4]. Despite the identification of sequence motifs, there are still too few datasets where antibody sequence is linked to the target antigen, which renders difficult the development of ML models for this task.
Without careful consideration and understanding of these algorithms and their effect on the data, heuristic transformations typically lead to distortions and artefact generation, especially in high-dimensional datasets such as single-cell transcriptomics. Topological data analysis is a new branch of data science focusing on exploiting topological properties preserved under continuous space deformations, such as the number of connected components, loops, or holes. These topological features can be quantified in homology groups, which count and classify the number of such topological features. Persistent homology (pH) is a technique from computational topology that quantifies the persistence of topological characteristics such as loops and cavities across various scales in a dataset. pH can distinguish between significant structures and noise by varying the observation scale and tracking how these features evolve.
Davide presented ongoing work at BSC on the application of topological techniques for single-cell analyses using the Mapper algorithm  [7] , a well-known tool for topological data analyses that enables the visualization and understanding of high-dimensional datasets in a way that would be challenging with more standard techniques. He illustrated the application of the method by focusing on the analysis of a scRNA-Seq dataset of the bronchoalveolar immune microenvironment in COVID-19. The dataset included samples from individuals with mild and severe cases of COVID-19, as well as controls. Mapper identified meta-clusters of cells that were enriched on the three clinical phenotypes. This work demonstrates that topological data analysis with Mapper provides a robust framework for visualization and downstream analysis of single-cell datasets.
Machine learning methods Pieter Meysman (University of Antwerp, Belgium) followed with the talk 'Tackling the complexity of (unseen) epitope-TCR predictions'. Pieter presented TCRex, a method to link TCR sequences from high-throughput TCR sequencing experiments to their specific epitopes  [8]. The approach uses a random forest trained on physicochemical properties and identifies common TCR patterns associated with recognizing specific epitopes. Additional analyses can be performed to evaluate the prevalence of specific epitope-reactive TCR sequences in a dataset. TCRex was applied to three datasets related to vaccine monitoring and infectious disease diagnostics and could detect differences in the presence of epitope-specific TCRs in pre-and post-vaccination repertoires. TCRex is freely available as a user-friendly web tool.