dataesr/scientific-paragraphs-categorization huggingface dataset:
Differently from  Moska et al. (2019)  no subtraction of an experimentally approximated residual background dose was executed (cf. Murray et al. , 2013). The sequences are summarized in Table  1. The program Analyst version 4. 31. 9  (Duller, 2015)  was used for processing and evaluation of OSL and IRSL data. Regenerated growth curves were fitted to a single exponential saturation function. Plots of OSL decay curves and regenerated growth curves for three samples (fine quartz grains) from the Biały Kościół section  (BT 1672 , BT 1673 , and BT 1675)  are shown in Fig. S4  in the Supplement, and a plot with preheat plateau test and recuperation for BT 1675 is shown in Fig. S5.
This cannot be attributed to changes in dose rates over burial time as these would have affected all dated fractions in the same direction. The age-depth plot (Fig. 4  right) visualizes that in the lower part (below L1LL2, samples BK1 to BK5) coarse-grain quartz ages systematically underestimate middle-grain quartz ages, which themselves underestimate the pIRIR 290 ages. These results argue for a longer-lasting hiatus within or at the base of the L1SS1 palaeosol (see above) and a very long hiatus within the S1 pedocomplex. The pIRIR 290 age of BK2 (365 ± 30 ka) agrees with the pIRIR 290 age of BT 1668 (343 ± 18 ka, with a = 0. 085, which is similar to the value used for BK2). Despite some differences between the ages obtained in Gliwice and in Bayreuth, which will be discussed later, the comparison of the dataset from both laboratories evidences amazing accordance.
Since the overall successful adaption of the quartz OSL-SAR protocol to dating of loess from the last glacial cycle, it has been favoured over IRSL or pIRSL because of higher sensitivity to daylight and the absence of anomalous fading. The results presented and discussed here, however, cast some doubts on the reliability of the quartz OSL ages unless the mentioned problems are better understood and apparent age underestimates due to reasons other than anomalous fading can be ruled out. Uncorrected OSL sensitivity change and different growth curve shapes for natural and laboratory-regenerated signal growth with doses are debated (e. g. Schmidt et al. , 2021). Even if the present database is not yet sufficient, it is very likely that the quartz problems arise from the origin and geological history of quartz grains and mainly affect loess samples older than L1SS1.
But attribution of luminescence ages to certain MISs is not yet always unambiguous. An analogue control by the Klintholm ice advance (30-34 ka) may be discussed for the loess of the upper L1SS1 pedocomplex at the Złota section (ZL-3, ca. 32 ka). The actual database is, however, too sparse to prove these hypotheses, and the discussed problems of optical dating need more clearness. In spite of the addressed uncertainties, we can so far for honesty reasons recommend aiming at age bracketing as narrowly as possible, using OSL from different quartz fractions and pIRIR from fine polymineral grains. In the case of the L1SS1 pedocomplex, for example, age bracketing between ca. 30 and 40 ka demonstrates the helpfulness of this approach. Exact dating remains challenging, in particular for a periglacial environment with strong heterogeneity of the dated material (Fig.
Sources of data and methodology The analysis is based on data extracted from a set of semi-structured surveys on 138 farms, conducted between 2012 and 2020 as part of different research projects (H2020 Agrilink, ANR Agrifirme and, Germea Chair). Purposive samples of farmers were interviewed to capture the diversity of farms that show new and emerging patterns of work organisation. The questionnaires covered the history of the farm and that of its manager, its current organisation and functioning (including activities, governance, development strategy, labour organisation, economic and financial management, and the sources of advice used and those desired), its environment and its future prospects. Cross-cutting thematic analyses were carried out to highlight in particular the diversity and complexity of the sources of labour (examples are shown in Figure  1 ), as well as the difficulties experienced in managing human resources.
Box 1: Common Agricultural Policy, social conditionality and labour regulation The agreement that was signed on 2 December 2021 on the financing, management and monitoring of the Common Agricultural Policy stipulates that financial support from the CAP will have to comply with major EU regulations regarding workers, in particular: Directive 89/391/EEC of the Council of 12 June 1989 on the introduction of measures to encourage improvements in the safety and health of workers at work These surveys were complemented by in-depth interviews conducted for the H2020 Agrilink project with 15 advisory providers. These interviews were about the functioning of the organisation, the advisory services offered (type, content of back and front office practices), and its target groups. Cross-referencing the results of the advisory providers' interviews with those from the on-farm surveys allowed us to characterise the gaps in the current provision of advice on human resource management on farms.
Conversely, the paper aimed to demonstrate the contribution of occupational groups to the extent and the way in which wealth inequalities shaped societies both at the macro-and the micro-levels. To do so, we estimated the relationship between wealth and income across occupational groups using aggregate data for five major European countries. Thus, we inductively built occupational group clusters, drawing on the relative importance of wealth for income. Second, we showed how crosscountry comparisons helped our understanding. This move went hand in hand with a decomposition of wealth between a household's main residence and valuables, on the one hand, and more disposable assets, on the other. Further, we drew on these previous steps to elaborate on a comparative analysis of the relative weight of wealth vis-à-vis income across country, age, and occupational groups.
Using the HFCS dataset, they extended Piketty's measures on wealth inequalities to the 17 countries covered by the dataset. They showed that wealth created great discontinuities between capitalists and employees. Drawing on Wolff and Zacharias  ' [2013]  decomposition of capitalists into various segments, they showed that the self-employed detained a large share of wealth. We share with Rehm et al. the idea that neither wealth matters only to understanding the top nor the patrimonial middle class but the entire class structure. To dig into this promising research direction, we made a between-group comparison using a key indicator used by Piketty at the country-level, the wealth-to-income ratio (WIR). Initially developed by  Stiglitz [1969] , the WIR is relevant in three dimensions  [Savage and Li 2021] : it measures the growing social weight of wealth, it is suitable for cross-country comparison, and its third aspect relates to the distributional structure of income and wealth.
Data, Variables, and Main Indicators The household financial and consumption survey [wave II] We used the Household Financial Consumption Survey [2014 wave] of the European Central Banks network for this study. Its main advantage was to allow us to capture the distribution of wealth and its variations with other economic and social characteristics  [Humer et al. 2016] , thanks to nationally representative samples of European households. The HFCS collects information on assets, liabilities, income, and consumption of households, providing insights into their economic behavior and financial situation. The survey's second wave was based on 84,000 interviews conducted in 18 eurozone countries, as well as Poland and Hungary, mainly in 2013 and 2014. Surveys have been criticized as being less reliable than administrative data [Piketty 2020: 704-705], especially insofar as they are subject to sample selection problems.
6 Recoding of occupational groups by the ESeG is based upon the indications provided in Méron et al. [2016]. 7 Rubin's rule aims to take into account the uncertainty resulting from the imputation procedure by the integration of the betweenimputation variance of each relevant parameter. The MI parameter estimate is the average of the five datasets' estimates; its variance is a combination of the average of the five datasets' variance estimates and of the variance of the complete data estimates. 8 The results reported in this study were estimated by running 1,000 replicate weights. 9 We compared the results from HFCS to LFS-2011 thanks to works by the ESeG team These five countries represented four clusters of countries according to the welfare regimes typology of Esping-  Andersen [1990]. Spain is an example of a Mediterranean country  [Ferrera 1996 ] with a low level of decommodification and a high familiarization of welfare provision.
ALBERTINI Marco, Gabriele BALLARINO and Deborah DE LUCA, 2020. "Social Class, Work-Related Incomes and Socio-Economic Polarization in Europe, 2005-2014," European Sociological Review, 1-20. BALLESTRA Carlotta and Richard TONKIN, 2018. "Inequalities in household wealth across OECD countries. Evidence from the OECD wealth distribution database," OECD Statistics Working Papers, 01 (Paris, Éditions OCDE). BEDÜK Selçuk, 2018. "Understanding Material Deprivation for 25 EU Countries: Risk and Level Perspectives, and Distinctiveness of Zeros," European Sociological Review, 34 (2): 121-137. BERMAN Yonatan and Branko MILANOVIC, 2020, "Homoploutia: Top labor and capital incomes in the United States, 1950-2020," World Inequality Lab Working Paper, 27: 1-29. BESSIÈRE Céline and Sibylle GOLLAC, 2020, Le Genre du capital.
The commentary is a text part in itself. It was republished in 1503 and again in 1509  9. The first time, the commentary was published together with another text parta fragment of Thābit ibn Qurra's De imaginatione spere et circulorum. The second time, however, it was published with three additional text parts: the fragment, Tockler's introduction and finally his description on how to build an armillary sphere. 10  Our ontology allows us to connect Tockler's commentary not only with the two aforementioned editions of the same commentary but also with all editions that contain the commentary on the same reference text. Furthermore, we can connect all editions that contain one of the other text parts that were printed together with the commentary at hand. To organize and interpret the multitude of possibilities and the high dimensionality of the collected dataset, we created a multi-layer network that could show us, in graph form, all possible connections along the temporal axis.
1. of (Valleriani et al. 2019). 12 The studies in which we analyzed the dynamics of the dataset and which contain links to the data and the scripts to create the matrices are (Valleriani et al. 2019; Zamani et al. 2020). each other as they each contain at least one image, and the images all meet at one of the following criteria of similarity: identical illustrations; illustrations displaying the same kind of visual representations to transmit the same knowledge (similar but not identical); and illustrations that are clearly different from each other but transmit the same scientific content. Based on these three groups of illustrations, we want to analyze how the knowledge transmitted by visual elements in cosmology works evolved during the same time frame. We extracted 31,084 illustrations from the editions of our corpus by applying a taxonomy that is also related to the mechanisms of production.
A Unified Resource Locators (URL) is a special type of URI that not only identifies but also locates data and returns it in a useful format, such as a text document for a human user to read or a data file for a program to process. Having data points that are uniquely identifiable, citable and (ideally) accessible means it is possible to create a shared knowledge base around certain topics. Our database, built around the Tractatus de Sphaera by Sacrobosco, can connect to other sources on the topic or to individual actors that appear in our sources. Having data in an unambiguous machine-readable format also means we retain flexibility in how we process and analyze the data, as we can transform it into different formats without human intervention.
Linked Data in Practice We developed our database on a linked data middleware, namely, the Metaphacts Open Source Platform (Metaphacts 2019). All our data points have a unique URI and, after publication, these data points can be retrieved via standard linked data endpoints  20. Here, we discuss how we intend to extend our data by linking to external resources. Some external resources, specifically persons and places, were already linked when they were entered into our database. A user can fill in a form to select the corresponding record in Wikidata, a community-built knowledge base that currently contains structured data on over 85 million entries. Out of the 410 persons in our database, 168 can be found in Wikidata. Additionally, the 43 printing and publication locations  21  in our database all exist in Wikidata and are linked accordingly.
For persons and locations, a link is used to establish an equivalence between an entry in our database and the corresponding one in Wikidata. Just like entities, links are identified by URIs and several types of links exist that establish equivalences. The OWL Web Ontology Language defines the sameAs link (Dean and Schreiber 2004) as two identical entries. For historical data, this relationship is often too strong. The city of Venice that appears in our records, for instance, cannot be said to be equivalent to present-day Venice  22. For this reason, weaker links of equivalence, such as exactMatch or closeMatch, prove more helpful for our purposes. Both of these links are part of the SKOS Vocabulary (Miles and Bechhofer 2009).
Proven Advantages of Using Linked Data Wikidata allows us to harvest additional metadata about the entries in our database, some of which are displayed in the web interface. We include portraits of all individuals, when one can be found. While a portrait does not add any immediate scholarly value to our project goals, it contributes visual imagery to our otherwise text-heavy database and acts as an incentive for users to perform the extra step of linking entries to Wikidata records. We include coordinates for relevant places, with which it is then possible to create geographic visualizations of book production. We are also able to obtain hierarchical data about the places in our database, for example, if we want to organize our place data not by city, but by countries. Broadly speaking, linking entries to an external knowledge base, such as Wikidata, allows us to separate data that we derive from our own sources from canonical data that is already known and recorded.
Potential Advantages of Using Linked Data As we have outlined, we intend to extend our multi-layer network through social connections between the actors identified in our database. Instead of identifying and cataloguing such connections ourselves, we would like to build upon existing prosopographic databases, such as the JSN and the RAG. We expect to find at least a few of the same individuals in these two databases as in our own, due to the overlapping topics and timeframes. Both sources record connections between individuals, which we hope to reference in our own database to build up a social network of authors, publishers and printers. In order to link our resources with external databases, two requirements must be met. First, we need a way to automatically identify which persons in our database match those in external collections, a process that is referred to as consolidation.
Consolidation requires common identifiers in two separate collections. The name of a person is an identifier, although generally not a very reliable one. There may be several persons that go by the same name or the same name may have various spellings. Ideally, consolidation uses unique, nonambiguous, shared identifiers so that heuristics or human intervention is not required. Second, we need an interface or -more specifically -an application programming interface (API) to programmatically access the databases. Unfortunately, neither of the two databases offer a SPARQL interface through which we could directly harvest the data. Both, however, provide their own APIs that allow to access the databases' content through programmatic means. In the following section we will broadly summarize our method of linking individuals in our database with corresponding database records in the RAG and the JSN.
Linking to the JSN The Jesuit Science Network database (Mrozik 2016) is a custom web application built on the Symfony PHP framework (Mrozik and Feyerabend 2016) and is based on the infrastructure of the Person Data Repository (BBAW) developed by the Berlin-Brandenburg Academy of Sciences and Humanities. It contains biographical data on 998 Jesuit scholars and 44 non-Jesuit scholars. Figure  2  displays a database record for Christoph Clavius. Clavius' biography is displayed as a timeline and includes details of his education and career. Figure  3  visualizes his social relations as a network. In Figure  2 , directly below Clavius' name, date of birth and date of death are several pieces of information that are critical for linking the JSN and Sphaera databases: the person's Virtual International Authority File (VIAF) identifier (a nonambiguous identifier) and machine-readable versions of the database record in XML and JSON formats (the API).
Another crucial building blocka link to a BEACON file -is hidden in the footer at the bottom right corner of the page. A BEACON link dump (Schindler 2018) is a simple linked data format that describes equivalence links between records in the origin database and in an external collection, in this case the VIAF (VIAF 2010). VIAF aggregates records on persons, places, works and other entities from several national libraries and assigns each record its own identifier. The JSN BEACON file is a practical basis for linking and consolidating our records as it provides both a list of all the entries that the database contains and a common identifier. We currently do not store VIAF identifiers for the entries in our own database. The only external identifiers we add are Wikidata identifiers. However, the Wikidata records on persons contain links to other authority files, VIAF being one of them.
Using SPARQL's Federated Query mechanism (Prudhommeaux and Buil-Aranda 2013), we can retrieve this external data from within our own database. Through this process, we discovered that not all records of persons contain VIAF identifiers, so we decided to manually add them to Wikidata. This way, the identifiers will be available to future Wikidata users. We then repeated the search and retrieved the complete list of VIAF identifiers for our records of persons from Wikidata, including those that we added ourselves. Finally, we compared this list with the list of VIAF identifiers in JSN that is provided through the BEACON file. We next ran a script that performs the comparison, which returned: «Found 1 match» (Fig. 4 ). One match is better than none, although we had hoped for a few more. The identified match is VIAF ID 30337205, which corresponds to Portuguese mathematician Pedro Nuñes.
We were interested in harvesting Nuñes' social relationships recorded in the JSN. For Nuñes there was only one: his student Christoph Clavius. When the comparison script returned the discovery of a single match, we in fact expected it to be Christoph Clavius, as he features prominently in our database. Why then was this match not discovered? It turns out that the JSN and Wikidata entries have differing VIAF identifiers for Christoph Clavius. JSN identifies him as 306060128, whereas Wikidata records 46873973. It appears that Christoph Clavius exists twice in VIAF or at least existed twice when his VIAF identifier was recorded in JSN. The duplicate record has since been removed from VIAF but is still accessible for reasons of transparency. We need to manually account for this in order to retrieve data on both Nuñes and his student Clavius.
Linking to the RAG The RAG uses Nodegoat (van Bree and Kessels 2013) for its repository. Nodegoat is a database and visualization tool specifically designed for scholarly projects. It provides a data entry backend for administrators as well as a front end that makes the biographical data of over 64,000 scholars available to the public. One entry is Jacques Lefèvre d'Étaples  23  (Fig. 5 ), who also appears in our database. His record in the RAG lists 12 biographical entries that connect him to six other scholars. These social relationships can also be visualized as a graph (Fig. 6 ). Nodegoat provides an API, through which the database can be queried. As the API is not publicly available for security reasons, the RAG provided us with login credentials and permissions to retrieve the person records.
The Nodegoat API, however, is not very  well documented, thus we had some difficulties working with it. We nevertheless managed to connect to the API with the help of Nodegoat's developers, but ultimately decided on a different approach. Using the Metagrid project (SAHS 2019) from the Swiss Academy of Humanities and Social Sciences, the RAG published a BEACON file that links the records on persons in their database to corresponding entries in the GND, the reference data file of the German National Library. Using this file, we were able to approach it as we did with the JSN database, because the GND identifiers for the persons in our database are retrievable through Wikidata. Again, we added missing GND identifiers in Wikidata. We were able to identify 18 records on persons in the RAG who also appear in our database.
Lessons Learned and Future Policy By linking the data we gathered in the Sphaera project to datasets from the Jesuit Science Network and the Repertorium Academicum Germanicum, we were able to retrieve additional information on 20 of the 410 persons in our database (including the semiautomatically discovered Christoph Clavius). We found one social connection to enrich our network -the relationship between Christoph Clavius and Pedro Nuñes documented in the Jesuit Science Network. 24  This data harvest may seem quite negligible but considering that we only connected to two other repositories, we believe there is.
Das RAG ist zugleich ein Vorzeigeprojekt im Bereich der Digital Academic History geworden, wie die zahlreichen Reaktionen in der Scientific Community zeigen. Obwohl das Projekt mit dem Auslaufen der Finanzierung durch die Union der Deutschen Akademien Ende 2019 formell abgeschlossen wurde, wird die weiterhin online zugängliche Datenbank durch die gleich vorzustellenden Projekte laufend ergänzt und weiterentwickelt -und bleibt damit aktuell. Gleichzeitig können das RAG und die neuen Datenbanken durch die Schaffung einer gemeinsamen Plattform erweitert werden, über welche sich bereits bestehende Datenbanken externer Projekte zur Universitätsgeschichte durch «Data linking» abfragen lassen. Entsprechende Vorarbeiten sind in den vergangenen Jahren im Rahmen der aus dem europäischen Netzwerk Héloise heraus entstandenen Untergruppe CAAD (Collaborative Approach on Medieval and Early Modern Academic Databases) vorgenommen worden 2.
net] from LAB1100, [http://lab1100. com], 16. 06. 2021. Zur Anwendung vgl. Kaspar Gubler und Rainer Christoph Schwinges, Repertorium Academicum Germanicum (RAG). A New Database for Web-Based Analysis and Data Visualization, in: Annali di Storia delle universtà italiane 21 (2017), S. 13-24; Kaspar Gubler, Database Migration Case Study. Repertorium Academicum Germanicum, in: HistData, 03. 02. 2020, [https:// histdata. hypotheses. org/545], 16. 06. 2021. Kaspar Gubler und Christian Hesse, Forschungsimpulse durch Datenvernetzung. Perspektiven für das Repertorium Academicum Germanicum (RAG) und das Repertorium Germanicum Online (RG Online) im European Network on Digital Academic History, in: Kuriale Quellen und Digital Humanities. Neue Perspektiven für das Repertorium Germanicum (1378-1484), hg.