dataesr/scientific-paragraphs-categorization huggingface dataset:
Crawford -Shinn -Sörlin 1993, p. 23-27. 8. Buela-Casal et al. 2006, p. 46-47. 9. Somsen 2008. 10. Heilbron 2008, p. 8. approaches. On the other hand, even if bibliometric data have been used for decades as sources for the history of science, 11 these studies often lack a precise historical or sociological contextualisation of the bibliographic data under study. To address this limitation, this paper focuses on a case (i. e. a scientific field, language, and period) that is poorly represented in the main bibliometric databases of today: namely, prehistoric archaeology in Italy from 1875 to 2000. In this framework, the relationship between bibliographic data and its socio-historical contexts can be explored. I propose an approach combining general information from bibliographic statistics and fine-grained information from qualitative data.
AN ENRICHED BIBLIOGRAPHIC DATA SET The data set 12 includes all the articles published in 5 journals between 1875 and 2000 (tab. 1). These journals were selected as they cover the broadest range of prehistoric archaeology studies in Italy. Some were included for specific reasons: the Bullettino di paletnologia italiana (BPI) has the As the main bibliographic databases poorly represented scientific articles in archaeology and languages other than English, 13 I constructed the data set by combining information extracted from the following sources: direct examination of the journals, the PreBiblio, 14 Dialnet,  15  and Google Scholar databases. This information was organised into 3 tables: articles (n=2842), authors (n=1221), and relations between articles and authors (n=3366). The languages of the articles were described manually as well as the nationality of the authors (this biographical feature has been used instead of authors' institutional affiliations which are generally used in bibliometric studies as it is easier to retrieve from databases).
Results Internationality of the journals was determined by analysing the nationalities of the authors and the languages of their articles. A total of 42 different nationalities (tab. 2) and 6 different languages (tab. 3) was observed in the full data set. Graphic representations of the proportion of the main represented nationalities (fig. 2 ) and languages (fig. 3 ) over time are given. Although the BPI had few articles in non-Italian languages before 1950, we see that linguistic and national diversification occurred during the second half of the twentieth century, after The subset analysed with a χ 2 test is indicated by italic values. the fascist regime. For both variables (nationality and language), the general profile of each journal does not change radically over time, except for Quaternaria, whose rate of non-Italian authors increased significantly from the mid-1960s.
CONCLUSION: THE HISTORICAL VALUE OF BIBLIOMETRIC APPROACHES This case study aimed to address some classic concepts of bibliometric analyses -such as the transition from national to transnational science-by examining a scientific discipline, language and time period poorly represented in bibliographic databases used in bibliometric studies. Zitt et al. characterised this transition by the appearance of three features in scientific productions: indexed journals (in main bibliographic databases), prominence of the English language, and concentration in high impact journals. 40 Although my data cannot test these three aspects, results suggest that a transition to a transnational model of science did not occur during the study period. However, results reflect the structuration and aggregation of a cognitive and social network at the national scale, different editorial strategies and the growth of research trends.
Journals inCluDeD in the Data set, with City oF PuBliCation, First anD last year oF PuBliCation (or last year inCluDeD in the Data set iF the Journal has ContinueD to Be PuBlisheD aFter this Date). GROWTH IN ITALIAN PREHISTORIC ARCHAEOLOGY ACTIVITIES taB. 1 -Journal name City First year Last year Number Number of publication of publication of publication of articles of volumes included BPI Rome 1875 1999-present 1520 90 RSP Florence 1946 2000-present 334 50 Quaternaria Rome 1954 1981 705 23 Origini Rome 1967 2000-present 192 22 ATA Siena 1973 1998 91 13 Sum 2842 Thompson Scientific's database, see Archambault et al. 2006. 14. http://prebiblio. uniroma1. it. 15. https://dialnet. unirioja. es.
Data and variable construction We use data from the South African National Research Foundation (NRF 1  ) from 1970 to 2004 which contains detailed personal information of the scholars (i. e. gender, ethnic group, affiliation, career history, scientific field, and NRF rating). Our main variable is the NRF "rating" for years 1983-2012, which is a measure of individuals' academic performance. The process by which the NRF grades researchers is rigorous, because it involves international referee reports looking at the CVs and the scientific output of each candidate applying to be rated. This process ends with a grade: a scientific committee evaluates the quality content of the referee reports and assigns the rating based on 13 ordered categories. The rating is unsuccessful when the referee reports are of poor content and/or the committee lacks the information to make a judgement.
Strong institutional incentives imply that almost all academics with a research oriented career apply to be rated: NRF data cover the 30% of scholars in the country who produced about 90% of all South African peer-reviewed research outputs  (Barnard et al. , 2012; León et al. , 2016). Our analysis focuses on scholars in the field of Science, Engineering and Technology (SET), but for completeness we also provide information on the field of Social Sciences and Humanities (SSH). In the field of SSH language and schools of thoughts usually put constraints on the PhD to first job transition. These constraints are particularly relevant and represent a bottleneck in the South African context. Separately for each field (SET and SSH) we construct the hiring network among the different South African institutions, based on scholars who found their first jobs within 5 years of receiving the PhD.
Decision-theoretic and value-of-information designs for clinical trials in small populations Most methodology for clinical trial design makes no reference to the size of the population in which the research is conducted. Whilst this may be reasonable in a large population, in rare diseases or other small populations it could lead to designs that are inappropriate. In order to establish the context for future research work, we completed an analysis of trials in rare diseases recorded in the ClinicalTrials. gov database as well as exploring novel methods. This showed that the sample sizes in phase 2 trials in rare diseases were similar for different prevalence but that phase 3 trials in rare diseases with lowest prevalence were statistically significant lower than those in less rare diseases and were more similar to those in phase 2 as shown in Fig.
A Bayesian approach offers another way to perform random-effect meta-analyses within the NNHM framework. One of the advantages is that the solution remains coherent also for small numbers of studies; on the other hand, careful prior specification is required, and the approach is usually computationally more demanding. We developed a general semi-analytical approach to solve the meta-analysis problem (and, in fact, a more general class of problems involving mixture distributions) via the DIRECT approach  [34]. We have implemented this in the bayesmeta R package, to provide an efficient and user-friendly interface to Bayesian random-effects metaanalysis  [35, 36]. The developed software allowed us to perform large-scale simulations to compare the different approaches in the special case of few studies; for an example of such a scenario, see Fig.
Besides issues of regulatory harmonization, another hurdle to the widespread implementation of novel statistical methods is the availability of software. To address this issue, we have produced open access statistical software to run on the freely available software environment R  [48]  to implement the new approaches that we have developed in meta-analysis and network meta-analysis (packages bayesmeta and nmaINLA) and in dose-finding (package dfpk and dfped). These software packages are available for download from the Comprehensive R Archive Network (https://cran. r-project. org).
, 2013). Adaptation implementation often depends on alignment with other programmes (e. g. health) that are designed to address non-climate related problems as well. In this study we use the term 'city' to refer broadly to all local authorities with urban characteristics, i. e. urban areas, towns, and cities. Specifically, the study analyses the LCPs of 885 Urban Audit (UA) cities across the EU-28 countries. Data on UA cities is available in the Eurostat repository, based on information collected and provided by the National Statistical Institutes, the Directorate-General for Regional and Urban Policy and Eurostat. We first develop a typology of LCPs in Europe. We then identify and review existing LCPs, focusing on stand-alone, comprehensive LCPs that were developed with climate change mitigation and/or adaptation as the main motivation.
The sample of cities The analysis is based on the entire sample of 885 UA core cities in the EU-28, and uses some of the data provided in the UA database,  3  which is now called "Statistics on European cities". The UA city sample currently contains 885 core cities and 22 greater cities or larger urban zones across the EU-28, plus a number of cities in Iceland, Norway, Switzerland and Turkey. The more than 900 cities in the EU-28 together represent 25% of the EU's population. The UA defines a city as a local administration unit (LAU) where the majority of the population lives in an urban centre of at least approximately 50,000 inhabitants. However, as explained below, to ensure representativeness within countries and across the EU-28, the UA also includes some smaller urban centres with less the 50,000 inhabitants.
The UA adopted the following criteria in order to ensure a balanced and regionally representative sample (see Fig. 1 ): cities in each country should represent about 20% of the population in the country, have a good geographical distribution (at least one city from each NUT3 Region), and vary in size to include large and small cities (including some urban centres with less than 50,000 inhabitants). The UA is run by the European Commission and Eurostat (2017) and has been developed in cooperation with the national statistical offices to compare data across European urban areas. Datasets include statistical information on individual cities and on their commuting zones (called 'Functional Urban Areas'). The topics and datasets that are reported by the database are wide ranging and include, for example, demography, housing, health, environment, and education.
Selection of local climate plans for the Urban Audit cities sample For each country, a team of authors (with native or full professional language proficiency) compiled a database of local climate (mitigation and adaptation) plans through a combination of desk/ web review and occasionally direct contact with local authorities. We took advantage opportunities to obtain the information we needed online and only contacted the respective city representative(s) in cases where further information and/or clarification were needed. In all cases the respective plan or policy had to be, or to be made available to us. A more comprehensive version of the analysis guidelines can be found in the Supplementary Information. The information extracted was entered into a database, where the name of the mitigation and adaptation strategy, the web link, and the date of search was recorded, along with comments on particularities of each city.
The relevant documents where downloaded and saved. The LCP may either be officially adopted by the municipal government, or simply acknowledged and noted; it may be binding or non-binding. The database includes draft and finalized plans as well as current and past strategies, i. e. including those with a timeframe that had already expired (e. g. 2010e2016). We included draft documents because we assume that the planning process is just as important as the plan itself  (Heidrich et al. , 2013; Millard-Ball, 2013)  and that a draft plan can already produce effects such as awareness raising and capacity building. The size of a municipality or local area differs across Europe and this has implications for what counts as local climate plan. For example, in France, municipalities are small compared to other countries.
Discussion and conclusion Our dataset includes 885 cities in all 28 EU countries, and is the first to provide a detailed database of local climate action. It is thus much more comprehensive and representative than previous similar studies. It contributes towards establishing patterns of local climate action and assessing the effectiveness of action by cities in support of EU policy targets geared towards combating climate change and meeting the objectives of the Paris Agreement. Data collected for this study was last updated in January 2017 (with some exceptions, e. g. climate networks). This allowed plans developed in the wake and immediately after the 2015 UNFCCC Conference in Paris, which saw a significant increase of climate action at all levels, to be included. This paper has presented the data and provided an initial analysis.
Acknowledgements We gratefully acknowledge all local municipalities who make their engagement in climate planning public. We thank staff and representatives of all the local authorities for their kind support. Special thanks to  Birgit Georgi , who helped in setting up this large network of researchers across the  EU-28 , as well as to  Georgi Stefanov  for helping out in the Bulgarian case and  Aleksandra Gutfranska  for analyzing the Polish situation. We also thank  EU COST Action   TU0902  that made the initial work possible and the positive engagement and interaction of the members of this group which led to this work. MO acknowledges funding from the  Spanish Government  (Grant no. FPDI-2013-16631 ). EKL was supported by the  Ministry of Education, Youth and Sports of CR  within the  National Sustainability Program I (NPU I ), grant number  LO1415.
Introduction Despite the increasing computer efficiency over the last decades, numerical simulations of welding processes remain prohibitive in terms of CPU time and storage, due to their multi-parametric aspect. To estimate the welding quantities of interest (residual stress, distortion, etc. ) depending on different input parameters (typically, the geometry, materials properties, boundary conditions and the imposed loading), larges computations have to be re-run. Thus, the construction of computational vademecums  [1]  (called also vir-tual charts  [2, 3]  or meta-model computations) with standard computations remains a cumbersome task. The idea of computational vademecums consists in computing at the offline stage general solutions of a parametric problem, so as to make available a database of solutions.
Their approach also does not meet requirements 3 and 7. In 'Arithmetic with Measurements on Dynamically-typed Object-oriented Languages'  [28] , the authors implement -in Smalltalk -a measurement system that is general enough to include the SI units for physical measurements, as well as non-physical measures such as units of currency (see section 5). The choice of Smalltalk is interesting because the language's dynamic type system gives the programmer more support for defining new types, but restricts the strength of static analysis that can be done. Their approach does not support our requirements 2,4, and 5. In 'Osprey: A Practical Type System for Validating Dimensional Unit Correctness of C Programs"  [17] , the authors present a sound type system to automatically check for all potential errors involving units of measurements.
AltaRica 3. 0 is an event-based, object-oriented modeling language dedicated to (probabilistic) safety analyses of complex systems. It makes it possible to design models at higher level than done with formalisms traditionally used for safety analyses (fault trees, Markov Chains, stochastic Petri nets, etc. ), without increasing the complexity of calculations of risk indicators. Several assessment tools have been developed for AltaRica 3. 0, including a stepwise simulator. This tool is of a great help for the design and the validation of AltaRica 3. 0 models. It is the analog for modeling of debuggers for programming. In this article, we show how the AltaRica 3. 0 stepwise simulator has been greatly enhanced by the introduction of an abstract notion of time. The key mathematical property is that abstract and concrete simulation are bisimilar: any concrete (timed, stochastic) execution can be simulated by an abstract execution and reciprocally any abstract execution corresponds to at least one concrete execution.
INTRODUCTION AltaRica 3. 0 is an event-based, object-oriented modeling language dedicated to (probabilistic) safety analyses of complex systems  (Prosvirnova, Batteux, Brameret, Cherfi, Friedlhuber, Roussel, & Rauzy 2013). It makes it possible to design models at higher level than done with formalisms traditionally used for safety analyses (fault trees, Markov Chains, stochastic Petri nets, etc. ), without increasing the complexity of calculations of risk indicators. The semantics of AltaRica 3. 0 is defined in terms of stochastic guarded transition systems  (Rauzy 2008 ). AltaRica 3. 0 executions are similar to those of other discrete event modeling formulisms: each time a transition gets fireable, it is scheduled and possibly fired after a certain real-valued delay, see e. g. (Cassandras & Lafortune 2008 , Zimmermann 1976 ) for introductions to (stochastic) discrete event systems.
This demonstration showcases ProvSQL, an open-source module for the PostgreSQL database management system that adds support for computation of provenance and probabilities of query results. A large range of provenance formalisms are supported, including all those captured by provenance semirings, provenance semirings with monus, as well as where-provenance. Probabilistic query evaluation is made possible through the use of knowledge compilation tools, in addition to standard approaches such as enumeration of possible worlds and Monte-Carlo sampling. ProvSQL supports a large subset of non-aggregate SQL queries.
INTRODUCTION When evaluating a query, it is often useful to capture metainformation about the result of a query, along with the result itself. The meta-information may indicate where the query result comes from, how it was computed, how many times each result was produced, what probability each result has, etc. Formal tools to capture such meta-information are data provenance  [7]  and probabilistic databases  [28]. This demonstration introduces ProvSQL, an open-source and lightweight module for the PostgreSQL database management system that adds support for data provenance and probabilistic databases. Many different provenance formalisms have been introduced for relational data provenance. ProvSQL captures most of them: provenance semirings  [19]  that generalize previous formalisms such as why-provenance  [7] , lineages used in view maintenance  [10] , or the lineage used by the Trio uncertain management system  [6] ; m-semirings  [17]  that extend provenance semirings with support for negation; where-provenance  [7] , not captured by semiring-based formalisms.
In addition, ProvSQL relies on provenance annotations to compute probabilities of query results, in the sense of probabilistic databases. ProvSQL is application-independent and to our knowledge the first system supporting such a large range of provenance formalisms. In particular, it is the first system with support of m-semiring provenance and of specialization of provenance to arbitrary, user-defined, semirings; it is also the first system that provides an uniform framework to capture both semiring provenance and probabilistic query evaluation. Existing probabilistic relational database engines such as MayBMS  [21] , Trio  [6] , or Orion  [9] , as well as the Perm  [18]  system for relational provenance management, are all implemented by modifying the internals of a fixed, now obsolete, version of the PostgreSQL DBMS, which results in code that cannot be easily maintained or even compiled on modern operating systems.
In contrast, ProvSQL is a lightweight extension to PostgreSQL, easily deployable on an existing PostgreSQL installation, not entangled with database engine code. Perm  [18]  and GProM  [4]  are similar in scope with the nonprobabilistic part of ProvSQL: they are systems to capture the provenance of queries in relational databases, with support for different forms of provenance, but not for probabilistic data. To solve the issues with tying Perm to a particular PostgreSQL version, GProM is instead built as a middleware between the user and the DBMS, rewriting queries to compute provenance annotations. The main differences between these systems and ProvSQL are as follows: • they do not allow computation in user-defined semirings; • they do not support the semantics of m-semirings; • they do not distinguish between set (DISTINCT) and multiset query semantics in the same way as ProvSQL; • the native provenance formalism, similar to why-provenance, stores provenance using additional attributes and rows of the result table -this approach is much less compact than the provenance circuit used by ProvSQL.
FOUNDATIONS We now give a brief review of the main foundations of ProvSQL: (m-)semiring provenance, where-provenance, probabilistic databases. For a more in-depth review of provenance in relational databases, see  [27]. A semiring (K, 0, 1, ⊕, ⊗) is a set K with distinguished elements 0 and 1, along with two binary operators: ⊕, an associative and commutative operator, with identity 0; ⊗, an associative operator, with identity 1. We further require ⊗ to distribute over ⊕, and 0 to be annihilating for ⊗. Examples of semirings include  [19, 23, 20] : • (N, 0, 1, +, ×): counting semiring; • ({unclassified, restricted, confidential, secret, top secret}, top secret, unclassified, min, max): security semiring; • (N ∪ {∞}, ∞, 0, min, +): tropical semiring; • ({positive Boolean funct. over X}, ⊥, , ∨, ∧): the semiring of positive Boolean functions over X.
Fix a semiring (K, 0, 1, ⊕, ⊗) and assume that all tuples of a database come with provenance annotations from K. Consider a query Q from the positive relational algebra (selection, projection, renaming, cross product, union). A semantics for the provenance of a tuple t ∈ Q(D) is defined inductively on the structure of Q, see  [19]  for details. Using this inductive definition of semiring provenance, one can use different semirings to compute different meta-information on the output of a query: counting semiring: the number of times a tuple can be derived; security semiring: the minimum clearance level required to get a tuple as a result; tropical semiring: minimum-weight way of deriving a tuple (as when computing shortest paths in a graph); positive Boolean functions: Boolean provenance as in  [22, 27].