dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, we used the procedure recommended by  Armstrong & Overton (1977)  to test for nonresponse bias. We compared early-returned questionnaires to late-returned questionnaires on the key research variables used in the study (see  Chaudhuri & Holbrook, 2001; Compeau & Higgins, 1995; Poppo & Zenger, 2002). The assumption for this analysis is that late respondents share similar characteristics and response biases with non-respondents. Because no significant differences in the key research variables were found between the first and last quartile of the respondents, we concluded that there was no evidence of obvious response bias in the sample. Further, an outlier analysis conducted on the compiled dataset using Cook's distance statistic did not indicate any significant outliers  (Cook & Weisberg, 1999).
Measures Validated scales from existing literature were adapted to the research context to formulate our questionnaire (Appendix B). To measure the items, we used a 7-point Likert scale ranging from 1 ("strongly disagree") to 7 ("strongly agree"). Items for the measure of technological spatial intrusion (TSI) were adapted from prior literature. Specifically, building on  Cohen's (2008)   (Venkatesh, et al. , 2003). Further, because all data were self-reported and collected through the same questionnaire with a cross-sectional research design, common method bias may be a concern. To alleviate such concerns, we adopted several procedural remedies and statistical controls that are described in Appendix E. Our analysis shows that the results of this study do not suffer from any confounds due to common method bias.
Hypothesis testing The research model examines the influence of two employee technological spatial intrusion (TSI) variables, namely, employee accessibility and visibility, on the ICT-enabled employee innovation (H1 and H2). A two-step hierarchical regression model was used for testing the hypotheses. We used SPSS for our analysis. In the first step, we introduced all control variables, and in the second step, we introduced the two employee TSI factors, i. e. , accessibility and visibility. Following the guidelines outlined by  Aiken and West (1991) , we mean-centered all values before hypothesis testing to reduce collinearity. We also checked for multicollinearity of our predictors and calculated the variance inflation factor (VIF), and found no significant multicollinearity problems  (Hair, et al.
75, p<0. 01). Because both the paths were significant, mediation analyses were tested using the Sobel test (Table  3 , Upper Panel), which suggests that the level of accessibility has a positive influence on the level of innovation (Z = 5. 90, p = 0. 000) through perceived ICT usefulness. The Sobel test assumes that the sampling distribution of the mediation effect is normal when it is often skewed and thus leads to biased estimates  (Srivastava, Teo, & Devaraj, 2016). Methodologists (e. g. Hayes, 2009; Preacher & Hayes, 2004; Shrout & Bolger, 2002)  therefore suggest that this analysis be supplemented with bootstrapping method with bias-corrected confidence estimates  (Mackinnon, & Williams, 2004; Preacher & Hayes, 2004)  as bootstrapping overcomes the problem by repeatedly sampling with replacement from the dataset and estimating the mediation effect in each resampled dataset.
ACKNOWLEDGEMENTS This work has been partially funded by projects  GRUPIN/IDI/2018/000176 ,  PGC2018-098623-B-I00  and by the  Tox/EcoTox  project of the  Université de Technologie de Compiègne foundation. The authors also wish to thank  Gert de Cooman ,  Teddy Seidenfeld ,  Jasper De Bock  and the anonymous reviewers for stimulating discussion. This is an updated and expanded version of a paper [35] presented at ISIPTA 2019.
Apparatus Stimuli were presented on a gamma-corrected CRT monitor (Sony G400, 36 x 27 cm) with a 1280 x 1024 pixel resolution and an 85 Hz refresh rate. The experiment was conducted in a dimly lit room, using custom-written code in MATLAB version R2014b (The MathWorks, Natick, MA), with PsychToolbox version 3. 0. 11 (Brainard, 1997; Pelli, 1997; Kleiner et al. , 2007). A chin-rest was used to stabilize the participant at a viewing distance of 57 cm. Responses were recorded on a standard computer keyboard.
Model Fitting Detailed description of the model-fitting procedure can be found in the Supplementary Information (Sections 1 and 2). Briefly, model fitting was performed in three sequential steps. First, we estimated a per-participant d 0 and meta-d 0 using a hierarchical Bayesian model. We used as inputs the empirical d 0 and meta-d 0 calculated separately for each prior-payo↵ condition. These per-participant sensitivities were fixed for all subsequent modeling. Second, we fit the discrimination behaviour according to the Type 1 models, selecting the best-fitting Type 1 model for each participant before the final step of fitting the confidence behavior according to the Type 2 models. For the Type 1 and Type 2 models, we calculated the log likelihood of the data given a dense grid of parameters (↵, , and ) using multinomial distributions defined by the stimulus type, discrimination response, and confidence response.
Acknowledgements This work was supported by  NIH  grant  EY08266 ,  NIH Training Program in Computational Neuroscience  grant  T90DA043219 ,  NSF Collaborative Research in Computational Neuroscience  grant  1420262 ,  NSF   GRFP DGE1342536 , and  French ANR  grant  ANR-18-CE28-0015-01  " VICONTE ".
The main idea of YAGO was to harvest information about entities from the infoboxes and categories of Wikipedia, and to combine this data with an ontological backbone derived from classes in WordNet  [4]. Since Wikipedia is an excellent repository of entities, and WordNet is a widely used lexical resource, the combination proved useful. YAGO sent each fact through a pipeline of filtering, constraint checking, and de-duplication steps. This procedure scrutinized noisy input and boosted the quality of the final KB, to a manually verified accuracy of 95%. This precision was possible thanks to the tight control that the YAGO creators had over the extraction process, the filtering process, the ontological type system, the choice of the relations, and the semantic constraints. However, despite new versions YAGO2 and YAGO3 with substantial jumps in scope and size, the focus on Wikipedia infoboxes meant that YAGO has not arrived at the same scale as Freebase or Wikidata.
Meanwhile, Wikidata  [18]  has evolved into the world's foremost publicly available KB. It is a community effort where anybody can contribute facts -either by manually adding or curating statements in the online interface, or by bulkloading data. Wikidata has motivated more than 40,000 people who contribute at least once a month. The result is a public KB with 70M named entities, very good long-tail coverage, and impressive detail. 1 t the same time, Wikidata understands itself as a collection of information, not as a collection of universally agreed-upon knowledge. It may intentionally contain contradictory statements, each with different sources or validity areas. Therefore, Wikidata does not enforce semantic constraints, such as "each person has exactly one father". Furthermore, the large user community has led to a proliferation of relations and classes: Wikidata contains 6.
7k relations, of which only 2. 6K have more than 1000 facts, and it comprises around 2. 4M classes 2 , of which 80% have less than 10 instances. Many instances (e. g. , all cities) are placed in the taxonomy under more than 60 classes, with three-fold multiple inheritance. This complexity is the trade-off that Wikidata has found to accommodate its large user community. For downstream applications, the convoluted and often confusing type system of Wikidata make browsing and question answering tedious. Moreover, there is little hope to run strict classical reasoners (e. g. , for OWL 2) in a meaningful way, as the KB contains many small inconsistencies so that every possible statement is deducible regardless of whether it is intuitively correct or false. Some of these issues have been pointed out in the comprehensive study of KB quality by  [19].
As a consequence, a query for mathematical concepts returns entities like tensor, polynomial, differential equation. and the Potala Palace as answers. Contribution. In this resource paper, we describe the new YAGO version, YAGO 4, which aims to combine the best of the two worlds: It collects the facts about instances from Wikidata, but it forces them into a rigorous type hierarchy with semantic constraints. The complex taxonomy of Wikidata is replaced by the simpler and clean taxonomy of schema. org [8]. The classes are equipped with SHACL constraints  [12]  that specify disjointness, applicable relations, and cardinalities. This way, YAGO 4 transfers the rationale of the original YAGO from the combination of Wikipedia and WordNet to the combination of Wikidata and schema. org. The result is a new knowledge base, which is not just large, but also logically consistent, so that OWL-based reasoning is feasible.
Finally, we check the cardinality constraints, removing all objects if there are too many for a given subject. Our system is implemented in the Rust programming language 8  , using the Iterator infrastructure to ingest and output data streams. We use the already existing stream operators, which resemble those of relational algebra (map/project, filter, flat map, collect/materialize into a hash structure). We also implemented new operators particularly for YAGO 4 (stream-hash join, streamhash anti join, group-by, and transitive closure). For example, the owl:sameAs links between YAGO 4 and Freebase can be extracted from Wikidata by the following algebraic operator plan: π <yago,owl:sameAs,<http://rdf. freebase. com/ns/+str(o)>> s=wd σ matches(str(o),/m/0([0-9a-z ]{2,7}) σ p=wdt:P646 Wikidata WikidataToYagoMapping Here, π is the projection operator, σ the selection, the inner join, W ikidata the table of all Wikidata triples (s, p, o), and W ikidataT oY agoM apping the mapping between Wikidata and YAGO instances (wd, yago).
To avoid reading the full Wikidata N-Triples dump each time, we first load the Wikidata dump into the RocksDB key-value store to index its content  9. This index allows for efficiently selecting triples based on a predicate or a (predicate, subject) tuple, and getting back a stream of triples from the database. The advantage of having operator plans in Rust is that we can benefit from declarative programs where performance optimizations are carried out by the compiler, generating highly efficient native code. After having loaded the data into RocksDB, our execution plan generates the Wikipedia-flavored YAGO 4 (see below) in two hours on a commodity server. We ran our system on a dump of 78M Wikidata items. 8M of these are entities about Wikimedia Websites-related entities, such as categories.
Data YAGO 4 is made available in three "flavors": • Full: This flavor uses all data from Wikidata, resulting in a very large KB. • Wikipedia: This smaller flavor of YAGO 4 contains only the instances that have a Wikipedia article (in any language). • English Wikipedia: This is an additional restriction of the Wikipedia flavor, containing only instances that have an English Wikipedia article. All three flavors of YAGO 4 are built in the same way, and have the same schema, with 116 properties and the same taxonomy of 140 top-level classes from schema. org and bioschemas. org, and the same subset of Wikidata classes. Table  2  shows statistics for the three YAGO 4 variants, generated from the Wikidata N-Triples dump of November 25, 2019. Each flavor of YAGO 4 is split into the following files: • Taxonomy: The full taxonomy of classes.
Size statistics for YAGO 4 in the flavors Full, Wikipedia (W), and English Wikipedia (E), Wikidata and DBpedia (per DBpedia SPARQL server on 2020-03-04). License. The entire YAGO 4 knowledge base, as well as all previous versions and the logo, can be downloaded from the Web page. YAGO 4 is available under a Creative Commons Attribution-ShareAlike License. The reason for this choice is that, while Wikidata is in the public domain, schema. org is under a Creative Commons Attribution-ShareAlike License. 10SourceCode. We have released the source code for constructing YAGO 4 on GitHub at https://github. com/yago-naga/yago4under the GNU GPL v3+ license. SPARQL Endpoint. YAGO 4 comes with a responsive SPARQL endpoint, which can be used as an API or interactively. The URL is http: //yago-knowledge.
Digital Archiving in the Arab World Aix-Marseille University SSH data platforms: Skills to support research in social sciences and humanities (SSH) in the Mediterranean The development of computer science significantly transformed scientific practices in the last quarter of the 20th century. Information technology (IT) first made it possible to develop computing power, then information storage capacities. It initially led to an increase in discoveries in the most quantitative sciences as it allowed accelerated calculations, particularly in fields where the data was quantitative and structured (e. g. tables of data in SSH). It then allowed new possibilities to be developed in more qualitative sciences, thanks to the increase in information storage spaces and the development of qualitative or non-explicitly structured data processing (e.
2016). These four main principles relate to different dimensions of data archiving and dissemination. To be "Findable", the data must be indexed on the internet, preferably with a unique and persistent identifier (PID). A persistent identifier is characterised as a unique character string which refers to an object (this may be a physical object, but also a web page or any other document). It is said to be persistent since the identifier will not change even if the designated object is moved. The DOI (http://www. doi. org/) is certainly the most well-known and most widespread of these identifiers, though others are also valid  (Cousijn, Kendall et al. 2018). Thus, a DOI for a journal article or database will remain the same, even if the article or database is moved to the file tree structure of a website or to another website.
The notion of accessibility is understood here in a narrow sense and refers to the transparency of data recovery protocol. "Open data" is, of course, accessible. Data that requires special protection (confidential data, for example) can be considered accessible if the authentication and authorisation procedures are explicit and visible. Interoperability is based on the principle that data is associated with elements that describe it unequivocally, but also on the fact that this set of data/ metadata is structured and conceptualised using shared, documented standards. Finally, to be reusable, the data must be organised in databases using common standards and must be well documented, that is to say with precise and relevant metadata. Metadata consists of sets of elements describing the data made available.
This is the case for the many online mapping interfaces (often based on geographic information systems), but also for an increasing number of data visualisation modules (there are numerous examples, but we may cite the Eurostat Data Browser and its tabular, graphic and cartographic interface for thematic explorations). The other aspect of mediation is human support. This is based on the involvement of a more experienced user in methodological or technical terms. In the French research system this concerns, for example, the role usually assigned to some of the research assistant found in research laboratories.
It was materialised in 2012 with the creation of the "Quetelet PROGEDO" Mixed Unit of Service which took over the activity of the Quetelet network. In 2017, the CNRS decided to assign the ADISP team to Quetelet-PROGEDO. In 2018, Quetelet-PROGEDO was transformed into a Service and Research Unit, called simply "PROGEDO". It fulfils the role formerly carried out by the CCDSHS, namely that of defining a data policy for the humanities and social sciences, as well as the mission of the Quetelet network (data dissemination) through the "Quetelet-PROGEDO-Diffusion" Today, the "Quetelet-PROGEDO-Diffusion" portal (http://quetelet. progedo. fr/) is firmly positioned in the FAIR data perspective, providing access to data according to its level of sensitivity (see below) and documenting it up to the level of questionnaire variables when possible.
As we will see, despite positive evaluation, experience shows that it is essential to provide additional support for the least skilled users and to continue efforts to publicise the services offered. This explains why in France, in particular, this tool is not only digital, but is simultaneously based on human resources which can answer to various requests from users. In order to make the data accessible, in the broad sense of the term 11 , efforts must be continued in supporting visitors to websites which provide data in order to make the best use of it in their analysis. Two tools we are responsible for have inspired us to illustrate this necessary articulation of digital enhancement tools with personalised support systems using support staff. Namely, the specific interface of the demographic observatory of the Mediterranean, DemoMed, and its tools, designed to simplify the reuse of spatial demographic data for the Mediterranean region, and the Aix-Marseille University data platform.
org) in order to make the data from its research available. This interface was developed with the aim of making the data accessible. From the outset, this notion of accessibility was designed to function without an intermediary between the user and the data. For this, software development was geared towards the simplest possible access but also taking into account the variety of possible users  (Oliveau, Doignon and Blöss 2018). The query interface does not only allow searches via an interactive map at the Mediterranean level, but also through SQL type queries, translated into everyday language. The results of data searches are offered in different forms (tables, maps, graphs), at the user's choice and depending on the data. To simplify access to the data, the interface offers two main starting points.
The first is a tool for obtaining data in the form of a table or graph. It leads the user to the data they are looking for (place, date, type of data, theme, phenomena, indicators, etc. ) through a classic mode of successive selection via drop-down menus and check boxes. Mandatory items must be completed by the user, however it is possible to refine the search by entering other optional information. The second way to access the data is a cartographic interface, which allows the database to be represented by exploring the Mediterranean region. The team worked hard to make the use of this cartographic visualisation tool as intuitive as possible for non-cartographers. The imported variables are enriched with as much metadata as possible (definitions, bibliographical references, various information).
These skill platforms all constitute real skill supports for developing a community of quantitative data users. The UDP of Aix-Marseille University, which we belong to, was officially created in 2018 (http://pud. mmsh. univ-aix. fr/) and, like other UDPs, offers training activities as well as specific support for researching quantitative data in SSH in the Mediterranean. The history of UDPs dates back to 2001. The idea of locally developing a support unit for researchers first appeared at the University of Lille 1 3. This came about in 2003  (Duprez, Cros 2010). Supported by the University of Lille 1 since its creation, the Lille University Data Platform joined the Maison Européenne des Sciences de l'Homme et de la Société (European House for Social and Human Sciences) in 2015. It remained the only initiative of its kind until 2009, when a second platform was created in Lyon 14 on the initiative of the Lyon-Sainte-Etienne House for Human Sciences.
These UDPs were created due to a strong need felt by local research teams, primarily to support researchers, but also to support students as they begin research work, i. e. in discovering and using the breadth of data available. More generally, there is a significant need for advice and training on the use of survey data or data from large databases. Faced with this situation, TGIR PROGEDO therefore took the initiative to stimulate the development of these UDPs, which form the necessary link between digital platforms and users. The initiative promotes the use of data from French, European and international research and public statistics (survey and administrative data, etc. )  (Chenu, Lesnard 2011). This data is mostly quantitative. For example, the research assistant attached to Aix-Marseille University's UDP provides assistance in searching for data, accessing it, processing and exploiting it.
An academic fellow is designated as scientific advisor for the UDP exercises support functions for all researchers, research professors, administrative and technical staff, doctoral students and master's students at Aix-Marseille University. The additional aim of the Aix-Marseille UDP is to develop expertise on human and social science data for the Mediterranean region. In addition to their primary mission of personalised research support, the research assistant organises training, seminars, study days and summer schools. He visits various teaching and research institutions to publicise the platform and its missions. He also actively participates in exchanges and associated activities in the national network formed by all UDP. Research assistants recruited by the UDPs have a common profile: they have strong statistical skills and teaching motivations.
They also must all have a very good knowledge of quantitative disciplines like demographics, statistics, geography, economics or quantitative sociology. This is to ensure that these new research support staff can share their experience in processing quantitative databases and statistical data processing methods with less experienced applicants. They must also demonstrate disciplinary openness to accommodate somewhat diverse requests for help from various backgrounds. The extremely positive feedback from those using the services offered by the UDPs once again demonstrates that while IT has managed to replace humans in many tasks, there is still a need to maintain close human interaction in many situations. Information technology first supported researchers in their calculations, then in managing their data.
CONCLUSION: SIGNIFICANT YET ESSENTIAL INVESTMENTS In France, the social science research system is reaching maturity and is structured in the form of collective tools, which notably allow better management of the data produced. We are moving away from the individual observations and interpretations that characterised the system and heading towards a more shared organisation, comparable to that found in other sciences. This method of organisation has a cost, but it is essential for developing and maintaining databases. One question remains: that of this work's sustainability. The increase in skills due to training by human means is sustainable. It allows those involved in SSH research to be better trained and better supported. This fully justifies investing in university data platforms, as well as in any other human support tool.
Decree no. 2001-139 of 12 February 2001. The decree no. 2015-1469 of 13 November 2015 abolished the CCDSHS. 3 "Mixed Unit of Service T2419", the "Quetelet Centre for archiving and disseminating data in the humanities and social sciences". 4 CDSP is the successor of the sociopolitical data bank established at the Grenoble Institute of Political Sciences, IEP. 5 ADISP inherited the database developed within the Laboratory of Secondary Analysis and Methods Applied to Sociology (LASMAS). LASMAS became the Maurice Halbwachs Centre in 2004 6 Ministry of Higher Education and Research, 2008, p. XIII 7 PROduction and GEstion des DOnnées: Data Production and Management. internet portal. It is also responsible for archiving quantitative data via the ADISP team 8.