dataesr/scientific-paragraphs-categorization huggingface dataset:
Mediation model estimation of predicted indirect effects was performed using structural equation modeling (SEM) with maximum likelihood estimation in Mplus software  (Muthén & Muthén, 2017). The bias-corrected bootstrap confidence interval procedure incorporated in Mplus allowed us to test the significance of each parameter while providing confidence intervals (CIs) for the parameter values (this procedure for estimating mediation models is widely recommended; see for example  MacKinnon, Lockwood, & Williams, 2004; Hayes, 2009). Model fit was considered acceptable if all the following criteria were met: A Chi-square goodness of fit (p-value >. 05), the comparative fit index (CFI) and the Tucker-Lewis index (TLI) >. 90, the root-mean-square error of approximation (RMSEA) and standardized root-mean residual (SRMR) <.
In these conditions, the number of individuals to be recruited was 655. For the present study, all 630 men interviewed were included in the analysis (23 women were excluded). Legal authorization was obtained by the "Comité de Protection des Personnes" (CPP) with number IDRCB 2012 A0144835, the "Commission Nationale Informatique et Liberté" (CNIL) with number MMS/VCS/ AR152838, and the "Agence nationale de sécurité du médicament et des produits de santé" (ANSM) with number 130500B-31. All interviewees provided written informed consent. The data were collected between March 2014 and April 2017. Each participant was interviewed for 45-60 min within the jail medical unit under strict conditions of confidentiality. Subjects were randomly approached for inclusion from admission lists of consecutively committed people in 8 of the 9 jails of Nord and Pas-de-Calais in France: Arras, Douai, Dunkirk, Sequedin, Annoeullin, Longuenesse, Maubeuge, and Valenciennes.
Data collected The MH-Prison Population and MH-General Population surveys used the same methodology to collect clinical and sociodemographic data. To characterize the samples, sociodemographic data were collected for gender, age in years (categorized into four bands:  18-24, 25-34, 35-44, and over 44) , education level (no education or primary level, secondary level, university level), marital status (single, married or coresiding with a partner, separated/divorced/widowed), employment status (employed, unemployed), monthly income in euros per household per month (categorized into five bands: <€534, €535-840, €841-1,300, €1301-2,520, >€2520), and history of migration for the participant, as well as his spouse, parents or grandparents (yes, no). T. Fovet et al. For each subject, the MINI (French version 5.
We introduced an interaction term, "PTSD status * sample" and adjusted the model for all covariates. The PTSD status term was categorical (i. e. , history of trauma exposure without PTSD symptoms, subthreshold PTSD, full PTSD), and the sample term was either the MH-General Population or the MH-Prison Population. Psychiatric comorbidities were also described for each trauma category and in each sample. We performed data analysis using R 3. 6. 1. The significance level was set at α = 0. 05, and all tests were 2-tailed.
, 2016). Second, no information about the history of incarceration was available for the MH-General Population sample, making it impossible to assess the impact of past incarceration on PTSD status in the general population. In addition, no information about the index offense and criminal history (except for prior incarceration) was available for the MH-Prison Population sample. Third, it was not possible to compare the types of traumatic events, as this information was not available in either database. Fourth, the study's cross-sectional nature does not provide a basis for inferring the timing of PTSD and psychiatric comorbidities, which may be both complications and predisposing factors of PTSD. Fifth, exposure to potentially traumatic events during childhood has been related to some personality disorders  (Battle et al.
Some recent patent infringement litigations initiated by universities have resulted in multimillion dollar damages and have attracted public attention and stimulated research especially among law scholars. In this paper, we build a brand new database that include patents filed by universities at the United States and Trademark Office (USPTO), their characteristics and, eventually, the information about whether they have been used in infringements lawsuits by universities in the years 1990-2019. Our study is articulated in two parts. First, we show that, although it is still a quite rare phenomenon, patent litigation involving universities has been significantly growing in the last 2 decades. Second, we study the characteristics of university patents that have been litigated vis-à-vis non-litigated university patents.
This has led   US (1990 US ( -2019)). This distribution is related to all patent infringement actions filed in the US by universities in the period 1990-2019. The litigation year corresponds to the year in which the cases started several authors to question whether universities can be compared to patent trolls  (Lemley, 2007). In our research, we develop a brand-new database of patents filed and eventually litigated by universities in the US. In particular, we collect patent infringement lawsuits involving universities up to 2019 from Clarivate Darts-IP 2  as well as US granted (utility) patents from  USPTO Patent Views (2018)  and Patent Assignment Database  (version 2017)  where universities appear as assignees. Our study is articulated in two parts. First, we describe the phenomenon of university patent litigation and its evolution over time, notably which kind of universities are more prone to litigation, which are the most active and which technological sectors are more involved.
Second, we focus on the characteristics of the litigated patents, with a particular interest in their value and on the type of universities involved in litigations (public vs private and US vs non-US). The academic literature has paid a great deal of attention to understanding the link between patent value and litigation. In this respect, the last part of our analysis is in line with works such as  Su et al. (2012)  who provide a study based on all utility patents issued by  USPTO from 1976 to 2010 and Gossart et al. (2020)  who focus on the LED sector specifically. The rest of the paper is organized as follows. In the next section, we present the theoretical background. In "Dataset construction" section, we describe the patent and litigation data; in "US universities in courts: key figures" section, we show the main figures, and, in "Econometric analysis of the characteristics of the university-litigated patents" section, we estimate an empirical model to evaluate the characteristics of the litigated patents by universities in the US in the years 1990-2019.
Our contribution Despite the importance of the topic, very few quantitative studies on university patent litigation exist  (Ascione et al. , 2021, pp. 59-70). To our knowledge, only  Rooksby (2011)  and  Firpo and Mireles (2018, 2020)  collect and examine data on patent litigations initiated by universities in the US. In particular,  Rooksby (2011)  analyzes 57 cases in 2009 and 2010, finding out that most of the litigated patents relate to the pharmaceutical sector and involve an exclusive license. Firpo and Mireles (2018)  study litigation cases filed by universities, foundations and non-profit organizations in the period 2000 to 2015, using the Stanford Non-Practicing Entity (NPE) Litigation Database. The authors look at measures of patent value and explore other litigation-related variables.
Patent data The exploitation of patent data presents two main challenges. First, patents' assignees names are not harmonized. Second, the sector of the patent assignee (private business enterprises, universities/higher education institutions, governmental agencies, individuals, etc. ) is not reported. In order to address the first issue, we develop an algorithm to clean and consolidate patent applicants' names. 8 In order to identify the sector of the patent assignee (in particular, whether the assignee is a university) we exploit the information from Ecoom-Eurostat-EPO-Patstat (EEE-PAT) database  (Callaert et al. , 2011). Whenever a correspondence between the patent assignee at the USPTO and the assignee at the EEE-PAT is not found, we perform an automatic search for keywords in the assignee name to allocate the unassigned entities to a unique sector.
Litigation data Data on litigation cases are collected from Clarivate Darts-IP, which allows us to identify the type of actors involved (company, university, patent assertion entities, person, and authority). For this study, we collect all patent infringement actions in the US involving universities (including their TTOs, affiliated foundations and schools of medicine) as plaintiff, up to December 31, 2019. Patent litigation data contain information about where the cases were filed, the relevant dates, the patent number, the names of plaintiffs and defendants, the outcome (if any), as well as other information associated with the cases. After having collected all the patent infringement cases, we clean and harmonize plaintiffs' and defendants' names, taking into account different spellings to put together each university with its affiliated research entities.
US universities in courts: key figures The final database includes 574 infringement actions filed in the US and 813 disputed patents issued at the USPTO, of which 521 are university patents-that is patents assigned to universities-and 292 are patents assigned to other types of organizations classified as university co-plaintiffs in the patent infringement action. The average number of patents in dispute per case is 2. 76 and about one-third of these cases involve more than two patents. The jurisdiction with the most cases is the District of Delaware, 10 where universities file more than 16% of cases, confirming the findings of  Rooksby (2011)  and  Firpo and Mireles (2018). The District of Texas Eastern and, to a lesser extent, the District of Delaware are among the top four districts in terms of patent holder favorability  (Barry et al.
Since we could not find an official list of private and public universities in the world, we scraped a publicly available website (https:// www. 4icu. org/) that contains a list of 13,745 entries of universities in North America, Asia, Africa, Europe, and Oceania. To our knowledge, this website is the only one that provides free access to a complete list of universities with the information on type of the university (private vs public) via the variable "con-trol_type". For the purposes of this analysis, the four categories of "control_type" (public, public-state, public-private partnership, and private) are reduced to public (including public, public-state, and public-private partnership universities), and private. In order to match the assignee list of our dataset with the list of universities from www.
Variables and methodology The descriptive statistics from the last section provide an overall picture of the phenomenon of patent litigation by universities in the US. In this section we try to go one step forward, by relying on an econometric model to identify the main characteristics of litigated university patents compared to non-litigated ones. In the econometric analysis, we consider all patents filed at USPTO by universities since 1990 and granted until 2014. We drop all patents granted after 2014, because only few of them have been litigated afterwards. The final dataset contains 122,182 patents, of which 469 have been litigated (in infringement cases) by universities, corresponding to about 0. 4% of all university patents. In our econometric exercise, the response variable (Litigation) is thus a dichotomous variable whereby litigated patents are designated as "1" while non-litigated ones are coded with "0".
Extreme consequences of this vision include the tendency to patent as much as possible to alleviate economic pressure to cover their costs, and the involvement of TTOs in patent litigation as "buffer" organizations between the filing university and the defendants  (Rooksby, 2011). Despite the increasing attention paid to the modern rise in patent filings and the involvement of patent trolls in the patent market, the characteristics of universities' increasing interest in litigation have not received equal attention, with the exception of few cases  (Firpo & Mireles, 2018 , 2020; Lemley, 2007; Rooksby, 2011). In our work, we go further in depth by collecting data on patents filed by universities at the USPTO and infringement suits brought by universities in the years 1990-2019. In doing so, we provide a detailed exploratory analysis that describes this overlooked phenomenon.
Moderating effects analysisLogit regressionsUnit of observation: patent. Grant years: 1990-2014; Robust z-statistics (in absolute value) in parentheses; ***p < 0. 01,**p < 0. 05, *p < 0. 1. The results are expressed as odds ratios (OR). The second part of the table contains the interaction terms. Fwd_cits5, Patent_scope, Claims, Bwd_cits, Npl_cits, and Renewal are interacted with the US dummy (Column 1), Public dummy (Column 2), and Elec_Engin dummy (Column 3), respectively dataset, namely American Public University System, California North State College of Pharmacy, DigiPen Institute of Technology, and National Institute of Technology. As a further robustness check, we performed the analyses on non-profit entities sample only. Tables.
ment. We gratefully acknowledge funding from the  French Agence Nationale de la Recherche  ( NPEIE  Project  ANR-17-CE26-0014-01 , https:// npeie. org) and the  CNRS-CSIC  2018  IRP ALLIES-Associated Laboratory on Linkages between Innovation and Environmental Sustainability  (https:// irp-allies. com/). Laura Ciucci  acknowledges the  University of Corsica  for its financial support.
Standardisation of measured values was undertaken using repeated measurements of international standards NBS-19 and IAEA-CO-1. All results are expressed using delta notation (δ 13 C) relative to the calcite international standard Vienna Pee Dee Belemnite (VPDB). The analytical error based on repeat measurement of an in-house MERCK carbonate standard was better than ±0. 1‰ for Complutense University of Madrid and University of Bradford laboratories. For specimens 14 C measured at ICA, δ 13 C data is not available.
Bayesian mixing modelling The ReSources open access software, developed within the Pandora & IsoMemo initiatives (https://isomemoapp. com), was employed to estimate the carbon contribution from different sources (water DIC, metabolic limestone carbonate, and metabolic organics) towards mollusc shell. A concentration-independent model was undertaken following the same specification described for the FRUITS software package  (Fernandes et al. , 2014). As an isotopic proxy for source carbon contributions, we employed the stable carbon isotope measurements on mollusc shells and carbon sources. As reference source isotopic values, we took the mid-point and range of previously reported δ 13 C ranges for red algae (-27. 2 ± 10. 5‰) and green algae (-13. 6 ± 3. 2‰) collected off the coast of Brittany in France  (Adin and Riera, 2003) , carbonate limestone (1.
Acknowledgments This research was performed as part of the projects HAR 2016-75605-R and HAR 2017-86262-P, funded by the  Spanish Ministry of Economy and Competitiveness, MINECO. During the development of this research AGE was funded by the  Basque Country Postdoctoral Programme  through a postdoctoral grant ( POS_2020_2_0032 ) and  University of La Rioja  (no code available). This study has also been supported by the  Prehistoric Research Consolidated Group of the Basque Country University  ( IT-1223-19 ), funded by the  Basque Country Government. PR and RF are funded by the  Max Planck Society. We thank the  Fishing Activity Service of the Cantabrian Government  for the authorization to collect modern specimens. We also thank the  Max Plank Society ,  University of La Rioja, Basque Country University (UPV/EHU ),  University of Cantabria (UC)  and  Instituto Internacional de Investigaciones Prehistóricas de Cantabria (IIIPC)  for providing support.
Crossing an intersection is a challenge for visually impaired people. While tactile maps can be a medium for appropriating this complex space, they benefit from being complemented by audio information. In this paper we propose a data model to describe an intersection, the paths that allow to cross it, and their accessibility attributes. We also present methods to generate this model automatically from OpenStreetMap, by inferring missing data through graph analysis techniques. Finally, we present an implementation, the evaluation of which confirms the ability of the model to generate a compliant description for intersections with enough data.
, 2016). However, few studies have focused specifically on describing the accessibility of intersections. Moreover, to our knowledge, there is no resource dealing with the automatic generation of description of intersections derived from an existing geographic database such as OpenStreetMap. In this paper, we propose a data model dedicated to the generation of a textual description of intersections, designed to be instantiated from OpenStreetMap data. We will first present a state of the art regarding the accessibility of geographic data to visually impaired people, then we will present the data model we propose and the way we designed it. Finally, we will present an implementation and an evaluation of this model regarding its ability to generate satisfying descriptions.
State of the art A tactile map is a medium that enables visually impaired people to understand how a space is designed. The automatic design of such maps was well explored in the literature, notably through generalization and stylization approaches of existing geographic databases  (Stampach and Mulicková, 2016; Touya et al. , 2019). Other approaches use deep learning on aerial images to mimic the maps as they are made by professionals  (Fillières-Riveau et al. , 2020). However, a tactile map cannot represent dense graphic information nor fine details at a large scale  (Touya et al. , 2019). Thus, it needs to be complemented with other means of representing the information. Sound is then another way of making spatial data accessible to blind users. An approach consists in bounding specific sounds to dominant colours to sound a topographic map  (Josselin et al.
Very few literature focused on intersections exist. Some route description formalisms integrate description of basic crossroad branches  (Gaunet, 2006). Guth et al. (2019)  proposes to integrate elements of the crossroad configuration into a flat database used to describe very succinct general and by crossing descriptions, but these descriptions are presented as data sequences, not full sentences. This aspect is addressed by  Kalsron et al. (2021) , however in both approaches the database is filled manually, not automatically derived from GIS data. We develop these aspects in the work we present here.
Data and method Describing the pedestrian accessibility of a crossroad requires adequate data. Our analysis of the literature in the previous section leads us to consider, in particular, data related to the number of branches of the intersection, the number and type of lanes per branch, and the pedestrian crossings and their accessibility attributes (tactile pavings, traffic signals, sound devices). Several topographic databases, particularly those produced by national mapping agencies, offer a detailed representation of urban space. However, these databases usually focus on the road network and do not offer a satisfactory representation of pedestrian and accessibility infrastructures. Their coverage, moreover, is limited to a given territory, which leads us to consider the use of Open-StreetMap.
Quality of the OpenStreetMap Data The road network occupies an important place within OpenStreetMap, however the presence of data related to pedestrian accessibility has improved considerably in recent years. The representation of sidewalks and pedestrian crossings is permitted by OpenStreetMap, however their presence and precision vary depending on the area studied  (Mobasheri et al. , 2017). If sidewalks are still generally poorly documented, pedestrian crossings are very present  (Favreau and Kalsron, 2022). Moreover, the latter can be combined with attributes indicating the presence of a tactile paving or a sound device (Table  1 ). Another problem with OpenStreetMap data, which is also applicable to other databases, concerns the intrinsic attributes of the intersection. To describe an intersection, professionals refer in particular to the branches that make it up, and this implies being able to delimit the intersection and their branches within a road graph.
Modelling intersections To automatically generate descriptions of intersections, we propose the data model in Figure  2. It actually represents a graph, with edges (Way) and nodes (Junction), to allow its instanciation from OpenStreetMap. Each branch is identified by a number and contains all the ways that are part of it, and a crossing if the branch can be crossed. We also introduced some specific behaviours : • The semantics of OpenStreetMap objects are in the form of key-value pairs. A single node can therefore represent several objects, such as a pedestrian crossing and a traffic light. To reproduce this behaviour within an object model, we used the decorator design pattern  (Gamma et al. , 1994)  which allows us to dynamically define a new behaviour for an existing object. • The different lanes of a way are indicated by attributes on OpenStreetMap 2.
Statistical evaluation The evaluation of the implementation of the model allows us to estimate its capacity to generate satisfactory descriptions with regard to the data available in OpenStreetMap. We chose to carry out the evaluation on three French cities: Lyon (522 969 inhabitants), Clermont-Ferrand (147 865 inhabitants), and Brive-la-Gaillarde (46 330 inhabitants). The quality of the description was assessed according to its conformity with the terrain and the available data. A description defined as correct corresponds to the terrain. A partially correct description indicates that it partly corresponds to the terrain but that the data, the implementation, or the segmentation were insufficient to provide a complete description. An incorrect description does not match the terrain at all.
Descriptions of 20 intersections per city, randomly chosen, were generated, and the 3 parts of each description were evaluated. The evaluation is based on the tool proposed by  Favreau and Kalsron (2022)  images to assess data issues. Pedestrian crossings sound devices were not take into account as aerial images don't enable to check this information. The general description is the one with the best results. These are mainly dependent on the quality of the segmentation (19 out of 20 intersections, i. e. 95% of the errors encountered). Descriptions by branch and by crossing are more sensitive to the quality of the OpenStreetMap data (respectively 26 out of 41 and 30 out of 36 intersections, i. e. 63. 4% and 83. 3% of the errors encountered). The branches were also strongly affected by implementation errors (19 out of 41 intersections, i.
Data and software availability Our implementation is available under the GNU GPLv3 free software license 3. All its dependencies, notably osmnx  (Boeing, 2017)  and crseg  (Favreau and Kalsron, 2022) , are also available under free software licenses. Text generation is done using the jsRealB javascript library  (Molins and Lapalme, 2015) , also available under a free software license. The data used as input is taken as is from the Open-StreetMap database, available under the ODbL open license.