dataesr/scientific-paragraphs-categorization huggingface dataset:
RenderX The Ludiscape environment allows users to incorporate educational material in various formats (JPG, WAV, MP3, MP4, and PTT) into a unique project that becomes the game engine. The game engine can then be exported in different formats (HTML, JavaScript, CSS, Shareable Content Object Reference Model [SCORM], EXE, and iOS) toward different platforms (PC, Mac, mobile phone, tablet, phablet, LMS, Moodle, and Dokeos). This unique project contains all of the codes required to control the game and all of the databases that feed into the game. It is this game engine that the graphical user interfaces will open when the user launches the app. On the basis of the decision tree (constructed in one of the first steps of the project), the rules (including the various stages) that announce the success are presented to the learner/player, that is, the levels to be completed to finish the game.
All of the data collected by the Equit'Area project from the City of Paris, involving a fine scale of geographical detail, were used in the development of Equit'Game. As such, at different levels in the game, we provided players with information and tools developed by scientists and experts. These resources take various forms: statistical indicators, maps, and results of analyses. To describe the state of health care in Paris, the infant and neonatal mortality rates between 2004 and 2009 have been displayed. Thereafter, the average annual levels of nitrogen dioxide, and particles with a diameter equal to or smaller than 10 µm-as modelled by the agency responsible for monitoring air quality in Greater Paris, AirParif-have been used to visualize the spatial distribution of environmental hazards in Paris (air pollution is only one of the factors used to measure environmental hazards).
High-Level System Diagram Equit'Game is composed of 3 blocks (2 principal components and an interface layer) and includes effective databases in XML and MySQL format. As Figure  3  shows, the structure used allows the Equit'Game administrators to edit the game via the first block, by using an XML database implemented within the Ludiscape software. The game can then be exported as a SCORM. js layer. The SCORM. js interface will be deployed on LMS Moodle or a similar Web-based platform, which operates via the MySQL database and allows players to access Equit'Game. Therefore, access to Equit'Game, regardless of the device used (PC, tablet, and mobile phone) requires connection to the Web-based platform with personal user ID codes. This final block is where players can save their data to the server via the MySQL database.
XSL • FO RenderX define their objective on the basis of the territory under consideration and the existing literature, justifying their choice. To this end, the level was broken down into a series of interrelated steps. Upon first connecting to Equit'Game, Marie (a key character in the game) greets the learners and they are taken to a virtual conference where they see several videos. Players must then take part in a type of board game exercise, which functions as an evaluation test of knowledge, before leaving the conference. They then set off to hunt down the necessary information, taking the train to Paris to gather information on the ground. Back on the campus, they must learn to store these data within a workspace laid out like an office. Various simulations and learning models are available to help learners to construct databases that are understandable and usable for multiple contributors.
Level 2 The goal of the second level was to learn how to summarize information using appropriate statistical tools, selected among the available statistical indicators, tables, and graphs (Multimedia Appendix 2). The level was structured as follows: back on campus, Marie (the game's learning companion; Figure  2 ) guides the learners toward a blue door, which symbolizes the start of the next level-Analyst. Players thus move into a new learning environment, accompanied by blue Marie, and learn about basic statistical methods and the different indicators they can use for territorial diagnosis. Robert is on hand to help learners as they watch 3 tutorials on how to use the statistical software Stata and its main analytical functions. They then get to grips with the software's various functions and statistical features with the help of crosswords, drag and drop exercises, and puzzles.
Level 3 The aim of level 3 was to learn how to present data by using appropriate Geographical Information Systems (GISs), and how to interpret the resulting mapping (Multimedia Appendix 3). To achieve this objective, the level was structured as follows: as in the previous level, Marie guides learners across the virtual campus to a green door that opens onto the Atlas level. In this new learning environment, green Marie (Figure  5 ) accompanies players as they learn about GISs, their use, and their importance for territorial diagnosis. Thereafter, Robert (Figure  5 ) guides players through 5 tutorials focusing on the ArcGIS software and its function in terms of spatial analysis and geomatic and cartographic features. They then familiarize themselves with the use of the various geomatic and spatial mapping functions and features with the help of exercises, including crosswords, drag and drop exercises, and puzzles.
Level 4 The aim of level 4 was to learn how to cross-compare data using appropriate geographical tools and correctly interpret their spatial distribution (Multimedia Appendix 4). To achieve this objective, the level was structured as follows: back on campus, Marie guides the learners toward an orange door, which symbolizes the start of the next level-Cluster. In this new learning environment, orange Marie (Figure  5 ) guides players as they learn the theoretical foundations of clustering techniques, their use, and their importance to establish a territorial diagnosis. Robert (Figure  4 ) then accompanies them as they watch 4 tutorials on the Satscan software (for scan statistics analysis) and its spatial analysis functions. To complete the level, learners must use the various simulation tools at their disposal to analyze the data they collected in level 1, identifying the most vulnerable areas of Paris city and disseminating results of the territorial diagnosis.
Organization and Management of the Serious Game, Equit'Game The schematic representation of the structure of Equit'Game, comprise a main block (dispositif. ludiscape) that contains the game's 4 levels along with external files that contain smaller. ludiscape blocks for each level. The main block contains all of the levels that make up Equit'Game, including all resources, databases, and videos, and the tests and quiz modules. The external files contain the tutorials, videos, and simulation modules, incorporating large databases that have been deliberately separated from the main block to reduce the size of the game, facilitating updates, and speeding up the handling of various game scenarios.
Acknowledgments This study was funded by  Université Sorbonne Paris Cité. The authors would like to thank all the participants involved in the serious game:  Denis BARD ,  Cyrille Harpet ,  Phillippe Quenel , and  Cindy Padilla. We also thank our collaborator Maternal and  Infant Preventive Institute and PMI, Air Quality Monitoring Associations, Airparif , and all Ludiscape's team.
Aims. Our aim is to estimate the intergalactic medium transmission towards UV-selected star-forming galaxies at redshift 4 and above and study the effect of the dust attenuation on these measurements. Methods. The ultra-violet spectrum of high redshift galaxies is a combination of their intrinsic emission and the effect of the Inter-Galactic medium (IGM) absorption along their line of sight. Using data coming from the unprecedented deep spectroscopy from the VANDELS ESO public survey carried out with the VIMOS instrument we compute both the dust extinction and the mean transmission of the IGM as well as its scatter from a set of 281 galaxies at z>3. 87. Because of a degeneracy between the dust content of the galaxy and the IGM, we first estimate the stellar dust extinction parameter E(B-V) and study the result as a function of the dust prescription.
Data and sample selection Our study is based on galaxies from the VANDELS survey. The data sample selection is described in  McLure et al. (2018b)  while the data reduction and redshift measurements and validation are described in  Pentericci et al. (2018). We briefly present an overview of the survey in this section. VANDELS is a public spectroscopic survey carried out with the VIMOS instrument  (Le Fèvre et al. 2003 ) located at the NASMYTH focus of the Unit Telescope 3 Melipal of the Very Large Telescope (VLT). It made use of the medium resolution grism spanning a wavelength window from 4800 to 10000Å with a spectral resolution of R=580. It targeted ∼2100 objects in a wide redshift range (1. 0<z<7). Targets were selected in the two widely observed UDS and CDFS fields covering a total area of 0.
2 deg 2. The primary target selection was performed using the photometric redshift technique. The reduction of the raw data was carried out using the EASYLIFE package  (Garilli et al. 2012 ) and all redshifts were estimated using the EZ software  (Garilli et al. 2010). A redshift flag has been assigned to each redshift measurement. This flag corresponds to the probability of the redshift to be correct. The quality scheme is composed of six values. Flags 2, 3, 4 and 9 (for objects with a single emission line) are the most reliable flags with a probability to be correct of 75%, 95%, 100% and 80%, respectively. A quality flag of 1 indicates a 50% probability of being correct, while a quality flag of 0 indicates that no redshift could be assigned. At the moment of writing, the internal VANDELS database provides 1527 unique sources (with more than 1300 available from the DR2).
It gives access to 1-dimensional and 2-dimenstional spectra. Photometric data are available for each of the VANDELS galaxies from different ground-based or space-based observatory. Both fields are partially covered by optical and infrared photometric observations coming from the CANDELS survey with ACS and WFC3/IR and SPITZER/IRAC instruments  (Galametz et al. 2013; Guo et al. 2013). Ground based data are also available with optical bands from the Subaru/Suprime-Cam instrument  (Furusawa et al. 2008; Cardamone et al. 2010; Furusawa et al. 2016; Sobral et al. 2012) , near infrared bands from the VIR-CAM instrument from the VLT  (Jarvis et al. 2013 ) and near infrared bands from the WIRcam camera of the CFHT  (Hsieh et al. 2012). We refer the reader to  McLure et al. (2018b)  for further details.
The SPARTAN tool To estimate the IGM transmission toward our galaxies we use the SPARTAN tool which is able to fit both photometry and spectroscopic data. In this paper we use the capability of SPARTAN to fit spectroscopic dataset and photometric dataset separately. This single data type fitting follows the same recipe as other codes used in the literature (e. g. Salim et al. 2007 , Thomas et al. 2017b). For a given object and a single template the χ 2 and associated probability are estimated with: χ 2 = N i=1 (F obs,i -A i F syn,i ) 2 σ 2 i ; P = exp - 1 2 (χ 2 -χ 2 min ) (1) where N, F obs,i , F syn,i , σ i ,A i and χ 2 min stand for the number of observed data points, the flux of the data point itself, the synthetic template value at the same wavelength, the observed error associated to F obs,i , the normalization factor applied to the template, and the minimum χ 2 of the library of template, respectively.
For the photometric fitting we performed in this paper this normalization is applied in the i-band. Once this normalization is done, SPARTAN convolves the normalized template with all the photometric bandpasses available for the observed galaxy. Finally, the relations in Eq. 1 are applied to estimate the physical parameters of the observed galaxy and their associated errors. When dealing with spectroscopic data, the general principle of the fitting process is similar. Nevertheless, this type of data allows for a different normalisation method. SPARTAN has to normalize the redshifted template to the observed spectrum. As for the photometry, we can consider a photometric filter and estimate the magnitude in the same filter from the spectrum itself. This magnitude serves as a normalization to all the templates.
This approach, widely used in the literature with photometric dataset, uses normalization that is always done in a given photometric band (e. g. , the i-band). As a result, each galaxy is normalized to the template in a different rest-frame region and all the galaxies are not treated in a similar manner. The spectroscopy opens a new redshift-dependent method of normalization. This method uses an emission line free region available in the spectrum. In the UV spectrum of distant galaxies, a region free from strong spectral line is between 1070 and 1170 Å (rest-frame). When fitting a UV spectrum at z=4. 5, this spectral region will be shifted at 5885-6435Å. SPARTAN will compute a spectrophotometric point in this region directly in the template and in the data using a box filter of the size of this region.
Dust content of z>4 galaxies In  Thomas et al. (2017a)  we identified a potential strong degeneracy between the estimates of the dust content of the galaxy and the IGM transmission prescription. This degeneracy is more prominent at z>4. In other words, the same data can be fitted with high values of both dust extinction and IGM transmission or lower values for both parameters. This is due to the small wavelength range available from UV spectra for fitting that is not able to constrain the dust content of the galaxy. In order to address this problem in the present work, we measure the IGM transmission in a two step process. First, we estimate the dust content of each galaxy in our sample using the photometric data presented in Sect. 2. We fit the SED over a broader wavelength range than the spectra including NIR data, providing robust constraints on dust extinction.
Tr(Lyα) towards z>4 galaxies We measured in the last section the dust content of our galaxies. We now move towards the estimation of the IGM transmission with the spectral fit. In order to estimate it we constrain the spectral fit of our VANDELS spectroscopic data fixing the E(B-V) to the one measured during the fit of the photometric data. We consider individual E(B-V) value for each of our galaxies and do not use the average values presented in Fig. 3. The other parameters, such as age or metallicity are still free to vary and the parameter ranges correspond to the ones of the fit of Sect. 4. Examples of spectral fit of VANDELS galaxies are presented in Fig. 4  at various redshift and they show that SPARTAN reproduces very well the UV continuum of our galaxies at all wavelengths. It is worth mentioning that we can see that the Lyα is poorly reproduced.
We display two stack spectra. In blue we show the average of all the spectra (79) with IGM transmission lower than the mean. The mean redshift is these spectrum ∼4. 36. In red we show the average of all the spectra (101) with IGM transmission higher than the mean, with a mean redshift of ∼4. 60. The stack spectra have been made with the specstack program (Thomas 2019b). match with the broad-band photometry available for the sources. The underlying cause for this is still under investigation but for the moment (and at the time of DR1 and 2) we have implemented an empirically derived correction to the spectra. This effect could in principle be relevant for the objects belonging to the first redshift bin. For this reason we repeated the same measurements in the two first bins using uncorrected spectra and measure an IGM transmission of 0.
These measurements are similar to previous measurements reported in the literature. -Using individual measurements of E(B-V) as constraint, we use the SPARTAN software to perform the spectral fitting of our galaxies. From this fitting we extract the values of Tr(Lyα) at various redshift. It decreases from Tr(Lyα)=0. 53 at z=3. 99 to Tr(Lyα)=0. 28 at z=5. 15. These results match very well the measurements from QSOs study and from theoretical predictions. This reinforces the fact that high redshift galaxies can be used to estimate the IGM. -Even more importantly the 1σ scatter of Tr(Lyα) is large at any redshift. It is higher than 0. 1 and equivalent to the standard deviation reported from QSOs data. -As expected we find that the IGM transmission measurements are sensitive to the choice of dust attenuation prescription.
-We test whether our results are sensitive to the redshift flag system in place in VANDELS and find that the differences are minimal. -Due to a lack of observational constraints, the measurements coming from a pure photometric fitting are not able to reproduce the results from the spectral fitting and from the literature. -Finally, we compute the IGM transmission leaving the dust extinction as a free parameter and we confirm the presence of dust/IGM degeneracy. In that case, the dust extinction goes toward higher value that are in tension with measure from the literature. This is then compensated by an higher IGM transmission. Finally, it is worth reminding that they are multiple IGM models in the literature and they should be tested against real data. High-redshift data samples are getting big enough to make statistically significant tests of these models.
Data description We use for aid commitments Official Development Assistance (ODA) data taken from the OECD's Creditor Reporting System (CRS) dataset. This dataset provides, among other things, time-series data on the official statistics on aid flows to developing countries, provided and validated by the members of the OECD's Development Assistance Committee (DAC). 14 We consider observations on aid flows from the G7 countries (Canada, Germany, France, Italy, Japan, the United Kingdom, and the United States) to 82 recipient countries. 15  We restrict our attention to the G7 donors for two main reasons. G7 countries are the major donors among all DAC donor countries and are usually criticized for weak targeting of aid, selfish aid motives and insufficient coordination  (Berthélemy, 2006). Moreover, there are insufficient observations for other DAC donors and new donors 16 to carry out meaningful data analysis.
Our main variable of interest, Oil R _ , refers to oil reserves held by recipient countries. The hypothesis is that oil-rich recipient countries are more inclined to receive aid from the G7 countries that are oil-importing donors. Compared to proxies of oil dependence, which are usually used in the literature, such as oil exports or oil production, proxies of oil wealth, such as oil reserves, capture the oil abundance and are less likely to fall within reverse causality concerns. Oil reserves are drawn from the dataset compiled by  Cotet and Tsui (2013). 17 The set of other control variables for recipient-related features, represented by the vector X jt in Eq. (  1 ), follows the literature on aid allocation. It encompasses first indicators of beneficiary needs: the level of income measured as per capita Gross Domestic Product (GDP), multilateral aid per capita, a human development index, the Human Assets Index (HAI), combining indicators of health, nutrition and education, and taken from the database developed by the United Nations Department of Economic 12 These additional explanatory variables are of two types: recipient related and other fixed effects (recipient, recipient-time, time).
13 Details of this method are provided in the Appendix C. 14 Validated CRS data are made public by the OECD DAC Secretariat and are freely available on the OECD website: www. oecd. org/dac/stats /idsonline. 15 For the list of countries included in our sample, see Appendix A. 16 A major concern here is the lack of data since the OECD-CRS data on emerging donors' aid flows are incomplete. Indeed, some important non-DAC donors (China, India, Brazil, and the Russian Federation) do not report their aid activities to OECD DAC or do not publish detailed data on their foreign aid activities). China and India, in particular, frame their financial, economic and technical support to other emerging and developing countries as South-South cooperation rather than aid assistance. 17 This dataset uses oil exploration and discovery data from the Association for the Study of Peak Oil (ASPO).
Oil reserves for each country at any particular year are calculated by subtracting cumulative production from cumulative discovery. and Social Affairs (UN-DESA). 18 The conditions imposed on aid are also examined by including proxies of institutional quality that capture merits criteria for aid distribution. Following the aid allocation literature, we include the inflation rate (Inflation) and the quality of governance of recipient countries. The governance variable is measured by the democracy indicator (Democracy), taken from the Democracy and Development Revisited dataset compiled by  Cheibub et al. (2010). As numerous empirical studies found that donors tend to be biased toward countries with small populations  (Isenman, 1976; Dowling and Hiemenz, 1985) , we include population (Pop) to control for the recipient country size.
Acknowledgments This article is part of a larger research project funded by the  Conseil Français de l'Energie  (Research project number  85 ; November 2015-October 2017). We would like to thank the editor and two anonymous reviewers of this Journal, the participants at the 39th international conference of  International Association for Energy Economics (IAEE, Bergen, Norway ) and at the 66th Annual Meeting of the  French Economic Association (AFSE, Nice, France)  for their valuable comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily represent the views of their affiliated institutions.
Subsequently, the cells were rinsed with cold L15 medium and the imaging was started immediately after mounting the sample holder on the microscope stage and warming the cells to 37 • C to synchronize the uptake of the viruses. Data was recorded over 20 min on single cells by acquiring multiple z-stacks spanning the entire cell volume with z-planes spaced 300 nm apart. EGFP and mCherry-labeled virus particles were excited in parallel with 488 nm and 561 nm continuous wave lasers. Figure  A1  depicts the microscope setup used All cell culture reagents were purchased from Thermo Fisher Scientific, Waltham, Massachusetts, USA, if not stated differently.
Data Analysis The analysis was done using the TrIC software (Figure  A3 ) previously developed in our lab  [30]. Briefly, the virus is tracked in 3 dimensions in the eGFP channel. Single viruses are tracked in 2D either manually, automatically using TrackMate  [34]  or using a home-written wavelet tracking method  [35]. The subpixel accurate 3D-trajectory is obtained in a second step by fitting the particle image with a 2D-gaussian function for the x-y position and a 1D-gaussian function for the z position. A box about the particle is taken (2. 94 µm × 2. 94 µm × ~5 µm or 21 pixels × 21 pixels × entire z-stack) and a 3-dimensional image cross-correlation is performed between the eGFP and mCherry channels. When a particle is detected in both channels, a positive correlation signal is observed.
Investigation of Individual Prototype Foamy Virus Env-Mediated Fusion Events To gain detailed insights into the fusion process, we performed live cell measurements using the fluorescently labeled FV particles described above. Cells were incubated with viral particles at ~4 • C. Subsequently, the cells were rinsed with cold L15 medium and the imaging was started immediately after warming the cells to 37 • C to synchronize the uptake of the viruses. Confocal z-stacks were collected over approximately 20 min and the data analyzed as outlined in the Section 2. Figure  2  and Movie S1 show the results of typical fusion of a PFV. Panel a of Figure  2  depicts the differential interference contrast (DIC) image of an infected cell overlaid with the trajectory of the infecting particle in three dimensions (3D).