dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Transformation As an integral part of the data integration pipeline, some data sets may require some transformations before they are inserted into the database for storage. This step is performed after the data collection and before the data is available in the PostgreSQL database. Examples of such transformations include the simple normalization of dates to a consistent standard or the addition of International Organization for Standardization (ISO) geocodes to spatial data sets to facilitate the generation of maps. As long as transformations can be implemented in Python or R scripts, endless transformations can be applied to the data sets. These transformations are designed to preserve the semantics of the data or their scope without altering them. Instead, they focus on enriching the data or making them consistent with data representation standards.
As data collection and transformation scripts document the source of the original data and the transformations applied, all transformations are clear to users, who can modify them for personal use. The full source code for the data download and transformation processes is available in our GitHub repositories. After the data have undergone the initial transformations and are stored in the platform, they can continue to undergo transformations through the creation of user-defined views of the database tables. Such views, which transform data before a visualization is created and do not alter the original table they pull data from, are created as structured query language (SQL) code through the web interface. These views can be shared among users and make each additional transformation completely transparent on the platform.
Data Exploration and Visualization A web interface for interactive querying and visualization of the platform's data sets is available  [25]  to help users without programming skills create visualizations from data queries. The results from the queries can then be published, shared, or integrated into live dashboards that are updated whenever the underlying data change. This interface is based on the open-source visual analytics platform Apache Superset. Figure  5  shows a dashboard created on the platform. The sharing and publishing of the visualizations built inside the platform can be done in multiple ways. All charts, SQL queries, and dashboards created and saved by a user are given a permanent URL that can be shared with users outside the platform. Additionally, dashboards can be flagged as published, allowing all users, as well as anyone not registered on the EGH platform, to view them.
Data Export and Interoperability All the data stored in our platform can be accessed and exported by both technical and nontechnical users. Users without coding skills can use our data-exploration web interface to apply filters to the available data sets and then download or share them as JavaScript Object Notation (JSON) or comma-separated value (CSV) files. Additionally, it is possible to export the data used in each chart (including the charts in the dashboards) as a CSV file. Technically savvier users can use our Python or R libraries to access the data stored in our databases and use them outside the platform for their own projects. Since the results of any query created in the system have a permanent URL attached, the resulting table can be referenced by external analytical environments, such as spreadsheets, R scripts, or Python scripts, or can be loaded into an external database.
Data Analyses Different levels of analyses are possible on the EGH platform. One level is the data cleaning and transformation procedure that is applied between the collection of data and storage in the platform's database. Another level is the analysis performed within the database through the web interface. This can be done either through the graphical interface or by writing arbitrary SQL code that runs on the server. Finally, we have the more advanced, analytical application programming interface, exposed through the EGH Python and R libraries. These software libraries expose a number of analytical methods, as well as data access functions, that help users to create their own analytical applications based on EGH's available data. One example of a data analysis workflow within the EGH platform was the support to the African regional office of the WHO during the first year of the COVID-19 pandemic  [11, 27].
Individual case data from member states were received as national line lists stored as Excel (Microsoft Corp) files. Data-quality checks were performed for each country line list, and data sets were then harmonized to have standardized variables. The following step was the integration of all data sets into the EGH database using our libraries. These data sets contained individual-level information for all countries. All variables related to the geographical localization of the cases (such as residence, place of infection, and place of reporting, among others) were then standardized using ISO 3166-1 alpha-3 codes for country, district, and province levels. These harmonization procedures were then followed by integration with data provided by the Global Administrative Areas (GADM) project  [28]  for all 3 administrative levels.
Population data were obtained via raster files from the Gridded Population of the World (GPW; version 4)  [29] , which provided fine resolution for population counts inside districts and provinces of the analyzed countries. All these data-harmonization steps were followed by the development of weekly and monthly analytical reports, which included exploratory epidemiological analyses. The data sets for this project cannot be shared publicly; nonetheless, the utility of the EGH platform is not reduced, as it has been designed to host both public and restricted data sets, as described above. This allows analysts with the proper permissions to combine both public and private data sets on the same analytical report or dashboard. As the variety of analytical possibilities is quite broad, being defined by user-defined code based on our web-based tools and software libraries, we refer the reader to the documentation listed on our GitHub repositories  [26]  for further details.
Application Hosting The EGH also provides a workflow for quick deployment of web or mobile apps based on the platform. This workflow allows for CI and deployment from a GitHub repository. It currently supports Python-based Streamlit (Snowflake Inc), H20-wave (H20. ai Inc), and Python/R-based Shiny (Posit Inc) applications. Other application frameworks may be supported in the future. Apps are encapsulated within Docker containers hosted on our server. This allows for low latency in accessing hub-hosted data sets. Docker templates are available for app developers to test and deploy their apps locally, without bothering with deployment details. Apps in production are updated via a CD workflow that allows for a rebuild of the containers whenever a new release is created in its GitHub repository.
Discussion A number of health data-aggregation platforms have been developed in recent years, particularly in response to the COVID-19 pandemic. With EGH, we created a platform that, due to the transparency of each step and the minimization of the need for redoing analytical steps already performed by other users, facilitates the steps of data collection, transformation, storage, and visual exploration in a way that enhances the reproducibility of the data analysis tasks. These data management problems are not new. In Brazil, the Infodengue and Infogripe projects have been monitoring arboviruses and influenza, respectively, and making data and epidemiological analyses open to the general public for many years  [30, 31]. Hürliman et al  [32] , in 2011, talked about the need for a global database to monitor neglected tropical diseases, but more than 10 years later, no such database exists.
During the COVID-19 pandemic, a number of new data repositories for disease surveillance were developed. For example, OWID, spearheaded by Oxford University, not only focused on disease surveillance but also played an essential role during the pandemic to guarantee an openly accessible global perspective on the spread of SARS-CoV-2 and its variants  [33]. Additionally, OWID maintains live dashboards for interactive exploration of its data sets. Google, with the construction of a fine-grained collection of COVID-19 data, also entered this arena through its public data sets program  [20]. Google's data sets excel in ease of use and built-in functionality for quick visual explorations of the data. The COVID-19 pandemic has also spurred many countries to make their own COVID-19 data available on the web.
However, we suspect that this attitude will not survive the end of the pandemic. The best indication of this is the fact that the openness observed for COVID-19 data has not yet been extended to other communicable diseases. The need for more widespread transparency regarding data that is of interest to public health is evident. Data transparency should be an integral part of the governmental infrastructure for health data management and should not only occur in response to visible health emergencies. The ultimate users of EGH are, therefore, governmental and nongovernmental data creators and analysts, who should see the EGH platform as a data-agnostic hub for data that is fully automated and hassle-free. We therefore expect the platform to continue to grow by answering new demands from our user base.
The data-integration aspect of EGH goes beyond supporting open data by also facilitating effective public health policies  [34]. Moreover, by being a completely open-source package that can easily be deployed in a country, it fills an important gap as an easy-to-deploy and low-cost solution for health data analysis. Despite the benefits that the EGH platform offers, some limitations exist. The quality of the analyses performed on the platform depends on the quality of the health data on which they are based. Validating the information contained in all the data sets it aggregates is beyond the intrinsic capabilities of EGH. Therefore, to improve the quality of the produced data, we interact with partners to provide them with feedback that can enable continuous improvement in data quality at the source.
Acknowledgments We regret to announce that our highly esteemed colleague,  Denis Hilton , died while this version of our joint paper was being finalized. He was a leading figure in many fields, especially social psychology and behavioral economics, and we benefited enormously from the lively discussions we had with him. He is sorely missed. The results reported in this paper are derived from a project undertaken by the  Sustainable Consumption Institute, University of Manchester , and funded by  Unilever. We are grateful to  Anna Thomas  and  Ilse Gortemaker  ( Unilever ) for their support for this project and comments on earlier versions of this paper. We are also grateful to  Doron Cohen ,  Celine Nauges ,  Grischa Perino ,  Dan Rigby ,  Andrew Stewart ,  Deborah Talmi ,  Kelly Tate ,  Nicolas Treich , and  Ada Wossink  for their useful comments and conversations.
Multiple elements of our results concordantly show that divisive normalization does not provide a good account of subjective value representation in human reinforcement learning. It is nonetheless still possible that this rule provides a good description of human behavior in other value-based decision-making domains. In fact, previous studies claiming evidence for divisive normalization used other tasks involving items whose values have not to be extracted from experience17,19,60. However, it is worth noting that evidence concerning previous reports of divisive normalization in humans have been recently challenged and alternatively accounts, such as range normalization, have not been properly tested in these datasets23,61. It is worth mentioning that range normalization principle has been recently successfully adapted to account for decision-making under risk62.
Acknowledgments SP is supported by the  Institut de Recherche en Santé Publique (IRESP , grant number:  20II138-00 ), and the  Agence National de la Recherche  ( CogFinAgent :  ANR-21-CE23-0002-02 ;  RELATIVE :  ANR-21-CE37-0008-01 ;  RANGE  :  ANR-21-CE28-0024-01 ). The Departement d'études cognitives is supported by the  Agence National de la Recherche  (ANR;  FrontCog   ANR-17-EURE-0017 ). SB acknowledges support from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (Grant agreement No. 948545 ).
One algorithm subject to this bias is Q-learning  [32] , where action values are updated using the maximum value to approximate the maximum expected value. In the two-stage task described above, the free action value is biased upwards due to considering only the best of two possible future actions, while the forced action value remains unbiased since there is only one possible outcome  [33]. Again, the expression of personal control is not itself the ends for these selection-based accounts, and both predict that choice preference should be reduced when terminal rewards associated with the free option are clearly different. Data from prior studies does not arbitrate between competing explanations for choice-seeking. Here, we used behavioral manipulations and computational modelling to explore the factors governing human preference for choice.
General procedure The paradigm was written in Matlab using the Psychophysics Toolbox extensions  [65, 66]. It was presented on a 24 inch screen (1920 x 1080 pixels, aspect ratio 16:9). Subjects seat ~57 cm from the center of the monitor. Our behavioral task design was designed as a value-based decision paradigm. All participants received written and oral instructions. They were told that the goal of the task was to gain the maximum number of rewards (a large green euro). They were informed about the differences between the different trial types and that the extrinsic reward contingencies experienced during the training phases remained identical during the test phases. After instructions, participants received a pre-training session of a dozen trials (pretrain and pre-test phases) in order to familiarize them with the task design and the keys they would have to press.
We selected the model with the maximal Schwarz weight for each subject. In order to verify that we could discriminate different state-action value estimates and how accurately we could estimate parameters, we performed model and parameter recovery analyses on simulated datasets  (Fig D in S1 Text). Note that the β-pessimist is identical to a Q-learner when β = 1. Since the β-pessimistic model includes an extra parameter, it is additionally penalized when it produces the same prediction as the Q-learner (i. e. , when β = 1), and would not be selected unless there was sufficient evidence for weighting the worst possible outcome.
Statistical analyses We performed all analyses using R version 4. 0. 5  [68]  and the following open-source R packages: afex version 1. 2-1  [69] , brms version 2. 18. 0  [70] , emmeans version 1. 8. 4-1  [71] , lme4 version 1. 1-31  [72] , mgcv version 1. 8-41  [73] , tidyverse version 1. 3. 2  [74]. We used generalized linear mixed models (GLMM) to examine differences in choice behavior. When the model did not include relative trial-specific information (e. g. , reward on the previous trial), we aggregated data to the block level. Otherwise, we used choice data at the trial level. We included random effects by subject for all models (random intercepts and random slopes for the variable manipulated in each experiment; maximal expected value, relative expected value, or incoherence for experiments 1, 2, and 3, respectively).
We performed GLMM significance testing using likelihood-ratio tests. We used Wald tests for post-hoc comparisons, and we corrected for multiple comparisons using Tukey's method. We used generalized additive mixed models (GAMM) to examine choice behavior as a function of trial within a block. We obtained smooth estimates of choice behavior using penalized regression splines, with penalization that allowed smooths to be reduced to zero effect  [73]. We included separate smooths by block. We performed GAMM significance testing using approximate Wald-like tests  [75]. We used Bayesian mixed models to analyze reaction time (RT) data because the rightskewed RT data was best fit by a shifted lognormal distribution, which is difficult to fit with standard GLMM software. For the RT analyses, we removed trials with RTs < 0.
and B. L. were supported by the  Agence Nationale de la Recherche  (ANR, https:// anr. fr/) grant  ANR-19-CE37-0014-01  (ANR PRC) and by the  European Commission  (https://ec. europa. eu/,  H2020-MSCA-IF-2018- # 845176  to J. M. ). D. B. was supported by a  Fondation pour la Recherche  Medicale (FRM, https://www. frm. org/) fellowship ( FDM201906008526 ). V. C. was supported by the  ANR  grants  ANR-17-EURE-0017  ( Frontiers in Cognition ),  ANR-10-IDEX-0001-02 PSL  (program ' Investissements d'Avenir' ),  ANR-16-CE37-0012-01  ( ANR JCJ ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
vi. Two applications are used to illustrate the performance of the models and the properties of our optimal rankings. In the following section, the preliminaries necessary for the development of the work are introduced. Section 3 reviews existing models for ordering items from sets and vice versa. The limitation of the existing models is highlighted and different ways to generalize these results are proposed. In Section 4, the new models under study are introduced. In the same section, how to solve each of the models is analyzed, relaxing the bi-level condition and linearizing the non-linear constraints. Section 4 concludes with a generalization of the introduced models. In Section 5, the bilevel models are applied to two different kind of data, ATP data and biomedical data. Some conclusions are discussed in the last section.
Linear order of items from a linear order of sets The Association of Tennis Professionals (ATP) regularly publishes the rank of tennis players for different variables (game performance). We have chosen nine of these variables and we have saved the ranking list for each of them. The ATP dataset can be found in http://www. atpworldtour. com/. We have saved the information in Table  1. Each column shows the ranking for the corresponding variable: in column C1, we have the list of the best tennis players in Career 1st serve points won on all surfaces from all countries: the best in this variable is Pete Sampras, the second Roger Federer and so on. In column C2 we see that best tennis player in C2 is Rafa Nadal, the second Novak Djokovic and so on. In  [17] , the authors present a combinatorial approach on data envelopment analysis to compare the players, an approach that allows obtaining an efficiency score for each player based on their game performance.
Multispectrum fit procedure The frequency of the S(2) transition, ν 0 , is determined from a multi-spectrum fit procedure using the Multi-spectrum Analysis Tool for Spectroscopy (MATS) fitting program developed at NIST  [17]. This procedure is applied to the seven averaged spectra corresponding to the seven pressure values between 2 and 100 Torr. Two different line profiles have been tested: (i) A quadratic speed-dependent Nelkin-Ghatak (qSDNG) profile  [18]  which takes into account the Doppler effect, the collision-induced velocity changes (or Dicke narrowing), quantified by the velocity changing collision rate, ν VC , the collisional broadening and shift and their quadratic speed-dependence. In this profile, the parameter, η, describing the correlation between velocity and internal-states changes in the Hartmann Tran (HT) profile is fixed to zero.
Note a The recommended value is corrected from the recoil and second-order Doppler shifts. The uncertainty includes all the uncertainty sources discussed in the budget error section. In addition, the CIA can be extracted from the baselines obtained with the multi-spectrum fit procedure. For this purpose, the loss rate, 1/cτ, corresponding to the baseline value for the 4916. 860 cm -1 wavenumber, is plotted versus the squared density in amagat 2 (Figure  1 , right panel) and then fitted with a linear function. The retrieved slope (1. 310(4)×10 -6 cm -1 amagat -2 ) at 4916. 86 cm -1 and 297. 43 K is very close (0. 7%) to the (calculated) value of 1. 319×10 -6 cm -1 amagat -2 reported in the HITRAN2020 database  [4]  at 4917. 00 cm -1 and 300 K.
0591(11) cm -1 atm -1 and -0. 0074(11) cm -1 atm -1 , respectively. The position value is very close to the value included in the HITRAN2020 database (with a difference of 7×10 -5 cm -1 ). The pressure broadening and the pressure-shift coefficients of the considered H 2 O line by H 2 are in agreement within the error bars with the values calculated in Refs. [25, 26]  using the Modified Complex Robert-Bonamy (MCRB) formalism ( 0. 0695(139) cm -1 atm -1 and -0. 0082(5) cm -1 atm -1 , respectively). It is also necessary to mention that the R(17)e transition of 12 C 18 O 16 O in the 20012-00001 band (ν 0 =4916. 969519 cm -1 ; S=2. 431×10 -24 cm/molecule)  [4]  is a potential interfering line. To evaluate its potential impact on ν 0 we looked for possible traces of CO 2 in the sounded gas by recording spectra of the stronger P(60) 12 C 16 O 2 line (ν 0 =4918.
8 and 2. 8 atm), respectively. With its 150 kHz final uncertainty, the reported transition frequency is the most accurate determination reported so far in the fundamental band of H 2 , improving by more than two orders of magnitude the accuracy of these previous determinations. This is also two times smaller than the best uncertainty achieved in the fundamental band of H 2 (for the Q(1) transition which is 4. 5 times stronger  [6] ). referenced to an absolute frequency standard. The theoretical values  [1]  were obtained from the H2spectre software  [22]. The red arrows represent the typical uncertainty on the calculated values. The experimental values in the 1-0 band are from Lamperti et al. [6]  and this work (Q(1) and S(2), respectively) and from Fleurbaey et al. [5]  for the 2-0 band (yellow dots), the Lamb dip measurement of the S(0) 2-0 frequency is also indicated (blue open square)  [12].
The measured value of the S(2) transition frequency is 51×10 -6 cm -1 (i. e. 1. 53 MHz) higher than the ab initio value given by the H2spectre software V7. 4  [22]. This difference is 1. 5 times larger than the 1 σ-uncertainty reported in  [22]  and confirms the fact that ab initio values are systematically smaller than experimental ones for H 2 and its isotopologues  [5, 6, 11]. This tendency is illustrated in Figure  3  which compares the H 2 transition frequencies of the 1-0 and 2-0 bands referenced to an absolute frequency standard to the best ab initio values  [22]. Note that a smaller difference of 0. 84 MHz (equivalent to the ab initio error bar) was found by Lamperti and co-workers  [6]  for the Q(1) transition belonging to the same 1-0 band of H 2 and given with a 310 kHz uncertainty.
DATA COLLECTION & ANALYSIS We investigated the criteria set out for promotion to (full) professor that are used by various institutions across the globe. Between May 2016 and July 2021, we drew on the Global Young Academy membership and alumni network to provide documents that describe promotion policies, involving criteria and procedures. This gave us 159 policies set by academic institutions themselves ('institutional policies') as well as 37 policies set by government agencies ('national policies'), from a total of 55 countries. About 60% of these policies are from countries located outside Western Europe and North America, providing a globally diverse perspective, unlike other studies  [5] [6] [7] [8] [9] ; altogether our documents cover countries with a total population of 5. 7 billion.