dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants Participants were recruited on the online platform Prolific and compensated with £1 for their 10-minute participation. We preselected users to be residents of the EU, English speakers, aged 18 or above, and with an approval score of 80 or above. The last 3 criteria were included to recreate the conditions of a typical online experiment. We determined the sample size based on the planned analysis of the main dependent variable, study participation. For a Chisquare goodness of fit test with a small to medium effect size of w = 0. 174, power = 0. 95 (df = 1), the g*power software estimated a sample size of N = 584. Although this number includes both complete and abandoned participation, we collected 600 responses to allow for some incomplete cases. However, despite precautions on Prolific, some participants accessed the questionnaire multiple times.
As in the long version, the dimensions are openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism. We furthermore chose the most frequently used social desirability scale  (Crowne & Marlowe, 1960) , and the same three items of the anchoring paradigm as used in  (Klein et al. , 2013)  and careless response items based on  Meade and Craig (2012)  as in study 1. To assess whether participants remembered the data sharing condition, we asked them to answer four comprehension questions on the different parts of the consent, with one item relating to the data sharing policy. As a further modification, we included one open response question: "What are your thoughts on open access to participant data in the scientific field?". All analyses regarding these two items are exploratory.
Procedure The study was presented on Prolific as a personality test. We assigned participants randomly to one of the two consent conditions and either the low or high anchoring condition, for a total of four different groups (data shared/low anchor, data shared/high anchor, confidential data/low anchor, confidential data/high anchor). Provided people agreed to participate, they then proceeded to the first question on the consent form before completing the questionnaires in randomized order. At the end, participants had the option to fill in the open entry and demographic questions, were debriefed and paid.
Exploratory analysis One additional question to ask is whether participants actually remember the content of the consent, specifically, do they remember if their data will be shared? We found that out of 542 participants, 301 correctly remember their sharing condition. Across the two conditions, however, there was a considerable difference: a Chi-square test and corresponding Bayes factor indicate that participants in the non-sharing condition are much more likely to remember this information, χ 2 (1,n = 542) = 58. 43, p <. 001, BF 10 = 1. 99 × 10 12. We repeated all analyses on the reduced dataset, which did not lead to any considerable changes (See supplementary material/osf. io for details). For the exploratory analysis of thoughts on open access to participant data, we pre-processed all written responses provided by the participants and then subjected this corpus to a treatment by hand coding and two different lexicometric analyses (for details on the preparation, please see https://osf.
This was done by identifying the vocabulary that significantly characterizes each class  (Reinert, 1983) , using the R-based software IRamuteq  (Ratinaud, 2009). This classification distributes the statements into different classes which can be distinguished by the contrast of their vocabulary  (Kalampalikis, 2005). The second method is based on cooccurrence of words and makes it possible to highlight the links existing between the different concepts provided by the participants. This analysis is conducted based on the context of a given word and by graphically presenting the existing associations and links between the words in the corpus. The HDC analysis enabled us to extract six stable classes integrating 100% of the elementary context units (ECUs) of the corpus. The dendrogram (Figure  3  On the other hand, participants referenced the importance and centrality of anonymity (classes 1 to 4).
General Discussion The purpose of this project was to provide insight into whether or not disclosing open access to anonymous participant data would influence either participation itself, or the quality of the data obtained in a psychological study. We investigated this question with two different participant samples typically used in psychological research: a first group recruited on campus, and a second group solicited via an online platform. Neither study indicated any difference in participation between the data sharing and non-datasharing consent condition. Although the first study, conducted with a sample of students recruited off-line provided some weak evidence for small differences in data quality, these differences are likely to be due to a relatively small sample size. The second, larger study provides support for this explanation, as there is no evidence for even a small bias due to different data sharing policies.
However, in those cases, the question is much less whether we should fully disclose our policy regarding data sharing, but rather whether we should grant access to the raw data at all. The GDPR does not oblige researchers based in the EU, or conducting studies with EU residents, to disclose all future (researchfocused) uses of anonymous datasets, or even whether or not a dataset not containing personal information will be openly shared. Yet, many of the participants in our study felt that researchers should ask permission to share anonymous data before the beginning of the study. To a certain extent, this desire to be informed is in contrast with the finding that only slightly more than half the participants remembered how their data will be treated. Nonetheless, this leaves two possibilities.
Readers can send change requests, so called "pull requests," via the GitHub platform to contribute. The OSTH is not only supposed to be a living book, but also an Open Educational Resource, which is available under the Creative Commons Public Domain Dedication (CC0 1. 0 Universal). 7 The authors chose this approach to make re-use and adaptation as easy as possible. No permission is necessary to create a copy of the GitHub repository, to re-use content, to copy information or to adapt it to different purposes. Noticing that GitHub might be a barrier to read the Open Science Training Handbook, we created a Gitbook, 8 that offers a more user-friendly approach. Also, a PDF and Word-document were made available via Zenodo 9 for download. The different formats can be accessed through the FOSTER portal.
Lessons Learnt Open Science is a field that changes quickly. FOSTER Plus intended to create a handbook to be kept alive even after the project's lifetime. The first step to ensure this was choosing the book sprint method, in which according to Zennaro et al. (2007) "[t]he initial close physical proximity of the authors allows for much more intense interaction than can be achieved in a purely online environment, and helps to build a team that can persist after the event," meaning that the community of authors developed a sort of ownership and can be in charge of accepting and managing future contributions. The second step was to attach a Creative Commons Public Domain license to the handbook, handing over the book to the community and to the public. The third step towards sustaining longevity and increasing outreach, was to widen the audience by translating the OSTH into different languages.
FOSTER Plus has been approached by several national initiatives, organizations and communities, e. g. the CEPAL library in Chile and the Italian OSSG (Open Science Support Group), which expressed their interest in translating the handbook into different languages. In addition, some FOSTER Plus project partners initiated translations in their national communities. These efforts resulted in the release of a Spanish and a Portuguese translation of the handbook. Further, the translations into Greek, French, Italian and German are in progress and FOSTER encourages translations into more languages. The Open Science Training Handbook GitHub repository 11 is the hub for all translations. Each language has its own sub-repository, from which a separate Gitbook 12 is created. In the following, three coordinators of the translations share their insights into the procedure, provide tips for coordination, explain the tools that were used, etc.
Conclusion and Impact Whilst the national Open Science communities managed to translate the handbook without any difficulties and the successful releases can be interpreted as a strong indication of the need for support in different languages, the project experienced difficulties in fully monitoring how many people used the handbook. The reason for this was a change in the user conditions of one of the services we chose to host the OSTH. However, the FOSTER Plus team moved the OSTH to its own servers and managed to enable proper tracking. The English original version and each of the released translations receive around 2,000 views every two months. These numbers show that there is a great interest from the community in improving Open Science training skills. Liber Quarterly Volume x 2019 11.
What do I do? I started to develop workshops in order to facilitate learning about research methods among the students, especially those who are doing their master's degree, and providing them with the tools to do so. At the same time, I initiated advocacy work across the school, aiming to win the researchers over to the importance of open access databases, such as our institutional repository. At present, at the beginning of each academic cycle, I go to a class of each course to deliver a training session about databases and open access content. The outcome is positive and, lately, the teachers started asking for it. Among the researchers and teachers I hold a session once a year promoting Open Science, highlighting its benefits for all. My perception is that they are aware of the benefits and open minded to the practice of sharing information and data, in order to obtain more value and citations.
Resources for (re)use The FOSTER platform 41 serves as an educational Open Science database, where prospective trainers, and other learners interested in the topic of Open Science, can access about 3,300 training materials with an open licence, which are ready for re-use. 42 Figure  3  shows that the materials available on the FOSTER portal are being used by bootcamp participants. The past couple of years, following the bootcamp activities, the trainers directory 43 was populated with 68 Open Science trainers from all over Europe, broadening the FOSTER training expertise in the Open Science subtopics, but also in the number of languages used by the presenters and the target The platform supports an extensive calendar of events across Europe, and serves as an excellent example and source for inspiration to those planning events in the related fields, being the main point of access among those interested in Open Science related events.
Learning Management System Considering the know-how of the consortium and other experiences in online training, the project team concluded that the LMS Moodle 44 fits the specific requirements of FOSTER's training strategy. Moodle provides an open-source software with a strong community, which ensures long-term sustainability, flexibility and additional plugins needed for the tailored configuration. The LMS is web-based and thus can be accessed from all over the world. With a Liber Quarterly Volume x 2019 default mobile-compatible interface and cross-browser compatibility, content on the Moodle platform is easily accessible and consistent across different web browsers and devices. Some other functionalities have been considered as important such as the multi-language interface, the management of the courses to replicate new instances and the flexibility to allow different types of courses and the possibility of integration with other tools and standards.
A learning path has the benefit of training users on a specific topical domain and allows them to demonstrate and receive recognition for their expertise in the topics of Open Science. In addition, the learning paths allow learners to navigate and discover the rich content of the platform in a more structured way (see Figure  8 ). As a result, five learning paths were created: Open Peer Reviewer, Responsible Data Sharer, Reproducible Research Practitioner, Open Innovation Accelerator and Open Access Author -with each consisting of a set of courses. FOSTER learners can take all the courses of a learning path and after their completion advance their knowledge and even become experts in one or more Open Science fields.
From 2017 to 2019, the platform supported 320 events and hosted 59 webinars and nine e-learning courses (see Figure  9 ). Moreover, the portal is a hub for Open Science trainers to share materials and exchange inspiration and experience. The platform counts more than 20,800 toolkit users since its release in November 2018. In total 744 badges have been awarded (as of July 2019). These numbers are increasing even since the project ended in April 2019. They can be seen as indicators for a high interest and an ongoing need for Open Science training. Our activities are closely linked to the ongoing developments in the field of Open Science. The European Open Science Cloud 52 is aiming to interlink researchers, data and publications, projects and organisations as well as services and training. Furthermore, several discipline-specific research infrastructures such as ELIXIR, 53 CESSDA 54 and DARIAH-EU 55 provide training for their research communities.
Environmental modelling is complex, and models often require the calibration of several parameters that are not directly evaluable from a physical quantity or a field measurement. The R package caRamel has been designed to easily implement a multi-objective optimizer in the R environment to calibrate these parameters. A multiobjective calibration allows to find a compromise between different goals by defining a set of optimal parameters. The algorithm is a hybrid of the Multiobjective Evolutionary Annealing Simplex method (MEAS) and the Nondominated Sorting Genetic Algorithm II (ε-NSGA-II algorithm). The optimizer was initially developed for the calibration of hydrological models but can be used for any environmental model. The main function of the package, caRamel(), requires to define a multi-objective calibration function as well as bounds on the variation of the underlying parameters to optimize.
The caRamel R package The caRamel package has been designed as an optimization tool for any environmental model, provided that it is possible to evaluate the objective functions in R. The main function, caRamel, is called with these syntax: caRamel (nobj, nvar, minmax, bounds, func, popsize, archsize, maxrun, prec). Arguments are detailed in Table  1. The main argument of caRamel is the objective function that has to be defined by the user. This enables flexibility as the user gives all the necessary information: the number and the definition of all the objectives, the minimization or maximization goal for each objective function, the number of parameters to calibrate and their bounds, and other numerical parameters such as the maximum number of simulations allowed.
To quantify these aspects, we selected 3 different metrics:  Generational Distance (GD) which is a distance based accuracy performance index  (Van Veldhuizen, 1999) ,  Generalized Spread (GS) evaluates the diversity of the set  (Zhou et al. , 2006) ,  Hypervolume (HV) which is a volume based index that takes into account accuracy, diversity and cardinality (Zitzler and Thiele, 1999). In addition, caRamel results are compared with results from another multi-objective optimizer available in the R environment: the nsga2() function of the R-package "Multiple Criteria Optimization" (MCO,  Mersmann et al. , 2014). This function is an implementation of NSGA-II algorithm. The arguments are the function to minimize, the input and output dimensions, the parameters bounds, the number of generations, the size of the population and the values for crossover and mutation probability and distribution index.
Kursawe test function The R script to run the Kursawe function optimization is available in Appendix A, or as a vignette in caRamel package. Figure  3  shows the results of 40 Kursawe test function optimizations. In the example of Pareto fronts (Fig  3a ), the shape of the final front is already reached after 1,000 model evaluations. GD and HV evolutions shows that CaRamel is converging 10 quite rapidly for the accuracy, after about 1,000 model evaluations (Fig. 3b-c ). The convergence for diversity (GS) is reached after 5,000 evaluations (Fig. 3d ). Comparison with MCO (NSGA-II only) shows that the use of MEAS makes the optimization process converge more rapidly but with a lower diversity (Fig. 3d ).
Conclusion The R package caRamel has been designed to easily implement a multi-objective optimizer in the R environment. The algorithm is a hybrid of the Multiobjective Evolutionary Annealing Simplex (MEAS) algorithm  (Efstratiadis and Koutsoyiannis, 2005)  by using the directional search method based on the simplexes of the objective space and the ε-NGSA-II algorithm with the method of classification of the parameters vectors archiving management by ε-dominance  (Reed and Devireddy, 2004). The main function of the package, caRamel(), requires a multiobjective function to be defined in a R script and bounds on the parameters involved in the calibration. In return, result of caRamel is a family of parameters sets that are Pareto optimal regarding the different objectives. Two examples of optimization have been explained: Kursawe test function and a hydrological implementation.
While comparing the results with nsga2 function from MCO R package  (Mersmann et al. , 2014) , it appears that both optimizers give similar results after 15,000 model evaluations. However, caRamel is converging more rapidly and has a stable and more reproducible solution. The optimizer was initially developed for the calibration of hydrological models, but it can be used for any environmental model, provided the model computation time is short enough to be run about 5,000 times for the calibration. , vol. 3, no. 4, p. p. 257-271, 1999. Zitzler, E. , Deb, K. , and Thiele, L. : Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary  Computation, vol. 8, no. 2, p. p. 173-195, 2000. Zhou, A. , Jin, Y. , Zhang, Q. , Sendhoff, B. , and.
Code availability The data analysis was performed with the open-source environment R (https://www. r-project. org/). The algorithm is provided as R package "caRamel", which is available from GitHub at https://github. com/fzao/caRamel, or from CRAN: https://cran. r-project. org/package=caRamel. https://doi. org/10. 5194/hess-2019-259 Preprint. Discussion started: 5 July 2019 c Author(s) 2019. CC BY 4. 0 License.
In the present paper, we analyze two NBI heated JET discharges presented in  [4]  at the radial position ρ t =0. 6 (square root of normalized toroidal flux), one of them an L-mode discharge and the other one an H-mode discharge. The density peaking in the absence of sources is determined by finding the density gradient corresponding to zero particle flux, known as the peaking factor. The analysis is performed using an extended version of the Weiland drift wave model (EDWM)  [12] , the gyro-fluid model TGLF  [13]  and linear and nonlinear gyrokinetic simulations using the GENE code  [14]. The focus is on interpretative comparison between transport models and the role of the NBI particle source by including a high energy (fast) ion species in the modeling. In particular, the dependence of the peaking factor on the fast ion density, temperature and their gradients is investigated and the predicted peaking is compared with experimental results.
Gyrokinetic model The gyrokinetic simulations in this study were performed with GENE  [14] , which solves the nonlinear gyrokinetic Vlasov equations together with Maxwell's equations in order to find the distribution functions of the species, the electrostatic potential and the parallel components of the magnetic vector potential and magnetic field. It allows for an arbitrary number of kinetic species as well as collisions and electromagnetic effects. The collisions are modeled using a linearized Landau-Boltzmann collision operator. Magnetic fluctuations, A P , were included in all simulations while the low beta justifies to neglect the B P fluctuations (see table  1  for the specific beta values used in the simulation). For the magnetic background field either a numerical equilibrium from the EFIT code  [20]  or an analytic circular model are used.
Conclusions In the present paper we studied the effect of fast ions on particle transport due to ITG/TE mode turbulence using the fluid model EDWM, the gyro-fluid model TGLF as well as linear and nonlinear simulations using the gyrokinetic code GENE. The analysis was performed with experimental data from two NBI heated JET discharges, one L-mode and one H-mode at the radial position ρ t =0. 6. The zero flux density gradient (peaking factor) was determined by performing scans in the electron (or ion) density gradient. In the scan the ion (or electron) density gradient was adapted in order to preserve quasi-neutrality while keeping the fast ion density gradient fixed. In EDWM and linear GENE we considered single wavenumbers while in TGLF the ion scale wavenumber spectrum was used in the analysis.
SIGNALS, the Star formation, Ionized Gas, and Nebular Abundances Legacy Survey, is a large observing program designed to investigate massive star formation and H II regions in a sample of local extended galaxies. The program will use the imaging Fourier transform spectrograph SITELLE at the Canada-France-Hawaii Telescope. Over 355 hours (54. 7 nights) have been allocated beginning in fall 2018 for eight consecutive semesters. Once completed, SIGNALS will provide a statistically reliable laboratory to investigate massive star formation, including over 50 000 resolved H II regions : the largest, most complete, and homogeneous database of spectroscopically and spatially resolved extragalactic H II regions ever assembled. For each field observed, three datacubes covering the spectral bands of the filters SN1 (363 -386 nm), SN2 (482 -513 nm), and SN3 (647 -685 nm) are gathered.
Strategy and Filter Configurations SITELLE was designed to be optimized for the study of emission-line objects. A lot of technical challenges were overcome while building the instrument to assure excellent sensitivity, a high spectral resolution, and a large FOV  (Drissen et al. 2019). SIGNALS' observing strategy is built upon SITELLE's unique capabilities. Three filters are used to optimize the detection and characterization of the strong diagnostic lines for nearby extragalactic nebulae: SN1 (363 -386 nm) with the [O II]λ3727 and the Bal-  These filters also provide a sampling of the continuum. Figure  1  shows an example of the deep images obtained combining the data from the three SIGNALS' filters using two different methods for M33. This dataset was acquired for the science verification of the SIGNALS project and includes four fields.