dataesr/scientific-paragraphs-categorization huggingface dataset:
Automatic construction of the execution pipeline To translate the principle scheme of Fig. 1 , pyCIF is not built in a sequential rigid manner. Plugins are interconnected dynamically at the initializing step of pyCIF depending on the chosen set-up (see Sect. 3. 3 for details on the way to configure the CIF). The main strength of such a programming structure is the independence of all objects in pyCIF. They can be implemented separately in a clean manner. The developer only needs to specify what other objects are required to run the one being developed, and pyCIF makes the links to the rest. It avoids unexpected impacts elsewhere in the code when modifying or implementing a feature in the system. In the following, we call this top-down relationship in the code a dependency. For each plugin required in the configuration (primarily the computation mode), pyCIF initializes corresponding objects following simple rules.
Following dependencies detailed in Table  S1 , for every object to initialize, pyCIF will fetch and initialize required plugins and attach them to the original plugin. If the required plugin is explicitly defined in the configuration, pyCIF will fetch this one. In some cases, some plugins can be built on default dependencies, which do not need to be defined explicitly in the configuration file. In that case, the required plugin can be retrieved using default plugin dependencies specified in the code itself and not needed in the configuration. For instance, in the call graph in Fig. 1 , "variational" (inversion) is a "computation mode" object in pyCIF. To execute, it requires a "minimizer" object (CONGRAD, M1QN3, etc. ) that is initialized and attached to it. The minimizer requires a "simulator" object (the cost function) that itself will call functions in the "control vector" object and the "observation operator" object.
Definition of configurations in the CIF In practice, pyCIF is configured using a YAML configuration file (http://yaml. org, last access: 23 August 2021). This file format was primarily chosen for its flexibility and intuitive implementation of hierarchical parameters. In the YAML language, key words are specified with associated values by the user. Indentations indicate sub-levels of parameters, which makes it a consistent tool with the coding language Python. To set up a pyCIF computation, the user needs to define the computation mode and all related requirements in the YAML configuration file. Every plugin has mandatory and optional arguments. The absence of one mandatory argument raises an error at initialization. Optional arguments are replaced by corresponding default values if not specified.
Acknowledgements. The authors thank the reviewers ( Peter Rayner  and an anonymous referee) for their fruitful comments on our manuscript. Financial support. The  Community Inversion Framework  is currently funded by the project (http://verify. lsce. ipsl. fr,last access:  23 August 2021 ), which received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 776810. Review statement. This paper was edited by  Christoph Knote  and reviewed by  Peter Rayner  and one anonymous referee.
Code and data availability. The code files, documentation pages (including installation instructions and tutorials) and demonstration data used in the present paper are registered under the following link https://doi. org/10. 5281/zenodo. 5045730 (Berchet et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-5331-2021-supplement. Geosci. Model Dev. , 14, 5331-5354, 2021 https://doi. org/10. 5194/gmd-14-5331-2021.
OPEN-SOURCE CODE DEVELOPMENT Collaborative open-source software development is unburdened by many of the obstacles and inefficiencies CCs face, outlined in the section above, and can help stimulate R&D by providing a broad range of knowledge and high bandwidth for productivity from a large community of contributors. The potential benefits of open-source developments are well demonstrated by the tremendous progress made today in the field of machine learning, whose community is traditionally linked to an open-source development model  [4]. Similar practices are being adopted in the nuclear field as well. An example of this is the development of OpenMC  [5]  which in just a few years has widely been accepted as a valid alternative to CCs to accelerate research objectives in national labs and academic institutions.
Another example is the development of several OpenFOAM® 1  -based solvers  [6, 7, 8, 9, 10] , which in the last few years have allowed researchers to quickly develop tools for the analysis of advanced reactor systems. Incidentally, many modern libraries for scientific computing are open source, which stimulates, and sometimes enforces by license terms (viz. GNU GPL license), the release of resulting codes as open source. This so-called copy-left then limits the use of several libraries in CCs. In this sense, the choice of a specific licence for open-source code development can have an impact on the transferability of results to the CC community. Simulation codes based on scientific computing libraries feature cutting-edge numerical algorithms, typically allowing for optimal scaling on high-performance computing systems.
For instance, in OpenFOAM, an engineer can program a solver by using high level functionalities for creating and solving equations, separate from concerns about model discretization, mesh management, and matrix operations and manipulations. At the same time, an expert on numerical methods can modify classes associated to discretization and matrix solution regardless of test models. Software organized into sets of building blocks/classes can also reduce the overhead associated with the computational infrastructure required for testing new ideas in the area of methods development. This -along with the possibility to have access, modify and redistribute the code -allows OpenFOAM and the other open-source software to benefit from contributions of the scientific community at large and facilitates contribution based on the developer's, often extensive, expertise in a specific area.
Traditional downsides of open-source collaborative development are limited quality control, fragmented developments, and lack of documentation. However, progress in the IT field helps nowadays mitigating them. Tools like Wiki pages, web-forums and Git repositories are made free-accessible by several web services. These tools help minimizing the need for documentation and of an organized user support; they promote a collective contribution to documentation, tutorials and Verification & Validation (V&V); furthermore, they favor large-scale interactions among users and developers. Distributed version control tools like Git, along with code sharing and collaboration platforms, facilitate practices like code review, issue tracking, and continuous integration testing that are critical to quality control of open source software projects.
OPEN-ACCESS DATA The availability of open-access data is a necessary ingredient for a successful code development based on an open-source collaborative strategy. It allows for a basic quality control by allowing verification of developed codes. Open-access is a clear trend in the scientific domain, with several universities and public bodies openly supporting this strategy  [11, 12]. In the private sector, a frequent practice is that of releasing enough data to stimulate research in a specific field. This is, for instance, the case for machine learning, where large open-access databases have recently been made publicly available by companies like Amazon and Microsoft. This trend towards open-access data is not yet prevalent in the nuclear field due to proliferation concerns and the resulting export restrictions.
PATH FORWARD As a means to initiate collaboration, an expert group has been established that will monitor new developments; establish and update the above-mentioned lists of codes, data and gaps; and develop guidelines for collaborative code development and for R&D in the field. It will also advise the IAEA by recommending benchmarks, training courses, publications and Coordinated Research Projects (CRPs). Workshops and special sessions at conferences will also be fostered. Alongside this effort, educational material will be continually developed and curated to accompany the initiative. Collaboration is being put in place with relevant stakeholders in the field of code and data distribution, and in particular with the OECD Nuclear Energy Agency Data Bank and with the Radiation Safety Information Computational Center at Oak Ridge National Laboratory.
An active interaction will be pursued with national, regional and international educational networks to promote the use of open-source tools as a way to improve understanding and to motivate students and young professionals. Interested institutions are sought to support the creation of open-source code and open-access data, as well as to make use of available open-source codes for future developments. In particular, the involvement of nuclear newcomer countries is encouraged. Collaboration tools are also being put in place, including a devoted IAEA website, a Git repository, a discussion forum, and a Wiki. Three concrete technical projects have been identified as first steps towards the development of a consistent multiphysics platform, as well as to prepare for new collaborations in the field.
One such project regards the coupling of OpenFOAM and OpenMC. This combination will provide cutting-edge multiphysics capabilities by allowing for coupled simulations of Monte Carlo neutron transport with computational fluid dynamics, porous medium treatments, etc. A preliminary external coupling is being developed at the North Carolina State University, while a more advanced coupling is planned in the frame of a collaboration among Argonne National Laboratory, EPFL, North Carolina State University, and University of Cambridge. Two parallel paths forward are being pursued. A first possibility is to make use of the current surface-tracking routine of OpenMC, but allowing for the use of OpenFOAM-type meshes. The second, more computationally efficient option is to extend OpenMC to delta-tracking, and to employ mesh-based spatial partitioning algorithms to identify containing cells for particle collision sites and for data transfer schemes.
this N = 173,426 social science dataset was collected through the collaborative COVIDiSTRESS Global Survey -an open science effort to improve understanding of the human experiences of the 2020 COVID-19 pandemic between 30th March and 30th May, 2020. The dataset allows a cross-cultural study of psychological and behavioural responses to the Coronavirus pandemic and associated government measures like cancellation of public functions and stay at home orders implemented in many countries. The dataset contains demographic background variables as well as measures of Asian Disease Problem, perceived stress (PSS-10), availability of social provisions (SPS-10), trust in various authorities, trust in governmental measures to contain the virus (OECD trust), personality traits (BFF-15), information behaviours, agreement with the level of government intervention, and compliance with preventive measures, along with a rich pool of exploratory variables and written experiences.
The implications of these complex factors to compliance with preventive measures imposed by different governments must be analysed in detail after the crisis. Indeed, the psychological and societal effects are likely to be more pronounced, more widespread, and longer-lasting than the purely somatic effects of the infection  5. To contribute to the understanding of the intersection between pandemic-related physical and behavioural issues, the present document describes a large-scale dataset collected through the collaborative COVIDiSTRESS global survey. The COVIDiSTRESS data collection efforts ran from 30th March to 30th May, 2020 by collaborators from 39 countries and regions with survey forms available in 47 languages and dialects. In total, 173,426 participants were recruited from 179 countries on six continents.
Compliance with medical guidelines has been shown to decrease not just as a result of higher stress levels  19  , but also of minor everyday stressors such as workplace conflict or household responsibilities  20. Prolonged states of emergency and the chronic psychological, social, and economic stressors related to them  21, 22  may decrease compliance with set behavioural objectives during pandemics. Conversely, social support from groups such as one's family, friends, and colleagues moderate the effect of concern for the disease or other sources of stress on one's psychological well-being  23, 24. Hence, as an effort to help health authorities and decision makers organize informed responses, we initiated the COVIDiSTRESS open science collaboration. The dataset can help researchers and stakeholders identify nuances in psychological and behavioural risk factors in the context of the COVID-19 pandemic, and assist governments and other organizations in adopting constructive policies appropriate to each country.
Methods Participants. 173,426 people accessed an online survey link to provide their experiences over a period of 62 days (30th March to 30th May. The stored dataset represents 125,306 people who met inclusion criteria (18 years of age and older and gave informed consent). Demographic characteristics for countries with over 200 responses appear in Table  1. Given the urgent call for COVID-19 research, the survey received a waiver to commence data collection from the IRB office at Aarhus University, Denmark. Participants volunteered based on online and media appeals without monetary compensation; excepting some of the Japanese participants received 7 T-points (equivalent to about 0. 065 USD) from the crowdsourcing service as a reward.
Validated short versions of established measures were used if available in local languages. The full list of variables included in the COVIDiSTRESS global survey as well as the response options participants used to answer the survey are available at https://osf. io/v68t9/. To protect participants' data and avoid sensitive Fig. 1  A world map visualizing the participants in each country. Only countries with n ≥ 200 are coloured. information, participants were not asked about COVID-19 symptoms or other aspects of their medical status. Additionally, no data that would allow identification of participants was collected.
translation. The survey was translated into 47 languages and adapted to the dialects and vernacular of different regions (Afrikaans, Albanian, Arabic, Bangla, Indonesian, Bosnian, Bulgarian, Chinese [Simplified and Traditional], Croatian, Czech, Danish, Dutch [Belgium, Netherlands], English, Spanish [Argentina, Colombia, Cuba, Mexico, Spain], Filipino, Finnish, French, German, Greek, Hebrew, Hindi, Hungarian, isiXhosa, isiZulu, Italian, Japanese, Korean, Lithuanian, Nepali, Persian, Polish, Portuguese [Brazil, Portugal], Romanian, Russian, Slovakian, Serbian, Swedish, Turkish, Urdu, Vietnamese). The translations were completed by a forward translator available online at the Open Science Framework (COVIDiSTRESS global survey  30  ) and in supplementary information. The corrections made were: • Filtered out cases without consent and younger than 18 years old.
• Marital Status: Except for the original English version of the survey, the order of the Dem_maritalstatus variable was mixed up in translations. The variable was recoded to correct this problem. There were some participants who had '5' in Dem_maritalstatus. These responses were recoded as 'Uninformative response'. • Education level and mother's education level: Removed dashes in front of the response options. There were some participants who had '1' in Dem_edu. These responses were recoded as 'Uninformative response'. • Gender: The variable Dem_gender was inverted for languages SSP (Spanish -Spain) and SME (Spanish -Mexico) in the raw data file. Thus, in these responses, Male was recorded to Female and vice versa. • Perceived Loneliness: The scale was initially coded as an extension of the PSS-10 battery.
technical Validation As of 30th May, the participants in our data represented 176 different countries. However, there were instances in which we only had one participant per country (i. e. The Bahamas, Uganda, etc. ). For computational purposes, we decided to examine the data quality for 42 countries that had over 200 participants. Overall, 25 of these 42 countries had more than 1,000 participants. Among these, Finland, France, and Denmark are the three countries with the highest numbers of respondents (over 10,000). At least 62% of the participants provided answers to half of the questions in the survey, and at least 47% responded to 90% of the questions. We added one variable, "answered_all, " that indicates whether a participant answered all questions for users' information. Of all 125,360 participants included in the cleaned dataset, 42.
48% answered all questions. Figure  2  demonstrates the proportion of valid data across 10 countries with the highest number of participants (top 10 countries). The mean age of participants (M = 39. 22, SD = 14. 09) falls between young-to mid-adulthood, and in most countries, the number of female participants is disproportionately higher. Figure  3  illustrates the distribution of gender in the top 10 countries. Similarly, our sample seems to disproportionately represent people with some levels of higher education (i. e. some college or higher). Figure  4   level, and marital status) can be found in Table  1  through Table  4. The dataset also includes answers to questions related to the respondent's current likelihood of infection (e. g. risk of infection with COVID-19 in the family and the degree of isolation), as shown in Tables  5  and 6.
Given our narrow timeline and the convenience sampling method, we acknowledge that our samples may not be representative of the populations of interest. However, we believe that the data can still be meaningfully used to understand the experiences of certain groups of people during this pandemic. Aside from some specific questions on COVID-19 (i. e. self-protective behaviours, trust in the government's agencies, etc. ), our data includes several scales that were previously validated within certain populations, including the Asian Disease Problem, PSS-10, SPS-10, BFF-15 (BFI-S), and the SLON-3. Figure  5  illustrates Cronbach's alphas for these scales in the top 10 countries. In Table  7 , we presented several descriptive statistics of each of the aforementioned continuous scales. Below, we described the preliminary statistics of the scales for all 42 countries.
Acknowledgements The COVIDiSTRESS consortium would like to acknowledge the additional contributions of numerous friends and collaborators in translating and sharing the COVIDiSTRESS survey, even if contributions were small or the person did not wish their name included as a member of the consortium. All funding information is listed in the supplementary material (Figure  S1 ). We also want to address thanks to the  IFB (Institut Français de Bioinformatique , https://www. france-bioinformatique. fr/) for hosting the server Shiny illustrating our results. This research was supported by  JSPS KAKENHI  Grants  JP17H00875 ,  JP18K12015 ,  JP20H04581 ,  JP20K14222 ,  Czech Science Foundation   GC19-09265J ,  Consejo Nacional de Ciencia y Tecnologia (Conacyt) ,  Full National Scholarship -MSc degree  (CVU:  613905 ),  Research Foundation Flanders (FWO) postdoctoral fellowship , and The  HSE University Basic Research Program.
904 3. 067 0. 624 1. 143 5 0. 831 Poland 3088 0. 892 2. 993 0. 729 1. 000 5 0. 894 Portugal 1067 0. 882 2. 886 0. 726 1. 100 5 0. 884 Romania 282 0. 869 2. 668 0. 651 1. 000 4. 6 0. 881 Serbia 266 0. 891 2. 712 0. 664 1. 200 4. 4 0. 849 Slovakia 942 0. 876 2. 680 0. 676 1. 000 4. 7 0. 866 Spain 615 0. 880 2. 638 0. 732 1. 000 5 0. 873 Sweden 3055 0. 908 2. 452 0. 687 1. 000 5 0. 865 Switzerland 1188 0. 918 2. 378 0. 650 1. 000 4. 5 0. 831 Taiwan 2745 0. 882 2. 686 0. 725 1. 000 5 0. 889 Turkey 1199 0. 878 3. 128 0. 684 1. 000 5 0. 883 United Kingdom 1500 0. 878 2. 711 0. 743 1. 000 4. 7 0. 884 United States 2314 0. 913 2. 734 0. 744 1. 000 5 0. 890 In addition to the raw data, a dedicated Web application was developed to provide a general overview of the COVIDiSTRESS dataset (https://covidistress.
Tephra Distribution, Volumes and Masses In order to construct tephra isopach maps, we combined thickness data from our POS 513 cores with published thickness data from more distal Mediterranean cores and from studies of proximal tephra deposits on land. Since the offshore thickness data are sparse, the shape of the distal isopachs can only be roughly estimated, introducing some error into the volume calculations. The case study by  Klawonn et al. (2014)  showed that errors in volume calculations imparted by individuals' choices of contours applied to the same data set by 101 researchers with different education levels are around 8%, and thus relatively small, for well sampled intermediate-distance deposits, but increase with decreasing data density and with both distal and more proximal data sets.
Other Rhyolitic Tephras Cores POS 513-7 and 8 south and southwest of Milos (Figure  1 ) contain up to four rhyolitic marine tephras of yet unknown origin because there are no onshore tephras with the same glass compositions in the reference database but chemical fingerprinting shows compositional affinity with Milos (see Part 1). Core POS 513-8 is much longer (744 cm) than core POS 513-7 (204 cm; Figure  12  in Part 1) and records a nearly constant hemipelagic sedimentation rate in the lower 4 m (Figure  3 ). It is thus well suited to estimate ages of the four tephra layers; their ages range from ∼61 ka to ∼82 ka. Major and trace element glass compositions suggest that the youngest of these layers in core POS 513-8 (507-511 cm bsf, 60. 5 ka) correlates with the oldest tephra at the base of core POS 513-7 (200-204 cm bsf, 50.
, 2004; Wulf et al. , 2018) , in the southwestern Black Sea  (Kwiecien et al. , 2008) , and in western Turkey  (Roeser et al. , 2012). The data points have been considered for the isopachs and volume estimates but lie outside the map area. The Cape Thera eruption (156. 9 ± 2. 3 ka;  Wulf et al. , 2020)  emplaced a basal pumice fall deposit (∼85 cm thick) followed by a localized ignimbrite up to 60 m thick at the caldera walls  (Druitt et al. , 1999). We estimate a minimum of 1 m proximal fall tephra thickness. Together with the 2-to 4-cm-thick marine tephra layer, CA17, and marine thickness data from the southern Aegean and Mediterranean Sea  (Hardiman, 1999; Wulf et al. , 2020) , we estimate the minimum area of the Cape Thera tephra to be 3. 5 × 10 4 km 2 (within the 0. 5 cm isopach). This area corresponds to ∼4.
, 1999; Mellors & Sparks, 1991). The Upper Scoriae 2 tephra covers a minimum area (within the 3 cm isopach) of 1. 1 × 10 4 km 2 , equivalent to ∼5. 8 km 3 erupted tephra volume, not including the proximal flow-deposit volume. This volume equates to ∼3. 6 km 3 DRE magma volume or ∼9. 3 × 10 12 kg magma mass and a magnitude of M ∼ 6. 0 (Figures  5b, 8 , Table  1  and Table  S3  in Data Set S1). The 27. 5 ± 0. 7 ka  (Satow et al. , 2015)  dacitic Cape Tripiti eruption emplaced a 1-m-thick onshore pumice fall deposit mainly exposed on Therasia  (Fabbro et al. , 2013). This fall deposit continues as 1-to 7-cm-thick tephra layer CA5 on the sea floor and was sampled by seven POS 513 cores. It has also been identified in six other cores of the region (Y4 ash;  Druitt et al. , 1999; Hardiman, 1999; Keller et al.