dataesr/scientific-paragraphs-categorization huggingface dataset:
For previously published datasets, the original DOI is provided in the source metadata (Sect. 3. 2) to ensure traceability and should be used when the survey is used individually. For universal accessibility, the data are downloadable through a standard HTTPS protocol where no login account is required. We used the web-based RAMADDA (Repository for Archiving and MAnaging Diverse DAta; https:// geodesystems. com/, last access: 5 May 2023) data repository system, which is an open-source content and data management platform and which works following a simple folder structure, with datasets organized by data-provider name to replicate the structure of the data portal. To enhance interoperability and reusability, we published the underlying data using a specific CSV format, with detailed and standardized variable names coming from FAIR vocabularies (see Table  4  and Sect.
3. 2). To be re-usable, the data are released under Creative Commons license CC-BY (https://creativecommons. org/licenses/by/4. 0/, last access: 1 March 2023, Creative Commons -Attribution 4. 0 International -CC BY 4. 0, 2023), which allows any user to use the data freely and with flexibility while at the same time ensuring acknowledgment of those involved in the collection and processing of the data. Keywords from the Global Change Master Directory (GCMD, 2023) are used to describe the data in a consistent and comprehensive manner and to increase the interoperability of the datasets. The end goal is to provide all the information necessary for effec-tive, long-term data re-use. In addition, we developed interactive, open-source Jupyter Notebook tutorials written in Python to interact with the data programatically.
Codes to convert the standardized CSV files to the point and line files as described in Sect. 3. 3 are provided for example. These resources are archived in the BAS GitHub repository and provided via an interactive web interface using Jupyter Book (https://antarctica. github. io/PDC_GeophysicsBook, last access: 1 March 2023, UK Polar Data Centre, 2023a). In addition, a specific Python package allows one to read and plot the specific CSV-formatted data (https://github. com/ paul-breen/xcsv, last access: 5 May 2023,  Breen, 2023). We believe these to be beneficial for assisting users in accessing the data and reproducing their own gridded products independently of the Bedmap project.
The SCAR Bedmap Data Portal The newly developed SCAR Bedmap Data Portal (https: //bedmap. scar. org, last access: 1 March 2023) provides a common endpoint for interacting with the Bedmap source data and products. The web-map architecture is based upon the well-used SCAR Antarctic Digital Database web map (https://add. scar. org, last access: 1 March 2023). The data portal is divided into five layer menus: "Base layers", "Topographic information", "BEDMAP1", "BEDMAP2", and "BEDMAP3". The first menu contains the gridded products: ice bed, surface, and thickness grids for Antarctica currently contain Bedmap2 grids; these will be updated with new Bedmap3 grids as they become available. The second menu contains general topographic information such as coastline and ice-land surface contours taken from the Antarctic Digital Database  (Gerrish et al.
Code availability The user guide for the data portal and the Jupyter Notebook tutorials designed for reading the standardized CSV ice bed, elevation, and thickness data or creating the shapefiles are accessible on the Jupyter Book interface in the BEDMAP3 section (https://antarctica. github. io/PDC_GeophysicsBook, last access: 1 March 2023, UK Polar Data Centre, 2023a) or via the BAS GitHub repository (https://github. com/antarctica/ PDC_GeophysicsBook, last access: 1 March 2023; DOI: https://doi. org/10. 5281/zenodo. 7821671,  Fremand, 2023). A specific library called xcsv (https://github. com/paul-breen/ xcsv, last access: 1 March 2023, Breen, 2023, last access: 1 March 2023) allows one to read and plot data in the extended CSV format as described in Section 3. 2.
Conclusion We have presented here the release of the source survey data on ice thickness, bed elevation, and surface elevation data used in Bedmap gridded products, including the upcoming Bedmap3. Altogether, this data release represents over 82 million data points collected as part of 277 campaigns since the 1950s. In addition to the previous Bedmap1 and Bedmap2 datasets, here we have gathered new ice thickness data from 84 surveys, adding 50 million additional data points to those previous compilations. We have developed a standardized CSV format in order to ensure interoperability between the different datasets, which we have checked following a specific quality-control procedure and summarized on a 500 m × 500 m grid to provide key statistics at the scale needed for the Bedmap3 gridded products.
These standardized, freely available, and previously unpublished datasets will lead to improved assessment of fundamental properties of the Antarctic Ice Sheet and predictions of its future contribution to sea level rise, increasing the value of these important data. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-15-2695-2023-supplement. Author contributions. ACF, PF, and JAB co-led this data release. ACF and JAB standardized the data. The Jupyter Notebook was developed by ACF. PF and HDP initiated the collaboration and PF liaised with all the data providers. ACF wrote the initial manuscript with input from PF, JAB, and HDP. PF designed and populated the web map. EF helped with the design of the web map. AA, BS, OE, PF, JG, JL, KM, MM, FP, SVP, HDP, ACF, JAB, JR, DMS, MJS, DS, KT, XC, and DAY were all members of the Bedmap3 SCAR Core Group and contributed to the overall project and standardization criteria.
Acknowledgements. We would like to thank the  Bedmap3 SCAR Action Group  for their role in the coordination and guidance in the project. We would like to dedicate this paper to the many scientists who have collected geophysical field data in harsh and extreme conditions over the Antarctic ice sheets over the last 60 years. Their commitment, dedication, and drive have populated this dataset and have greatly advanced polar science. Funding for the  British Antarctic Survey  staff came from  Natural Environment Research Council core  funds. Funding for the data collection came from many grants, institutions, and projects. These are individually cited where appropriate in the metadata of the datasets. Financial support. This research has been supported by the  Natural Environment Research Council  (grant no.
BEDMAP3: -https://doi. org/jg6n (Fremand et al. , 2022b); -https://doi. org/jg8b (Fremand et al. , 2022c). The data can be downloaded directly from the RA-MADDA interface by clicking on the "GET DATA" link from the metadata page or using wget commands as documented in the following instructions: https://antarctica. github. io/PDC_GeophysicsBook/ BEDMAP/Downloading_the_Bedmap_data. html (last access: 1 March 2023, UK Polar Data Centre, 2023b). When using these data, please also cite the DOI citation provided in the source CSV metadata if this exists.
org/about/formats. php, last access: 1 March 2023, Canada World Ozone and Ultraviolet Radiation Data Centre, 2023). The format used for the Bedmap Data Portal follows most of the geoCSV recommendations, and headers are compliant with the Climate and Forecast (CF) Bedmap version Cumulative Cumulative Cumulative dataset campaigns data points volume Bedmap1 127 2 million 213 MB Bedmap2 193 29 million 7. 2 GB Bedmap3 277 82 million 22. 8 GB upon a template agreed to by the SCAR Action Group and converted to a specific CSV format. Open and easy to use, this format has been widely used in the scientific community and is well suited for storing tabular data. As an ASCII-delimited file, the CSV format allows long-term preservation of the data thanks to its very simple structure. This format is often recommended (e.
In so doing, we also follow  Mertens et al. (2020) , where a survey-based study over a large number of respondents confirms that media exposure and online searches are good predictors of the increasing fear of coronavirus (in this, see also the review paper by  Garfin et al. 2020). Additionally, in  Hisada et al. (2020) , the authors have underlined the relevance of online searches in predicting emerging COVID-19 clusters of infections. In detail, we collect and compare two datasets over the same reference period, from January 6, 2020, to June 19, 2020. On one side, we consider the daily Google Trends data. Specifically, we examine the search volumes of the word "coronavirus" along with its translations for different countries' most spoken languages. Data retrieved at a country level allow for sounding out similarities and discrepancies in the search for information practised by users in need of awareness.
In our approach, such compulsive searching is intended as a proxy for the anxiety generated by the pandemic. On the other side, we consider the daily levels of the main stock indexes, including companies related to the countries. The source of financial data is Eikon -Datastream (in line with studies such as  Lewis and Bozos 2019, and Chizema 2010). In order to have a reliable and consistent dataset (in terms of countries' features of interest), countries are chosen by using the Human Development Index (HDI) used by the United Nations Development Programme (UNDP) in the Human Development Report Office to rank countries on the basis of their human development. Specifically, we select areas with an HDI index greater than 0. 8, calculated with the 2018 information. The choice of 0. 8 as a threshold is appropriate because all countries with at least that level can be considered as "very high human developed countries" (see, UNDP 2019for additional information on the definition).
It ensures a good enough level of connections between socio-financial entities within the countries. Namely, it guarantees the incorporation of the necessary links between citizens' cognitions of the problems, ability to get informed about them, access to the relevant resources and financial strategists presence (this choice is in line with the findings presented in Chundakkadan and Ravindran 2020about the relevance of the access to online sources to increase the response capacity of a country). Indeed, the starting point of our study is that the data on Google Trends about searches offers a reliable description of how people search for information in a given country. This statement is valid where access to the web is widespread and granted to citizens. Therefore, it is not valid for less developed countries such as those classified as "Low human development" in  UNDP (2019) , where a large portion of the population has poor internet access for causes such as lack of infrastructure, devices or limited digital literacy.
In so doing, we have a translation task to face, as acknowledged by  Fetzer et al. (2021). Nevertheless, the use of one word allows us to obtain intuitive results and is far from being restrictive in our context (we are also in line with  Baig et al. 2020; Goodell and Huynh 2020). Indeed, a preliminary inspection of the Google Trends data shows that the considered word is the most relevant related to the studied pandemic; second, the quoted paper directly derives information about economic anxiety from Google Trends. Differently, we here start from the idea that the anxiety is manifested through the Google searches of the word "coronavirus" (and its translations); in doing so, we differ from  Chundakkadan and Ravindran (2020) , and we are in line with  Bento et al. (2020)  where such a keyword is employed.
Also, the sample is small in their case, which is utterly appropriate for a survey-based study. The rest of the paper is organised as follows. Section 2 discusses some key contributions on the roots of the anxiety for a pandemic and its links with the stock indexes' performance. Section 3 presents the employed dataset by also providing details on the data collection procedure. Section 4 illustrates the indicators used for the study. Section 5 outlines and discusses the analysis results. The last section concludes.
Data 1 We now present the employed data. As we will see in detail below, the considered dataset is associated with Google Trends and the stock indexes at the country level. As a premise, stock indexes data are not always available; moreover, some countries have regions and territories whose inhabitants have limited resources to gather information from the web. In these circumstances, the validity of the Google Trends data for the intended purposes is questionable because the detected volumes of searches may not be representative of the entire population but just of a set of more privileged citizens. To avoid such sources of bias and inconsistency, we focus on a qualified set of countries whose data provide a good description of the situation of their inhabitants. At this aim-and for providing a consistent analysis-we have used the Human Development Index (HDI) adopted by United Nations Development Programme (UNDP)'s Human Development Report Office as the criterion for selecting the countries to be investigated.
Indeed, HDI is a composite index made of factors like life expectancy, education, per capita income indicators, and other relevant factors whose details are recollected in Ul  Haq (1995)  by Mahbub ul Haq, one of the two designers of the index. HDI is used to rank countries on the basis of human development. More specifically, we take all the countries defined as "very high human developed countries", namely those having an HDI index greater than 0. 8. The selection is based on data from 2018, Table  1  of  UNDP (2019). China is added to the considered countries-even if the HDI of China is 0. 75-because of its centrality in the COVID-19 propagation; the first known human infections were in China. Employing Google Translate, the word "coronavirus" is translated from English to the equivalent word in the most used language in each of the considered countries.
In so doing, we obtain the translations reported in Table  1. The translated terms are employed to query the web search indicator from Google Trends. Namely, for each country, one looks for the index of search of the "coronavirus" translations in the language awarding the largest number of speakers. The period investigated captures the first wave of the pandemic; it goes from January 6, 2020, to June 19, 2020. At the end of this process, one gets a time series matrix regarding 63 countries. In our analysis, we are interested in examining the Google Trend search indicator   1  and Fig. 1  for an idea of the main trends in the data. The most noticeable point is the high volume of searches around mid-March 2020. We associate at least one stock index with each country on the abovementioned list.
Per each index, the closing prices are downloaded from Thomson Reuters Datastream. The period is the same adopted for collecting the Google Trends data (see Table  2  and Fig. 2 ) so that one has the same amount of data. Andorra, Bahamas, Barbados, Belarus, Brunei, Liechtenstein, Palau, Seychelles and Uruguay do not have a stock market index of reference in our data source, so we exclude them because we need to have data points for both the variables under consideration. The final list of considered countries contains 54 elements. Furthermore, we align the Google Trends and financial data so that the volume of web searches can be used in the analysis for each day in which prices are recorded. Indeed, as we will see, the indicators proposed in the paper are grounded on the joint observation of the Google Trends data and the stock indexes' performance.
Therefore, it is necessary to work on these two quantities considering only the days when Google Trends data and the stock indexes' performance are registered and can be jointly elaborated. More than this, a biased and incomplete analysis would be the outcome of the study of only one of such factors. Hence, reducing the Google Trends data for having a shared time frame with the stock indexes' performance ones lets the study be free of biases and suitable to pursue the intended aim. In this respect, we notice that Google searches data are available daily. In contrast, financial data are available only when the financial markets are open, i. e. during trading days, typically not on weekends or other special dates. This explains why the Google Trends data are reduced. As a reference for the number of observations, one can look at column "N.
Time t represents the day with the maximum level of searches over the period [1, T] and depends on j. Also, such dependence will be conveniently omitted. The minimum value of the elements of w j is not necessarily null. Indeed, null search means the absence of interest for the considered word in the country ji. e. null amount of Google searches; such an occurrence does not necessarily appear over the period  [1, T]. Assigning value 100 to the highest daily magnitude of Google searches over [1, T] and null value to null searches allows a easy normalisationimplemented directly by the Google Trends proprietary algorithm-of the Google search data in the range [0, 100]. For facilitate comparisons, we impose the variation range [0, 100] also to the series p j k for each j and k through a simple normalisation procedure.
We denote the normalised series of the prices by pj k. First, we identify t ∈ {1, … , T} such that p j k (t) = max{p j k (t) ∶ t = 1, … , T}. Then, we set pj k (t) = 100. Null price is associated with zero value for the normalized series, so that we set pj k (t) = 0 when p j k (t) = 0. Evidently, one can have p j k (t) > 0 for each t = 1, … , T , so that one has pj k (t) > 0 for each t. The entire series can be derived as follows where [•] is the integer part of the real number •. The analysis and comparison of the normalised financial data and Google Trends index is performed at the country level. It is implemented by conceptualising suitable indicators that provide several insights into countries' regularities and discrepancies as presented in the next sections.
The proposed indicators allow for capturing the changes in moods over timefor the case of the A j presented in Eq. (  2 ) and (3)-and also facilitate classifi- cation of countries under a more global perspective on the overall considered period-see H j presented in Eq. (  6 ) and (  7 ) and R j in (8) and (9). Moreover, A j accounts for the values of Google searches and prices, while H j and R j compare the daily increments/decrements of such quantities. To make research on a reasonably homogeneous setting and for a fair treatment of the considered dataset, we have taken into consideration only "very high human   3 ), at country level. Also in this case, indents represent the differences in the starting date of the Google Trends data at country level developed countries"-i. e. those with an HDI greater than 0.
Version 2. 0. 0 also brings a more robust library base, with structural changes that enhance numerical simulation. Furthermore, the library brings new models, while already-present models have been revisited for cleaning and updating implementations, and fixing bugs. These new modifications are discussed in Section 2. In addition, a tool that creates a bridge between the OpenIPSL and other power system simulation tools has been developed. In this new library version, a Python tool has been created to automate processes needed to provide starting guess values used by Modelica tools in the initialization of power system's dynamic models. This new tool and its valuable usage to the simulation of power system models assembled with OpenIPSL are briefly described in Section 3.
Licensing, facilitating contributions and documentation The previous Mozilla Public License 2. 0 was replaced by entity and individual versions of the BSD 3-clause license. This modification facilitates features developed in different collaborations to be securely added to the library. In addition, to facilitate community contributions, the library now has an automated procedure for verification and storage of signatures on contributor agreements, allowing transparency in library development with multiple contributors. This has been implemented using ''Contributor License Agreement assistant'' (github. com/cla-assistant), and is automated through GitHub services. The GitHub repository now has more succinct documentation that directly informs users about the library itself. A code of conduct together with contribution guidelines have been added to enable better interactions between users and the multiple developers.
Stable base version The library now uses Standard International (S. I. ) and perunit units in quantities, allowing a better compliance with the Modelica language, and facilitating the development of consistent models. It also enables the Types feature to be used throughout the library, allowing the scaling of differential equations  [3]. Hence, it culminates previous efforts to date in a robust and stable base version that is compatible with multiple Modelica-compliant tools, such as Dymola ® , OpenModelica ® , Modelon Impact ® and SystemModeler ®. Fig. 1  depicts simulation results obtained using different Modelica-compliant tools for the same OpenIPSL model. A software-to-software validation procedure has been applied to many models in the library, allowing bugs and inefficient implementations to be identified and corrected.
Models that underwent this process have a been graphically annotated using a green line around their icons, indicating the verified component model to the user. The validation results are now available publicly for use of third parties, see: https://alsetlab. github. io/ NYPAModelTransformation/reports. In addition, new models have been also added to the library. Examples of such models are the ULTC (Under-Load Tap-Changing) transformer, the GGOV1DU a variant of the general purpose turbine-governor models GGOV1 and the SoftPMU which may allow users to develop and test applications that need estimated bus frequency values for control design  [4].
Examples and tests Originally a set of Application Examples that illustrated how the library can be used to model both common test models and real-world grids was located outside the library itself as an independent package, which created confusion for new users. Hence, this has been updated and it is now available within OpenIPSL's main package under Examples. As a consequence, the set of examples is now maintained together with the main library, resulting in more efficient continuous development. Furthermore, the library's previous version had a sub-package called Examples that was renamed in this new version to be Tests, because it consists of simulation models that are used for unit testing as the one used for software-to-software validation.
Power flow to records Since its first version, the OpenIPSL has focused on dynamic simulation and its models are initialized with results from power flow computations. The library, however, does not provide the means for power flow calculations to be performed natively within a Modelica-tool. As a matter of fact, this was done in previous versions of OpenIPSL by using tedious procedures involving calculations in third-party proprietary tools and manually inputting data into models for simulation to be carried out from a particular operation point. This, of course, was a great caveat of the library that needed to be addressed. To overcome this issue, a new tool has been developed in Python  [5]. The tool is able to carry power flow calculations using GridCal (), an open-source Python library.
In fact, version 2. 0. 0 can be seen as a more stable basis from which the OpenIPSL development can be carried on without significant structural changes. Moreover, a complimentary Python tool, that sets up power flow solutions and automatically populates Modelica records to be used in model initialization has been developed. It greatly benefits OpenIPSL models by bridging a gap between Modelica-based tools that are used mainly for dynamic analysis with those that are used for steady-state analysis.