dataesr/scientific-paragraphs-categorization huggingface dataset:
We also present several extensions to this core idea to further enhance 3D-specific data storytelling. For example, since understanding the spatial context for specific data views is often critically important, the views are represented as 3D frames floating in the same space as the data visualization, enabling the audience to see them from various vintage points. In addition, to increase clarity, we employ a staged animation when the transition between data views would require changes to several visualization parameters, for example, when new data filters or color maps have been applied. In summary, the main contributions of this article are: • the design of a novel visualization framework to facilitate 3D-enabled multi-person correspondence about spatial datasets for team science, • an extension of keyframe-based storytelling metaphors to support co-authorship, treating the created stories as collaborative documents authored asynchronously, • the design of a data story server and communication protocol for using V-Mail across an ecosystem of hardware platforms that seamlessly adapt the fidelity of the experience to the hardware, and • user feedback and lessons learned from a first evaluation of using V-Mail in a real team-science setting where team members utilized all V-Mail client platforms we created (desktop app, mobile app, and native video players on multiple mobile and web-based devices).
In V-Mail's approach to storytelling with spatial 3D data, a 'page' is a view of the 3D dataset that shows a particular situation. Annotations can be provided by sketching onto the 2D projection of the 3D view or by attaching text messages to it. 'Flipping to the next page' animates from one 3D view to the next, allowing viewers to follow along while maintaining spatial context. Several page flips can be connected into 'chapters', but the transition from one chapter's end to the start of the next can be more abrupt (i. e. , without an animated transition). As in the children's picture book, it is possible to animate forward and backward through the data story, with different playback modes to control the time with precision or to quickly skip to a new chapter. With this approach, we break complex messages down into more digestible, visual, and expressive forms (C3-C5).
The V-Mail frameworks requires just three additional concepts to be added to this core "book metaphor" functionality: Floating 3D pages-When presenting visualization results with a set of captured data views, we often think of a panel listing the views; e. g. , thumbnails on the viewer in AniViz  [3]. V-Mail includes such a panel, but in addition to this panel, the data views are also included directly within the spatial visualization of the underlying 3D data. Together with the smooth transitions between the data views achieved with animations, these floating pages assist users in understand the spatial correspondences between views as they progress through a data story (C4). Living documents-To remove the one-way character of existing 3D data correspondence and turn data communication into a collaborative and iterative process accessible to non-experts, V-Mail treats data stories as living documents (C1, C6).
Users may stop the transition to explore the visualization in an intermediate frame, but this exploration is only possible when the set of visualization parameters of two states are the same and the parameters can be changed continuously (e. g. , changing a slicing plane in volume rendering). In cases where there are no obvious intermediate frames (e. g. , changing a visualization type or dataset) we (  3 ) use an imagebased blending approach. This blends two images where the screenshot from the previous data view fades-out and gradually fades-in into that of the next data view (see the second row of Fig. 4 ). When both camera location and state change, we (4) break the transition into two stages. First the camera moves to the next location. Then, the visualization state gradually changes to that of the new data view (see the last row of Fig.
With it we can thus reach completely new audiences with data stories. Even if someone never used a data story before and thus does not have or is not willing to install dedicated software, they can still view them. Depending on the media channel used to share the URL, they can also respond to a story such as by commenting in a separate e-mail. Moreover, this form of consuming also allows users to explore the story on their own first  [24] , before, potentially, adding to it and working collaboratively with other authors. To explore the situation where users are willing to install a dedicated tool such as a dedicated mobile app on their phone or tablet, we developed an Android V-Mail client (Fig. 9 ) to facilitate a higher interaction fidelity for both phones and tablets. On top of playing back the video of the data stories, this app also allows users to use our advanced playback modes, add new annotations (both graphical and textual), and have limited 3D exploration via the pre-computed 360°i mages for each data view.
V-Mail server and general operation The V-Mail server comprises a database and a file storage. The purpose of integrating the database is to check available stories without opening files in the storage. It has a single table; each row represents a V-Mail story, and the columns are: an unique ID of the story, a name given by the user who created it, and last times modified by each client. The file storage contains additional files for V-Mail stories (Table  2 ). Files for each story are saved in separate folders, and names of the folders are the unique IDs listed in the database. When a user opens the desktop or mobile client, the client accesses available stories from the database and shows these as a list (Fig. 8(a) ). When the user clicks a button to open a specific story, files used by the current client ("Use by" column in Table  2 ) are downloaded and loaded.
D. student in cultural geography. During the first few days of the twoweek period, we met individually with the artist and science writer to provide training for the V-Mail clients they chose to use and to assist them in their tasks. We recorded qualitative feedback by taking notes from interviews we conducted after the training sessions as well as via questionnaires before and after the two-week period. In addition, we saved each V-Mail that they created and configured the V-Mail clients to log all edits. After the two weeks, the artist shared the resulting V-Mail in a regularly scheduled, "live" video-conference with the scientists, gathering feedback on whether the story covers important data features and whether it employs appropriate storytelling techniques.
Initial Feedback from a Brain Science Team We integrated V-Mail into a different system that visualizes crossing pathways in white matter areas in a human brain (see Appendix B). We showed the V-Mail during a live video conference to members of a neuroscience research team, who was already familiar with the visualization system. Their initial reaction was somewhat mixed; they told us the tool itself is novel and could be useful in presenting findings to a broader audience, but there is no urgent need for such tool for their team-science collaboration. The team already meets regularly, almost weekly, using a live video conference or in a meeting room, and the team is led by one senior member to whom his students report. They use remote asynchronous communication tools such as e-mails to coordinate the meetings or share datasets.
A major limitation of the current V-Mail implementation is that it can only be integrated into applications built using Unity3D, and a separate instance of the V-Mail plug-in is needed for each system. To ease the integration process, we are considering a more loosely coupled implementation where a separate storyboard application communicates over a network with visualization software. Since the webpage and the mobile app do not rely on other data visualization applications (these clients play a video), we can continue using these clients for showing and interacting with stories created from new connected software. We observed that participants used lightweight clients if the features required for their tasks were available in those clients. We wonder, if the clients supported some lightweight data exploration features, participants might have explored data even though their tasks did not require this.
g. , with spatial annotations and animated transitions, and (2) the app is designed to solely work with shared visualization results. The latter point may be of interest to visualization practitioners as this approach enables them to bypass installation steps and computation requirements often imposed by using exploratory visualization software such as ParaView  [2]. Also this separation suggests an interesting future direction of turning V-Mail into a collaborative framework that unifies well-known software in the field for creating data stories and conducting discussion. In our first V-Mail deployment and evaluation, we took the approach of asking a real science team to integrate V-Mail in whatever ways they found most useful for the tasks at hand during a two-week period. The results were generally positive, re-confirming the urgent need for improved tools for spatial communication in this context, while also suggesting areas of required improvements to advance V-Mail from the prototype stage to a production-level tool.
A. DETAILS FOR THE MAIN SUPPLEMENTARY VIDEO In the main complementary video (00:53-4:23), we demonstrate our tool with a use-case based on the Antactic ice sheet dataset. Here, we describe this use-case and provide references to sections in this paper. In Act #1, which starts at 0:58, a student receives a V-Mail URL token in a message (Section 4. 3. 2). The link takes him to our web client, which shows the story as a video file in a web page (web client in Section 4. 3. 3). The student clicks a button on the web page to open the story in the our custom desktop client (Fig. 8(c) ). In Act #2, which starts at 1:23, the student extends the V-Mail to ask a question to his supervisor. He captures additional data views and adds annotations (Section 4. 2. 1). Animated transitions are automatically constructed by the system (Section 4.
The importance of the three other emission categories (electricity, travels and commutes) depends on the laboratory with the electricity share being the most variable (7-18 %), followed by travels (11-16 %) and commutes (8-12 %). We observe that, whatever the chemistry laboratory studied here, the total emissions are equal or higher to the median emissions calculated from the laboratory database of the GES 1point5. 26  In particular, heating and electricity emissions are 60 to 190 % higher than the GES 1point5 median for the three chemistry laboratories. In the following sections, we detail the analyses of emissions per category, and the associated mitigation strategies.
They can choose up to two types of commuting distances and means of transport representative of a typical working week. The rate of answers was approximately 50 % and the total commute emissions were proportionally extended for each socio-professional class. Purchases emissions are estimated by combining two procedures. For the small fraction of purchases related to office computers, emissions are evaluated per unit using suppliers EFs. For all other purchases, excluding travel tickets, emissions are calculated by multiplying their tax free price by the monetary emission factors of the GES 1point5 EF database  26  (Table  S2 ). This hybrid EF database combines environmentally extended input-output (EEIO) EFs with corrections that either use LCA or supplier prices to estimate monetary EFs for gases, plastic-ware and gloves or carbon intensities from representative companies that sell certain types of goods to research labs (such as Sigma-Aldrich for chemical products, for instance).
The boundaries are "cradle-to-grave" and include the production and transport of acetone, distillation of the solvent (manufacture and use of the distillation/chiller unit), evaporation of acetone during distillation, transport and incineration of the waste solvent. Emission factors for the different elements have been calculated with ecoinvent 3. 9 life cycle inventory database + IPCC life cycle impact assessment method, and directly from GES1point5 for electricity. Inventory associated to the manufacture of the distillation/chiller unit has been built through an estimation of the metal/plastics/electronic content of the machines.
Background As a densely populated city-state and a global travel hub, Singapore's vulnerability to importation of communicable diseases and outbreaks is without a question. The Severe Respiratory Syndrome (SARS) outbreak in 2003 unveiled the weaknesses in the country's responses to combat it, specifically in areas of epidemiological surveillance and a healthcare system for emerging infectious disease  [18, 19]. Before vaccines were available, analyses of information on policy measures from datasets such as the OxCGRT for general trends and patterns in government responses revealed striking commonality in the first months of 2020, where intensive policy responses, particularly containment and health policies, became a global phenomenon  [9, 20]. The peaks and valleys that followed saw countries lifting and reimposing policies in a see-saw manner.
Dataset A predominant search was run through the official webpage of Ministry of Health, Singapore (MOH) and further complemented or cross-checked with review of information available on websites of Singapore government agencies or data repositories such as Singapore's national open data collection (data. gov. sg). Where available and applicable, search filters were applied to specify the relevant types of data that correspond to the period or dates of interest from the sources (Appendix A1) Data regarding epidemiological outcomes was first collected from 'Situation Reports' published on MOH's websites (Appendix A1). This dataset subsequently comprised calculated monthly total new cases and deaths, and monthly average active hospitalisations and ICU cases (Appendix B1). To examine these health-related outcomes with vaccination rates, month-end full and booster vaccination rates were used for analysis since vaccination strategy commenced in December 2020, with full vaccination rate picking up in January 2021 and booster rates in September 2021 (Appendix B2 and B3).
The dataset for COVID-19 policy measures were collated from MOH's dedicated site for 'Past updates on COVID-19 local situation' (Appendix A1). Each announcement or report published within the stipulated month and year was read and analysed for their content relevance as a policy intervention based on signalling keywords such as 'advisory', 'measures', 'guidelines' or the equivalent. For consistency, the period over which information regarding policy measures was published also defined the period over which data for the aforementioned epidemiological outcomes and vaccination rates were collected and analysed. The policy interventions that were identified were subsequently examined for their relevance to the CPTI framework  [15]  and classified accordingly. A policy category and sub-category i.
gov. sg is an initiative by the Ministry of Finance and Graph/charts and tabulated numbers managed by the Government Technology Agency of • Dataset on 'Progress of Covid-19 Vaccination' with docu- Singapore (GovTech). It was launched as the Singapore mentation of different population vaccination status (cu- government's one-stop portal to its publicly available mulative data) datasets from 70 public agencies. Through the active use of charts and articles, it plays a pivotal role in making government data relevant and comprehensible to the public • 'Updates on Singapore's Covid-19 situation': Updates on Singapore's COVID-19 Situation (Past Updates on COVID-19 Local Situation: https://www. moh. gov. sg/ covid-19/past-updates) • Key words used to identify a policy intervention included '(Health) advisory', 'Guidelines/Guidance/Approach', 'measures', 'precautionary' or the equivalent.
General data strategy To be as inclusive as possible, we combine the World Value Survey (WVS) with the European Social Survey (ESS). Both contain identical questions on risk taking, as well as a number of controls that can be matched. We focus on the period between 2005 and 2012, and for each country select one survey round during this period, with priority being given to earlier time periods for countries that recur repeatedly in either the WVS or the ESS. 2  When a country was included both in the WVS and the ESS, we use the WVS data. If a country did not figure in the WVS data, we added the data from the ESS, adding in the order round 3, 4, 2, or 5, depending on availability. These choices were made to obtain measures that fall as much as possible into the early part of the measurement window, to guarantee a temporal lag to the subsequent economic measures, and as close as possible to each other.
The supplementary materials contain stability analyses concerning these choices, and show that they do not substantively impact our measures. That is, changes over time in countries are second-order relatively to the differences we observe between countries. This results in a novel dataset including representative samples for 92 countries and more than 130,000 observations-the largest comparative dataset on risk taking to date. We combine this datasets with entrepreneurship rates, as well as a large number of macro-economic and institutional controls from a variety of sources. Micro-economic controls are furthermore compiled from the WVS and ESS databases themselves. Entrepreneurship rates are measured at the country level, and are taken from subsequent waves of the same survey wherever possible, i.
e. they generally stem from surveys run after 2012, the last year from which we use preference data. We include some data from the years of the surveys used to measure the preference in addition to those from subsequent survey rounds when the latter are not available, in order not to lose any observations. This serves to establish the proper temporal order between the independent and dependent variables, which is a necessary condition for establishing causal relationships. The legal origins thus come before the measures of preferences, and the preferences are measured no later than the economic outcomes we aim to predict.
Descriptives of risk tolerance in the WVS-ESS data To capture risk tolerance, we use a survey question contained in the World Value Survey (WVS) and the European Social Survey (ESS). Survey questions to capture risk preferences have gained traction in economics following a seminal validation by  Dohmen et al. (2011). Subsequent validations using alternative methods confirm the viability of survey questions in capturing risk tolerance in controlled lottery experiments  (Hardeweg et al. , 2013; Galizzi et al. , 2016). Using identical experiments with students in 30 countries,  Vieider et al. (2015)  showed that (i) survey questions are predictive of risk tolerance as measured in incentivized experiments in most countries; (ii) survey questions predict not only choices over gains, bus also over losses and when probabilities are unknown; and (iii) these correlations are even stronger at the aggregate level.
Descriptives of entrepreneurship in the WVS-ESS data We compose our data on entrepreneurship rates from the same surveys from which we take our measure of risk tolerance. Since both the WVS and the ESS provide nationally representative data, our measure ought to accurately measure entrepreneurship rates in the included countries. To guarantee independence of the different measures, and to use as much as possible data on economic outcomes hat are measured subsequent to the preference data in keeping with the proper consecutio temporum implied by our structural equations, we generally use entrepreneurship data from survey waves subsequent to he ones from which we obtain the preference data. Exceptions are made for countries that do not recur in subsequent waves, and where we thus considered it preferable to use the same wave rather than losing the observation.
The entrepreneurship data derive from a question asking respondents whether they are self-employed. This type of question has been used frequently in the literature to capture entrepreneurship (see e. g. Borjas and Bronars, 1989; Chanda and Unel, 2019). We found that in some developing countries included in the WVS, the question seemed to have registered independent farmers as entrepreneurs. We thus used a different question in the WVS on employment to correct such entries. Fig. 6  shows a world map of entrepreneurship rates. Countries with entrepreneurship rates in the 4th quartile are invariably developing or middle income countries, such as Nigeria, Ghana, India, Indonesia, Iran, and Thailand, as well as Mexico and several south-American countries. The lowest levels of entrepreneurship are found in Eastern Europe and parts of Central and East Asia, as well as in Argentina.
6 This leaves us with 73 countries in the sample. Panel 8(a) investigates future orientation, and panel 8(b) individualism. Neither of the variables is associated with any of our measures of interest, and the path 𝐿𝑂 → 𝑅𝑇 → 𝐸𝑆 remains intact. We next control for preferences measures, and in particular, trust and patience. We assemble the data on trust from the same WVS and ESS surveys as done for risk tolerance, using the question on generalized trust included in both surveys. In the ESS, we reduce answers to the question to a binary measure to make it comparable to the WVS answers. In Fig. 8(c ), risk tolerance takes the place of the confound in the model, since from a theoretical point of view we best think of risk tolerance as determining trust, Fig. 8. Other preferences as confounders of the 𝑅𝑇 → 𝐸𝑆 relation.
Robustness analysis The biggest challenge to the model as estimated so far comes from potential endogeneity concerns about the legal origins variable. While reverse causality from risk tolerance to the colonization or conquest by one versus another European power centuries ago would appear extremely implausible, it is nevertheless conceivable that some other characteristics of the countries included in our dataset-be they geographic, climatic, economic, or demographic-may have affected the likelihood of being colonized by England rather than France. If those same characteristics have also contributed to shaping risk tolerance, this could bias the effects we estimate. In this section, we tackle this issue head-on, using two complementary strategies. We start by running additional   3 ) introduces a dummy indicating whether a country has ever been colonized, taken from  Ertan et al.
[1] [2] [3] [4] [7] [8] [9] [10] [11] [12]  For mortality, available studies have consistently shown that the effects of cold are distributed along a wide range of lags, of up to a month, while the effects of heat are more immediate and do not usually last for more than a week. As a result of the distributed nature of the approach modelling these short-term lagged effects, studies generally rely on regression models applied to daily timeseries of the environmental exposure and the health response. A major impediment to this type of large-scale analysis is the restricted access to daily health records in many countries, regions and cities, which limits the homogeneity, comparability, spatiotemporal coverage and population disaggregation of the results. Projects and initiatives, such as PHEWE,  13, 14  NMMAPS,  15  EURO-HEAT,  16  CIRCE,  17  PHASE,  18  MCC  2, 9  and EARLY-ADAPT,  1  have been designed to compile and jointly analyse daily health databases covering large continental or global domains.