dataesr/scientific-paragraphs-categorization huggingface dataset:
Creating the Distance-Adjusted Air Passenger Data [3] The goal here is to adjust the KCMD-revised air passenger trend data [2] to correct for the fact that it underestimates mobility at short distances due to the use of alternative transportation means. To do so, we draw on the distance (in km) between country pairs. Our correction factor is specified as: k max k A$B 1= c where k max is the maximum possible distance between two countries, in this case 19,951. 16 km (the distance between Paraguay and Taiwan), and k A $ B is the empirical distance between two countries A and B, based on CEPII's GeoDist dataset  (Mayer & Zignago, 2006). The parameter c is chosen so that it maximizes the   Mayer and Zignago (2006)  correlation r between the GMP-revised tourism data [1] and the KCMD-revised air passenger trend data  [2].
Creating the Global Transnational Mobility Dataset In the final step, we merge the two revised data sources. As we hold that both the GMP-revised tourism data [1] and the distance-adjusted air passenger data [3] individually tend to under-estimate actual mobility flows (see Sect. 9. 2), our final estimate is always the largest of the two when we have both kinds of information-that is, either [1] or  [3]. When we lack [3], we take [1]; and vice versa. As final steps, we: -Round decimals (non-integer estimates can occur due to the time-series decomposition applied by  Gabrielli et al. , 2019  and the correction factor introduced above). -Add missing full country names and information on the world region a country is located in, based on the United Nations classification (drawing on Duncalfe, 2018).
-Exclude countries for which, after the merging procedure, no information was available. 10 Consequently, the dataset is reduced to the set of 196 countries used when creating the unified UNWTO dataset. The resulting Global Transnational Mobility Dataset can be explored on an interactive world map at the KCMD Dynamic Data Hub (https://bluehub. jrc. ec. europa. eu/ migration/app/index. html; browse 'Datasets' -'Mobility' -'Global Transnational  Mobility (KCMD-EUI)' -'Estimated Trips'). More information can be found on the website of the Migration Policy Centre of the EUI (https://migrationpolicycentre. eu/ projects/global-mobilities-project/), where the dataset can be downloaded. A list of the variables contained in the dataset can be found in the Appendix (Table  9. 3).
Exploring the Dataset: Key Descriptive Findings The Global Transnational Mobility Dataset covers 196 sender and receiving countries. Through the integration of two different sources, it is, to our knowledge, more comprehensive than all pre-existing information on worldwide cross-border mobility. Among other merits, its focus on transnational movements on a global scale helps to put migration in perspective, in both its geographical and demographic scope. The number of yearly migrant flows is very difficult to establish, and different alternative estimation methods have been proposed  (Abel & Cohen, 2019; Abel & Sander, 2014; Azose & Raftery, 2019; Dennett, 2016). According to these methods, estimates range between 30 and 90 million migration episodes per year in the 2010-2015 period  (Abel & Cohen, 2019, p.
8). Based on our new dataset, we estimate that, on average, about 2. 55 billion yearly cross-border trips took place in the 2011-2015 period. Very crudely, thus, international migration episodes are  between 28 and 85 times (depending on migration estimates) less frequent than human movements across national borders in general. For specific regions, this ratio can be even higher. For example, in the European Union (for which actual yearly migration flow data is available), approximately 500-700 transnational trips occurred for every migratory move in 2016  (Deutschmann & Recchi, 2022). While we leave to future research the full exploitation of the dataset's potential, also in conjunction with other datasets (not only on migration but also, for instance, on global trade, bilateral political relationships and many other potential predictors or predicted variables), the following pages offer a preliminary outline of several major takeaways.
For example, it has been shown that transnational human capital is an important resource that improves opportunities in life  (Gerhards et al. , 2017). Furthermore, transnational mobility shapes world views, attachment to other countries and cosmopolitan attitudes  (Deutschmann et al. , 2018; Helbling & Teney, 2015; Kuhn, 2015; Mau et al. , 2008; Recchi, 2015). While these consequences of unequal access to transnational mobility chances have mainly been studied from a European viewpoint so far, a global perspective is largely missing. The Global Transnational Mobility Dataset may prove a good starting point for future analyses in this direction. The next section digs a little deeper into this global stratification by looking at the relationship between transnational human mobility and levels of prosperity.
Transnational Mobility Differs by Levels of Prosperity and Country Size There is a relatively strong and significant relationship between a country's number of outgoing trips and the national level of prosperity, measured as GDP per capita in purchasing power parity based on World Bank data (r ¼. 63). A similar pattern is found for the relationship between mobility and population size (r ¼. 58). The threedimensional graph in Fig. 9. 7 illustrates the relationship between the three factors in combination. The distribution of dots, representing countries, follows a clear pattern, ranging from low GDP, small population and low mobility (bottom front corner) to high GDP, large population and high mobility (upper back corner). These insights are not entirely new but are showcased in a clear and robust way by this novel dataset.
Discussion A spate of migration and asylum-seeking crises has been hitting the world since the turn of the twenty-first century. The globe is on the move but, in spite of their salience in the media and public opinion, refugees and other migrants constitute only a tiny portion of the whole number of people crossing borders daily. According to various estimates, there were between 30 and 90 million migration episodes per year GDP p. c. ( Outgoing trips (log) Fig. 9. 7 The relation between mobility, population size, and GDP per capita in the early 2010s worldwide  (Abel & Cohen, 2019). But according to our estimate, yearly border-crossings come close to 3 billion globally. By providing estimates of the amount of such transnational mobility beyond migration, the Global Transnational Mobility Dataset facilitates the study of the volume, directions and change of country-to-country human mobility on a worldwide scale.
This chapter described the procedures by which we have reached these estimates. While there is no single existing data source providing exact information on the number of people crossing national borders worldwide, we have argued that the two more complete and reliable sources (data on tourism and data on air passengers) show significant consistency and can be merged according to a few relatively simple combination rules. We hope that our work will prove useful in two regards. First, we hope that the freely available GMP Global Transnational Mobility Dataset will be used to tackle questions related to mobility at the global scale. Potential applications are manifold and range from transnational mobility's unequal global structure and its social consequences to analyses that use the data to model the spread of infectious diseases.
A first external study has already leveraged the dataset to model the spread of Covid-19  (Liu et al. , 2020). Second, we hope that some aspects of our methodological approach can be transferred to other instances where researchers may consider merging two data sources, for instance, where regional migration data is available from several sources. By focusing on yearly country-to-country flows of human mobility (whatever their duration), our dataset complements estimates of worldwide migration flows which refer to stays abroad longer than 12 months based on the conventional UN definition of migration. This dataset also improves upon previous usages of the UNWTO data  (Deutschmann, 2016 (Deutschmann, , 2021;; Reyes, 2013)  by capitalizing on an additional source and estimation methods. Finally, the Global Transnational Mobility Dataset parallels recent alternative attempts at measuring population mobility with digital sources  (Fiorio et al.
, 2017; Hawelka et al. , 2014; Messias et al. , 2016; Rango & Vespe, 2017; Spyratos et al. , 2018 Spyratos et al. , , 2019;; State et al. , 2013; Zagheni et al. , 2017). Data triangulation across our data and digital estimates may prove useful to test the comparability of outcomes obtained through such different approaches. Several important limitations remain. The first issue concerns the existence of grand-tour tourism and open-jaw flights. For instance, consider a traveler who goes on a round trip to Southeast Asia from Italy. She flies from Rome to Bangkok both on her way in and out and takes buses or rents a car to travel subsequently through Thailand, Vietnam, Laos, and Cambodia, before returning to Thailand to take her flight back home. According to the original UNWTO tourism data, there would be four trips: ITA ! THA, ITA ! VNM, ITA ! LAO, and ITA ! KHM.
According to the GMP-revised tourism data [1], there would be eight trips: ITA ! THA, THA ! ITA, ITA ! VNM, VNM ! ITA, ITA ! LAO, LAO ! ITA, ITA ! KHM, and KHM ! ITA. According to the air passenger data (regardless of distance-adjustment), there would be two trips: ITA ! THA, THA ! ITA. In reality, however, there were six trips: ITA ! THA, THA ! KHM, KHM ! VNM, VNM ! LAO, LAO ! THA, and THA ! ITA. In this case, both sources and all strategies lead to very different outcomes and none of them captures the transnational mobility that actually took place. This issue has no easy solution. Structurally, it should lead to a slight overestimation of long-distance mobility between world regions (which is most likely when such roundtrips are prone to occur). However, we argue that, compared to all global travels, these kind of journeys are rare and should not jeopardize the overall reliability of the dataset.
A second limitation consists of the following: by basing a substantial part of our mobility estimates on visitors who stayed overnight ('tourists' in the UNWTO terminology), we may be underestimating short-term border crossings. For instance by commuters who live in border regions and regularly go to the other side for work, leisure, or shopping. The following example is revealing in this regard: For the US, detailed data on land-border crossings are available (US Department of  Transportation, 2018). Looking at mobility between the US and Canada, the distance-adjusted air passenger data (see Sect. 9. 3. 3) estimates about 20 million trips, while the GMP-revised tourism data (see Sect. 9. 3. 1) suggests around 33 million trips. The recorded land-border crossings, by contrast, are 103 million-98 million private car passengers alone.
Many of these moves are not likely to be overnight stays. While it is hard to generalize from this example, it suggests that the mobility estimates in the Global Transnational Mobility Dataset (and the correction factor introduced in Sect. 9. 3. 3), although considerably larger than those provided by alternative global sources, are still quite conservative. Finally, it is important to keep in mind that the Global Transnational Mobility Dataset contains mobility estimates rather than counts of actual, recorded trips. This is crucial. By applying a statistical approach to correct and adjust the data, we aimed to create a revised dataset that on average captures mobility between countries more accurately. In a minority of individual cases, this revision procedure might, however, lead to more inaccurate estimates.
We would therefore like to remind that this dataset is well-suited to study structural features of transnational human mobility globally or for aggregates of countries. If the research interest is mobility between specific pairs of countries, the estimates in the Global Transnational Mobility Dataset should be taken with caution. Readers need to be aware of this limitation and should possibly compare the estimates to figures provided by alternative sources. With these caveats in mind, we hope that this novel dataset will prove to be a valuable resource for all researchers interested in studying the human side of globalization. More particularly, this dataset can help embed migratory movements into the larger picture of transnational human mobility and better eschew the 'settlement bias'  (Hugo, 2014)  that recurrently weakens traditional migration studies.
Attention to transnational mobility is especially needed to take into account less traditional and more reversible forms of migration (temporary, circular, shuttle, etc. ). Also, it is needed more generally to remind us that international migrants are first of all people who cross bordersand therefore part and parcel of a mobile world. Finally, there are several general lessons we want to share with readers who might be interested in following a similar merging strategy based on different datasets: 1. Automatize! The more the combination procedure is automatized, the easier it is to update datasets in the future. In our case, both the UNWTO and Sabre continuously update their datasets and it would be desirable to be able to quickly expand the time frame of the Global Transnational Mobility Dataset as these updates become available.
Despite the availability of monthly air passenger volume projections for 2020  (Iacus et al. , 2020) , we were unfortunately not able to automatize the whole process (partly due to point [2] below). However, scholars interested in conducting a similar project with other sources are advised to automatize as much as possible to increase efficiency and facilitate future updates. 2. Standardize! One of the most time-consuming issues in combining mobility data from different sources is to bring the datasets into a mergeable format. One common obstacle occurs when only idiosyncratic country names rather than standardized country codes are available. For example, rather than using the standardized code COD, sources often only contain idiosyncratic names such as "Congo, Democratic Republic of", "D.
It is always possible to make the dataset smaller but a lot more difficult to make it larger again. 6. Be cautious! In our view, merging data from different sources can bring advantagesand we see a lot of benefits of the GMP Global Transnational Mobility Datasetbut it may also carry risks, as already emphasized above. After all, different datasets are usually collected with different purposes in mind and are often based on different definitions and collection procedures. It is therefore important to keep the resulting limitations in mind and reflect carefully on whether or not a certain combined dataset is well-suited for a specific research goal. We hope that these recommendations can help other researchers who plan to embark on similar endeavors. Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.
There are 18 countries that are part of the UNWTO data collection that do not report arrivals by country of origin (which means they may be part of the full tourism dataset as senders of tourists but not as receivers): Afghanistan, Bonaire, Djibouti, Equatorial Guinea, Eritrea, Gabon, Ghana, Guinea-Bissau, Liberia, Libya, Mauritania, Saba, Sao Tome and Principe, Sint Eustatius, South Sudan, Syrian Arabic Republic, Turkmenistan, and United Arab Emirates.
A fourth important advantage is related to the fact we can use the SDID method to estimate the treatment effects in case of multiple treated units (like in a typical difference-in-differences where treated units are all jointly pooled on the treatment sample) as well as in case of a single treated unit to gauge the heterogeneous effects (like in a standard SCM where a single treated unit is compared to a weighted combination of the donor units). In our application of the SDID, we use data from Cambridge Econometrics European Regional Database (2017), which has been widely used in economic studies of European regions (for instance by  Becker et al. , 2010; Tabellini, 2010). This database offers comparable information across regions, sectors and time over a sufficiently long pre-1995 period.
Our analysis uses a ten-year pre-1995 period and terminates in 2001 to exclude confounding factors such as those that arise from Austria and Finland adopting the single currency. The Cambridge Econometrics European Regional Database (2017) covers NUTS 2 and NUTS 3 regions for EU-28 countries plus Norway. It includes measures of GDP, Gross Value Added (GVA), population, employment (at both NUTS 2 and NUTS 3 level), gross fixed capital formation (GFCF) and hours worked at the NUTS 2 level. Information is available for the total regional economy (all sectors aggregated, abbreviated with Total in the tables and figures throughout the paper) and for six broad sectors (NACE Rev. 2) as follows: A: Agriculture, forestry and fishing (abbreviated with Agriculture); B-E: industry less construction (Industry); F: construction (Construction); G-J: wholesale, retail, transport, accommodation & food services, information and communication (WRTAFIC); K-N: financial & business services (FBS); O-U: non-market services, which we exclude from our analysis.
Acknowledgements We would like to thank an anonymous referee and the editor,  Florin Bilbiie , for the valuable criticisms and detailed suggestions. We also would like to thank seminar participants at  London School of Economics, Johns Hopkins University, University College London, University of Florence, Université Paris Nanterre, University of Venice, Maastricht University, University of Bergamo, University of Verona ,  European Free Trade Association, Central Bank of Finland , and at the meetings of  Royal Economic Society, Journées LAGV Aix-en-Provence, Society for Institutional and Organizational Economics, European Economic Association, French Economic Association, and ASSET  for useful comments on previous versions. We also wish to thank  Luigi Moretti  for work on previous versions of this paper and useful suggestions and comments on the present version.
Such locally adjusted and empirically based thresholds have a low bias in total but, still, are challenged by a degradation of detection accuracy depending on the local surface brightness  (Bley and Deneke, 2013). This is caused by locally-optimized approaches that seek to find the optimal threshold to distinguish clouds and clear sky and thus have to deal with a surface-dependent bias (see explanation in Sect. 3. 2 and 1b)). For analyses of land-surface impacts on clouds it is, however, crucial that the satellite-based detection of cloud occurrence over different land cover types is unbiased with respect to surface properties. In this study, two approaches to detect boundary-layer clouds on the basis of SEVIRI HRV data are presented and compared for the analysis of cloud cover changes over different surface types: the locally optimized cloud detection scheme of the Local Empirical Cloud Detection Approach (LECDA) and the Regional Empirical Cloud Detection Approach (RECDA) that is robust to regional variations in surface reflectivity.
Both approaches are processed for November of a multi-year period over a large region of Paris and its surrounding area. The paper is structured as follows. In Sect. 2 the data used is described. The methods to derive LECDA and RECDA as well as the validation with in-situ Cloudnet data are explained in Sect. 3. Section 4 presents the results and discussion of the comparison as well as the validation of the two cloud masking approaches, and a meteorological case using RECDA. Section 5 contains the conclusions.
Cloudnet data The cloud masks are validated against the calibrated Cloudnet classification product (CF-1. 0, Level 2) for November (2015 to 2019) at Palaiseau (48. 718°N -2. 202°E) located ∼25km to the Southwest of Paris are used  (Illingworth et al. , 2007). The location of the Cloudnet station is shown in Fig. 2. Ground-based remote sensing instrumentation including a ceilometer, a doppler cloud radar and a microwave radiometer of the atmospheric observatory SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique,  Haeffelin et al. (2005) ) as well as model outputs of the ECMWF Integrated Forecast System (IFS) serve as input for the Cloudnet Target classification algorithms described in ACTRIS Deliverable WP5 / D5. 5 (M24). As a result a classification into 11 classes distinguishes ice and water clouds, precipitation, aerosols, insects, clear sky and combinations thereof (Appendix A: Table  A1 ) is obtained in a 30s time interval and for 719 heights above sea level  (168.
7µm ) -T CCR -2xT CCR (2) where T LC =1. 8K is the threshold for liquid clouds and T CCR =1K is the cloud confidence range. Details of this approach can be found in  Westerhuis et al. 2020. Pixel values with a CL F LS below 0 are removed from the dataset to retain only low-level liquid clouds. The ultimate goal of RECDA is to derive spatially unbiased cloud fraction anomalies over different land cover types, while the goal of LECDA is a pixel-by-pixel cloud detection with minimum bias. Both approaches are relatively simple and nearly independent of other channel information. The thresholds are determined empirically, based on occurrence frequencies of HRV counts in solar zenith angle (SZA) bins as shown conceptionally in Fig. 1a ) for a single SZA bin and pixel. The first peak of the distribution presents clear sky conditions (CS) while the second refers to cloudy (CL) conditions.
Local evaluation using Cloudnet data Cloudnet data are used to evaluate and compare the local performance of both approaches. In order to aggregate the Cloudnet Data to the temporal resolution of SEVIRI (15 minute interval), Cloudnet cloud fraction is calculated for each 15 minute time window (30 time steps). The time window of 15 minutes is approximated by the time a cloud needs to traverse the satellite reference pixel region of 3×3 pixels (∼ 3×5 km). This is roughly estimated based on the length of the ground track (∼ 4 km) times a length scale factor of 2 divided by the average wind speed of ∼ 10 m/s at cloud top height  (Greuell and Roebeling, 2009). The time window is centred around the corresponding SEVIRI time slot plus a time scan correction factor of 11 minutes. This   and Roebeling, 2009).
In general, the month of November, though relevant for this study, has only a limited number of daytime hours and thus available data per SZA slot. The ground-based validation of the satellite-retrieved cloud masks is difficult as differences can be due to the specific measure-   This study presents and compares the applicability of two empirical cloud masking approaches based on the single HRV channel of MSG SEVIRI for the high-resolution analysis of land surface effects on boundary layer clouds. The performance of the cloud mask based on the regional approach RECDA is compared to the local approach LECDA with respect to the influence of the underlying surface. Both cloud mask approaches and obtained cloud fractions are analysed over the ∼150×150 km region centered on Paris, where holes in fog and low stratus are expected.