dataesr/scientific-paragraphs-categorization huggingface dataset:
They occupy roughly 30 Gb of disk space, with a large variation in single file size depending on the background intensity (generally decreasing away from the Galactic plane) and pulsar intensity. The files in 15deg 20MeV are on average 15× larger, including photons with energies > 20 MeV within 15 • of the pulsar position. They are suitable for spectral analyses (e. g. using the gtlike Fermi tool) or to explore the lowest LAT photon energies. README 3PC PhotonData. txt describes the FT1 files in more detail. 3PC sensitivity xxx. fits : The all-sky Fermi LAT pulsar sensitivity, in FITS format, useful for e. g. population syntheses. Example 3PC SensitivityMap xxx. py is a python script that reads the FITS file and generates a plot as in Figure  25. README 3PC SensitivityMap xxx. txt provides details.
5. Figure9shows an example of gamma-ray light curves in different energy bands for the MSP PSR J0030+0451. Figure4and Appendix B show further examples, overlaid with radio profiles when available. The supplementary online material includes all the pulse profiles constructed for this work. After preparation according to Section 2, the photon arrival times were converted to rotational phase using timing models obtained from pulsar timing observations conducted in radio or X-rays, or by direct timing of the LAT photons, as described in Section 3. In a number of cases, the timing models' validity interval only partially overlapped with the time range of the LAT dataset. To avoid contaminating off-pulse phase regions by extrapolating inaccurate models, we restricted the LAT datasets to the validity intervals in the cases of (1) timing solutions containing high-order (≥ 3) frequency derivatives suggestive of strong timing noise; (2) "IFUNC" or "WAVE" terms (also used to mitigate timing noise); or (3) "ORBIFUNC" or high-order (≥ 2) orbital frequency derivatives for pulsars in binary systems, or black widow pulsars that are often subject to timing instabilities.
Acknowledgements We thank  T. Minamikawa  at the  Graduate School of Science and Technology, Tokushima University , and  A. Nakamura  at the  Graduate School of Information Science and Technology, Hokkaido University , for their technical advice. This work was partially supported by  JSPS ,  Grant-in-Aid for Scientific Research  (No. 25287105  and No. 20K12604 ) (to TK and YH),  Grant-in-Aid for Exploratory Research  (No. 25650044 ) (to TK),  Japan Science and Technology Agency (JST) / Core Research for Evolutional Science and Technology (CREST) , Grant Number  JPMJCR1662 ,  Japan  (to TK, KF, YH), and the  Research Program of 'Dynamic Alliance for Open Innovation Bridging Human, Environment and Materials' in 'Network Joint Research Center for Materials and Devices'  (to YH).
We estimate the push and pull factors involved in the outmigration of Jews facing persecution in Nazi Germany from 1933 to 1941. Our empirical investigation makes use of a unique individual-level data set that records the migration history of the Jewish community in Germany over the period. Our analysis highlights new channels, specific to violent contexts, through which social networks affect the decision to flee. We estimate a structural model of migration where individuals base their migration decision on the observation of persecution and migration among their peers. Identification rests on exogenous variations in local push and pull factors across peers who live in different cities of residence. Then we perform various experiments of counterfactual history to quantify how migration restrictions in destination countries affected the fate of Jews.
The data set, known as the Resident List, was compiled by the German Federal Archives, which was instructed by the federal government in 2004 to create a "scientifically established directory of all Jewish residents in the German  Reich 1933 -1945 " (see Zimmermann 2013 ). 2 This data set records biographic information, as well as a detailed migration or deportation history, including the timing of migration movements, the destination countries, and/or the deportation date and place. To the best of our knowledge, we are the first to use this data set for a scientific quantitative study. Therefore, a first objective of our study is to establish the Resident List as a reliable historical source and describe the characteristics of Jewish emigration based on these data. We exploit the available information on individuals' city of birth to reconstruct (part of) their social network.
g. , localized violence and economic deprivation), leading to correlated effects. Our data and context provide a unique setting to tackle these issues. Indeed, the disaggregated nature of our data set allows us to control for a large battery of fixed effects that absorb many unobserved correlated effects. Moreover, crosscity mobility of the German population was high after the collapse of the German empire in 1918. Hence, when Hitler came to power, many peers, friends, and relatives were living in different cities of residence. Our measure of social network exploits this fact: we focus on distant peers (DPs), namely, individuals from the same age group and the same city of birth but living in a different city. As a result, decision makers and their (distant) peers were exposed to different local push and pull factors of migration, which should filter out potential correlated effects.
Our main data set offers rich information on the location of the interwar Jewish community in Germany and their migration decisions. However, it lacks information on individuals' education, income, and wealth, which potentially affect migration prospects. 4  This prevents us from assessing the effect of financial constraints life by financing religious and secular institutions, such as synagogues and more than 5,000 Jewish associations  (Gruner 2019). 4. It is likely that economic means affected the capacity and motivations of Jewish outmigration decisions in Nazi Germany. It is also likely that the influence of income and wealth for instance were ambiguous. On the one hand, richer individuals could more easily cover the costs associated with travel and relocation. On the other hand, well-off households might have been more sensitive to the expropriative taxes imposed on departure.
On the response of foreign countries, notably the United States, see  Strauss (1980 Strauss ( , 1981) )  and  Friedman (2017). Existing historical research has unraveled important patterns of Jewish emigration, such as by year and by geographical regions, as well as the demographic composition of Jewish emigrants (notably  Strauss 1980 Strauss , 1981;; Rosenstock 1956 ). These studies describe and discuss a spatial pattern of migration over time that is similar to what our data reveals, such as the initial rush to Western European countries, and the later shift to further-away destinations, such as the United States and Shanghai (see Section II. B). Note, however, that the numbers provided in this earlier research are limited in scope and precision, as Finally, we contribute to the empirical literature on migration by providing a cleaned and cross-validated version of the Resident List to the communities of economists, historians, and social scientists.
We believe that the coverage and quality of this data set make it valuable not only for tackling the specific case of Jewish migration in Nazi Germany but also for studying refugees' migration in general. The reasons the data set is so unique-even compared with many modern data sets on refugees-are numerous. It covers most of the population at risk. It contains fine-grained information on individuals and localities. It provides information not only on the migrants but also on the stayers. It includes multiple destinations allowing us to study the choice to outmigrate and the choice of destination (which feeds back on the incentives to outmigrate). To the best of our knowledge, none of the data sources covering modern or historic contexts covers all these features. The article is structured as follows.
II. A. Historical Background 1. Jewish Life in Germany. When Hitler took power in 1933, about 503,000 Jews were living in Germany according to the census of 1933, of which about four-fifths were of German origin, and about one-fifth were of foreign nationality  (Statistisches Reichsamt 1936). 7 Compared to 65 million people living in the German Reich, the Jewish community was small and made up they are often based on rough estimates by statistical authorities, and not based on comparable micro-data used in this article. 7. The census of 1933 recorded Jewishness based on a person's religious affiliation. In contrast, the Nazis defined Jewishness based on Jewish ancestry by counting the number of one's Jewish grandparents. Zimmermann (2013)  estimates that about 600,000 people satisfying the Nazi definition of Jewish ethnicity were living in Germany in 1933.
For some families their entire circle of friends was exclusively Jewish. [. ] neither acculturation nor the abandonment of religious observance brought about close relations with non-Jews" 8. Maurer (2005, 273)  , referring to sources other than the ones we use, reports that 55% of the entire Jewish population lived in the top 10 cities in 1933, a number consistent with the share we obtain with our data. 9. Meyer (2000)  counts about 35,000 mixed couples in 1933. According to  Kaplan (2005, 250) , only about 23,000 Jews converted in the German empire from Judaism to Christianity (between 1871 and 1919), which corresponds to about 4% given a population of Jewish people estimated at around 500,000. Data from the prewar period from 1933 to 1939 for a selected set of cities from  Lowenstein (2000, 85)  suggest small conversion rates in a range between 0.
It depended on local anti-Semitic sentiments and arbitrary decisions of local party members, which created an environment of fear  (Bartrop and Dickerman 2017). We document a strong positive correlation between detainment and local anti-Semitism using our data set in Online Appendix G. 2. In the first years of the Nazi rule, incidences of violence intensified and culminated in the infamous 1938 November pogroms, known as Kristallnacht or the "Night of Broken Glass. " During Kristallnacht, hundreds of synagogues were attacked, several thousand businesses were destroyed, and thousands of men were taken into custody. With the start of World War II, the Nazi policy changed from encouraging Jewish outmigration to the extermination of all Jews. At the end of 1941, emigration from Nazi Germany was officially forbidden, and mass deportations to the concentration camps began shortly afterward.
II. B. Data on Migration and Deportation We base our empirical investigation on individual-level information on migration, detainment, and deportation of Jews living in Germany in the 1930s. Our main source of information is the Resident List that was compiled by the German Federal Archives  (Zimmermann 2013). The Resident List provides information on individuals' first and last names, birth dates, birthplaces, gender, and places of residence. Particularly valuable for the purpose of our study, it records (when relevant) the migration and/or deportation history: timing of migration movements, the first and second destination countries, and/or the deportation date and place. Moreover, it conveys information on whether and when a person was detained (subject to Schutzhaft). We supplement it with the 1939 census to recover further information on the nonmigrants.
To the best of our knowledge, these sources had never been used for quantitative research. An important part of our work is hence devoted to transforming the raw information into estimation data sets and validating its suitability for econometric analysis. In this section, we provide a summary of our data construction procedure; see Online Appendix A for more details. To estimate our model of outmigration decision, we need to define and collect information on the overall Jewish "population at risk" (of persecution) over the period. Then we explain a number of sample cuts dictated by data availability and our procedure for encoding migration trajectories. Finally, we conduct several validation exercises. 1. Defining and Measuring the Population at Risk. Here we explain the definition of the population at risk of persecution in Nazi Germany and how the Resident List relates to it.
The Resident List. Our source of information for the migration and deportation status of Jews, as well as for their detainment status and cities of birth and residence, is the Resident List. In 2004, the Federal Government of Germany commissioned the Federal Archives to construct "a scientifically established directory of all Jewish residents in the German  Reich 1933 Reich -1945. "  (Zimmermann 2013, 2)  What is unique about the Resident List is its coverage of the resident population of ethnic Jews at the individual level. 16 The aim of the project was "to establish a list as completely and precisely as possible of the approximately 600,000 Jewish residents from a variety of sources, who [at any time] between 1933 and 1945 had resided in Germany"  (Zimmermann 2013, 2). However, the total number of individuals in the raw data of the Resident List (provided to us by the Federal Archives on March 25, 2019) is larger, at 812,566.
The difference comes from the fact that the Federal Archives chose a criterion of inclusiveness in the Resident List that was broader than what is needed for our purpose. The first difference is that they included all residents they could find with at least one Jewish grandparent. 17 The second difference emerges when we investigate the characteristics of individuals that lack information on their city of residence. It seems to be related to including a large number of foreign Jews who, during deportation, transited through Germany after 1941 (see Online Appendix B. 2 for more details). 16. It is important to note that the individual-level data of the 1933 census have been lost (according to our personal correspondence with the Federal Archives). The only official (undigitized) data on Jewish population in 1933 we managed to find from the 1933 census are aggregated at the district level.
VII. CONCLUSION In this article, we study the importance of social networks and immigration restrictions in destination countries for Jewish migration out of Germany during the period 1933 to 1941. Using individual-level data on Jewish residents of Germany, we find that network externalities played a first-order role in outmigration decisions. In particular, we document evidence for two novel channels of how networks can affect emigration in situations of violence: First, our results show that networks aggregate information about the extent of persecution, which affects outmigration incentives positively (threat effect). Second, we estimate a structural model of emigration and quantify the effect on outmigration of observing peers fleeing from Germany (exodus effect). This exodus effect is of significant magnitude and operates besides the standard diaspora effect of migration networks (late migrants tending to follow early movers' destination choices).
Finally, to confirm the variant of the mcr gene, the 3130 Genetic Analyzer (Applied Biosystems, Waltham, Massachusetts, USA) was used for Sanger sequencing on positive amplification products from PCR. The sequences obtained were aligned with others already described and characterized in the GenBank database using the Geneious 8. 0 (Biomatters, Auckland, New Zealand) and MEGA X (Molecular Evolutionary Genetics Analysis, https://www. megasoftware. net/docs) bioinformatics programs. A summary of study procedures is presented in Figure  1.
Data entry and statistical analysis Data were extracted from the study forms and laboratory records, double entered into a Microsoft Excel 2010 spreadsheet (Microsoft, Redmond, Washington, USA) and analyzed using EpiData v. 2. 2. 3. 187 (EpiData, Odense, Denmark). The prevalence of bacterial isolates and their genotypic and phenotypic resistance to colistin were calculated and reported with a 95% confidence interval (CI). Phenotypic resistance was defined as bacterial growth occurring in the broth microdilution test at future experiments. The plates were processed in a reader at the recommended wavelength (260 nm). Each isolate was inoculated and subsequently measured in duplicate.
This study was supported by funding from the Universidad de las Américas, Quito, Ecuador (grant number VET. MCA. 19. 03). The SORT IT antimicrobial resistance program is funded by the National Institute for Health and Care Research, Department of Health and Social Care, United Kingdom, and supported by implementing partners. All open access and ethics-related costs were covered by TDR. Funding was also provided by Consejería de Economía e Infraestructuras, La Junta de Extremadura (grant number IB18047). Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the Revista Panamericana de Salud Pública/Pan American Journal of Public Health or those of the Pan American Health Organization.
Conflicts of interest. None declared. Funding. This study was supported by funding from the Universidad de las Américas, Quito, Ecuador (grant number VET. MCA. 19. 03). The SORT IT antimicrobial resistance program is funded by the National Institute for Health and Care Research, Department of Health and Social Care, United Kingdom, and supported by implementing partners. All open access and ethics-related costs were covered by TDR. Funding was also provided by Consejería de Economía e Infraestructuras, La Junta de Extremadura (grant number IB18047). Authors hold sole responsibility for the views expressed in the manuscript, which may not necessarily reflect the opinion or policy of the Revista Panamericana de Salud Pública/Pan American Journal of Public Health or those of the Pan American Health Organization.
Databases The search was conducted between November 14 th and December 16 th 2019 in the following databases: CAB Direct, JSTOR, Pascal & Francis, PubMed, ScienceDirect, Scopus, WorldCat (see SI 1 for further details). To access French publications and grey literature, a complementary search was conducted over the same period using "Ixodes ricinus" as the unique keyword in the following databases: Agricola, Open Access Thesis and Dissertations, Thèses vétérinaires -i. e. Doctor in Veterinary Medicine (DVM) dissertation theses -(French only), and theses. fr -i. e. PhD theses -(French only). The efficiency of the search strategy was tested using a previously established test-list of 110 references known to be relevant for at least one of the established criterion references (see SI 2). To reach a more exhaustive corpus, additional references identified opportunistically and published until 2020 were also considered and submitted to the same eligibility screening (see below).
Acknowledgments The author thanks  Gilles Bourgoin ,  Sébastien Grech-Angelini ,  Sara Moutailler ,  Frank Boué ,  Mathilde Gondard ,  Guy Joncour ,  Lorraine Michelet ,  Gérald Umhang ,  Muriel Vayssier-Taussat ,  Olivier Plantard ,  Alber Agoulon ,  Thomas Pollet ,  Karine Chalvet-Montfray ,  Émilie Lejal ,  Maud Marsot ,  Frédéric Stachurski  and  Laurence Vial  for sharing their raw data on the presence of I. ricinus (Figure 2).
SPET produces a series of PEDT data recorded at once on different ROIs. They were acquired using a JEOL F200 (200 kV) TEM equipped with a NanoMEGAS Digistar unit capable of generating simultaneous precession and line scan motions. A 10 nm diameter electron beam was scanned in a direction perpendicular to the interfaces, from the substrate to the coating with a step of approximately 0. 92 nm between two acquisitions, resulting in oversampling of the same area of the film. The diffraction patterns were acquired with an ASI Cheetah M3 hybrid pixel detector (512 × 512 pixels) with 0. 02 s exposure time. The precession semi-angle was set to 1. 4 •. A goniometer tilt step of 2 • was applied after each line scan in the range [+44 • ,-34 • ], while a tilt step of 1 • was used in the range [-34 • ,-50 • ].
To avoid shadowing of the film by the substrate or coating during sample rotation  [8] , the tilt axis was aligned with the direction of scanning. Importantly, no tracking of a specific area was performed between each goniometer tilt. A rapid visual examination was carried out to ensure that, for each tilt angle, the SPET data were systematically collected from the substrate to the coating. For analysis, PEDT data from one given ROI were extracted from the SPET series and processed using the program PETS 2. 0  [14]. In this work, only 9 ROIs (out of 38 collected) were selected for data processing as indicated in Table  1  and in Figure  3. As structural parameters change rapidly close to the substrate, more ROIs were taken into account in this part of the film. Each ROI is referred to in relation to the distance (in nm) from the substrate interface as presented Figure  3.
Collecting PEDT data from both the substrate and coating during SPET data collection serves two purposes. Firstly, it indicates when the film is entered and exited during the scan. Secondly, PEDT data obtained from the substrate can act as an internal standard, enabling an accurate determination of the unit cell parameters. This is achievable thanks to new functionalities recently implemented in PETS 2. 0 to correct distortions induced by the microscope's optical system and by the precession motion  [15]. To ensure a standardized procedure for data analysis, we first determined calibration constant and distortion parameters on STO and used them for the subsequent analysis of PEDT data collected on different ROIs of the film through its thickness. Kinematical and dynamical refinements were performed on JANA2020 with the results of the dynamical refinements reported in Table  1.
Note that while the kinematically derived R(obs) values typically fell within the range of 25 to 30%, the use of dynamical refinements resulted in a significant reduction in these values (Table  1 ), accompanied by an enhancement in the accuracy of structural parameters. We performed first-principles simulations to corroborate the experimental lattice parameters and tilt angles obtained by SPET. For this, we used Density Functional Theory simulations using the Vienna Ab initio Simulation Package  [16]  in combination with the meta-Generalized Gradient Approximation (meta-GGA) Strongly Constrained and Appropriately Normalized (SCAN) exchange-correlation functional  [17]. This functional was previously shown to produce the correct description in terms of the insulating or metallic character and structural parameters of various 3d transition metal oxide perovskites  [18].
Conclusions In this work, we introduced a new approach to thin film characterization based on 3D ED. SPET has been previously used  [9, 10]  to obtain crystallographic phase and orientation mapping on a volume. In this study, we extracted electron diffraction intensities (3D ED datasets) from different ROIs and analyzed them quantitatively, aiming for accurate structure refinements. Specifically, SPET was used on a 35 nm PVO thin film, combining a scanning procedure with PEDT. By scanning with a nanobeam of 10 nm diameter along the PVO film thickness at every tilt step during a PEDT experiment, we could analyze the film structure at different thicknesses by properly sorting the acquired diffraction patterns. Cell parameters and atomic positions were accurately determined and an evolution along the thickness of the film was observed in the out-of-plane unit cell parameter, as well as in the tilting of the VO 6 octahedra and in the y coordinate of the Pr atoms.