dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors would like to acknowledge  Johan Parra ,  Patricia Delville ,  Cristophe Boitel  and  Marc-Antoine Drouin  from the SIRTA atmospheric observatory for their assistance with the execution of the field experiments. This acknowledgement is extended to  Razvan Pirloaga  and  Dragos Ene  from the  INOE Institute, Romania. We would also like to thank  Fabrice Bertrand  and  Jean-Paul Vinson  from the  LATMOS Laboratory, France , for their collaboration. We would like to acknowledge the two reviewers for their expert comments which enabled us to improve the proposed calibration method. Felipe Toledo acknowledges the  French Association Nationale de la Recherche (ANRT)  and the company Meteomodem for their contribution to the funding of this work. Finally, we state that this work is part of the  ACTRIS-2  project and has received funding from the  European Union 's  Horizon 2020 research and innovation program  (grant no.
654109 ). Financial support. This research has been supported by the  European Union 's  Horizon 2020 research and innovation program  ( ACTRIS-2  (grant no. 654109 )) and the  French Association Na-tionale de la Recherche (ANRT)  and the company  Meteomodem. Together , they helped fund the work of coauthor Felipe Toledo. Review statement. This paper was edited by  Stefan Kneifel  and reviewed by  Alexander Myagkov  and one anonymous referee.
| Statistical analyses All statistical analyses were conducted using the software r (version 3. 5. 1; www. r-proje ct. org). The number of body-shaking were tested using a general linear mixed model (LMM), in which the presence/absence of reproductives, the presence/absence of eggs, the colony of origin ("Nestmate" or "Non-nestmate"), and all interactions among these three factors were used as explanatory factors. The stock colonies were also included as a random factor in the model to control for their multiple uses across the experimental micronests. To fit with homoscedasticity and normal distribution of model residuals, the numbers of "body-shaking" were log-transformed. The model was simplified following a step-by-step procedure removing the nonsignificant interactions, and then, post hoc Tukey HSD comparisons were conducted.
Data. Surveys and child anthropometry data. We extracted individual-level height, weight and age data for children under 5 from household survey series including the Demographic and Health Surveys, Multiple Indicator Cluster Surveys, Living Standards Measurement Study and Core Welfare Indicators Questionnaire, among other country-specific child health and nutrition surveys  [49] [50] [51] [52]  (Supplementary Tables  3  and 4 ). Included in our models were 420 georeferenced household surveys representing over 3 million children under 5. Each individual child record was associated with a cluster, a group of neighboring households or a 'village' that acted as a primary sampling unit. Approximately 185 surveys with height, weight and age data included geographic coordinates or precise place names for each cluster within that survey.
In the absence of geographic coordinates for each cluster, we assigned data to the smallest available administrative areal unit in the survey (polygon) while accounting for the survey sample design (15,781 survey polygons for overweight and wasting)  53, 54. Boundary information for these administrative units was obtained as shapefiles either directly from the surveys or by matching to shapefiles in the Global Administrative Unit Layers  55  database or the Database of Global Administrative Areas  56. In select cases, shapefiles provided by the survey administrator were used or custom shapefiles were created based on survey documentation. These areal data were resampled to point locations using a population-weighted sampling approach over the relevant areal unit with the number of locations set proportionally to the number of grid cells in the area and the total weights of all the resampled points summing to one  43.
Select data sources were excluded for the following reasons: missing survey weights for areal data, missing sex or age variable, incomplete sampling (for example, only children ages 0-3 years measured) or untrustworthy data (as determined by the survey administrator or by inspection). Details on the survey data excluded for each country can be found in Supplementary Table  5. Data extraction and processing methods have been described previously  21. Child anthropometry. Using height, weight, age and sex data for each individual, WHZs were calculated using the age-, sex-and indicator-specific lambda-musigma values from the 2006 WHO Child Growth Standards  10, 57. The lambda-musigma methodology allows for Gaussian z score calculations and comparisons to be applied to skewed, non-Gaussian distributions  58.
A child was classified as overweight or wasted if their weight-for-height/length was more than two s. d. (z scores) above or below the WHO growth reference population, respectively  59. These individual-level data observations were then collapsed to cluster-level totals for the number of children sampled and total number of children under 5 affected by overweight and the total number of children who are wasted out of the children who were not overweight. Temporal resolution. We estimated prevalence of overweight and wasting annually from 2000 to 2017 using a model that allowed us to account for data points measured across survey years, and as such, allows us to predict at monthly or finer temporal resolutions. We were limited, however, both computationally and by the temporal resolution of covariates (Supplementary Table  6 ) and thus produced only annual estimates.
Seasonality adjustment. WHZs were used to calculate individual child wasting status. As a data preprocessing step, we performed a seasonality adjustment on individual-level child weights in order to account for differences in observed child weight that may have been due to food scarcity during the month in which the survey was conducted. To adjust weight measurements, we fitted a model for each region with a 12-month seasonal spline, a country-level fixed effect and a smooth spline over the duration of our data collection using the mgcv package in R and the following formula: WHZ  s cc month ð Þ þ s tp t ð Þ þ as:factor country ð Þ : I Month is the integer-valued month of the year (1, …, 12), t is the time of the interview in integer months since the earliest observation of any child in the dataset and country is a factor variable representing the country where the observation was recorded.
These covariates were selected based on their potential to be predictive for overweight and wasting, according to literature review and plausible hypothesis as to their influence. Acquisition of temporally dynamic datasets, where possible, was prioritized to best match our observations and thus predict the changing dynamics of the two indicators. Of the 12 covariates included, 6 were temporally dynamic and were reformatted as a synoptic mean over each estimation period or as a mid-period year estimate. These included average daily mean rainfall (precipitation), educational attainment in women of reproductive age (15-49 years old), enhanced vegetation index, fertility, urbanicity and population. The remaining six covariate layers were static throughout the study period and were applied uniformly across all modeling years; these covariates included growing season length, irrigation, nutritional yield for vitamin A, nutritional yield for protein, nutritional yield for iron and travel time to nearest settlement >50,000 inhabitants.
To select covariates and capture possible nonlinear effects and complex interactions between them, an ensemble covariate modeling method was implemented  60. For each region, three submodels were fitted to our dataset, using all of our covariate data as explanatory predictors: generalized additive models, boosted regression trees and lasso regression. Each submodel was fitted using fivefold cross-validation to avoid overfitting and the out-of-sample predictions from across the five holdouts were compiled into a single comprehensive set of out-of-sample predictions from that model. Additionally, the same submodels were also run using 100% of the data and a full set of in-sample predictions were created. The three sets of out-of-sample submodel predictions were fed into the full geostatistical model as the explanatory covariates when performing the model fitting.
healthdata. org/lbd/dbm. ). Administrative boundaries were retrieved from the Database of Global Administrative Areas  39. Land cover was retrieved from the online Data Pool, courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center, USGS/Earth Resources Observation and Science Center, Sioux Falls, South Dakota 40. Lakes were retrieved from the Global Lakes and Wetlands Database, courtesy of the World Wildlife Fund and the Center for Environmental Systems Research, University of Kassel  41, 42. Populations were retrieved from WorldPop  43, 44.
1a ]. Magnetic structure refinements according to the other allowed irreducible representations do not describe the observed magnetic scattering and are disregarded. This result shows that below Tc PrAlGe displays ferromagnetic correlations along the easy c-axis. The size of the ferromagnetic moment refined from the data is 2. 29(3) µB/Pr. To investigate the magnetic correlations further, small-angle neutron scattering (SANS) measurements were performed on a PrAlGe single crystal using the SANS-I instrument, PSI. SANS probes variations in the magnetization density on the meso-to nanoscale, covering a typical range of real-space length scales from 5 to 500 nm. Therefore, the observation of magnetic SANS at low momentum transfers near |q| = 0 (which is the FM zone center) can signify the existence of short-range spin correlations and/or magnetic spins correlated over nanometric length scales.
Therefore chemical substitution promises a straightforward route towards an easy low magnetic field control control of both Weyl node type and possible axial gauge fields of Weyl fermions, complementing other control methods such as rotation of a uniform magnetization  48  , photoinduction  49, 50  , or external pressure-induction  51  , and thus adding to the catalogue of functional responses that may be useful for applications. sample grown from flux, and loaded into a standard cylindrical vanadium container. The sample temperature was controlled using a standard Orange cryostat with a base sample temperature 1. 6 K. No magnetic field was applied. The refinement of the PND profile in Fig. 2b  was performed using the FullProf software  53. In the software, the instrumental contributions to the diffraction peak shape are taken into account by using fixed peak profile and shape parameters determined from standard sample measurements done in the same instrument setup.
Small-angle neutron scattering (SANS) SANS measurements were performed using the SANS-I instrument at PSI. Most of the SANS data presented here were obtained on a 25 mg floating zone grown single crystal. For the experiment the crystal was mounted with a horizontal plane defined by orthogonal [001]- behind the sample. Data measured in each configuration were normalized with respect to the incident beam intensity to cover a q-range 0. 007 Å -1 < q < 0. 055 Å -1. Exploratory measurements done upon rotating the sample and magnet together (`rocking' measurements) showed no angle-dependence of the scattered magnetic intensity on the detector over a range of ±5°. Therefore, we collected data at fixed sample angle, with a data collection time at each stabilized T, µ0H typically of 5 to 20 min. To obtain the one-dimensional profiles in Fig.
Acknowledgements The authors thank  B. Fauqué  for fruitful discussions. D. D. , L. D. , and J. C. acknowledge support by the  Swiss National Science Foundation (SNF). D. D. acknowledges further support by the  Forschungskredit of the University of Zurich , grant no. FK-18-088. L. D. is partially funded by the  Swiss Government through Excellence scholarship. J. S. W. acknowledges funding from the  SNF Sinergia Network "NanoSkyrmionics"  Grant No. CRSII5_171003 , and the  SNF  project  200021_188707. S. S. T. and T. N. acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme (ERC-StG-Neupert-757867-PARATOP ). A. G. G. acknowledges financial support from the  ANR  under the grant  ANR-18-CE30-0001-01  and the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  829044.
Powder neutron diffraction (PND) PND was performed using the DMC instrument at the Paul Scherrer Institute (PSI). The neutron wavelength was 2. 457 Å. Diffraction profiles were collected from a 2 g powder integrals of Berry curvature-related properties, that closely follows the postw90. x module of Wannier90 Fortran code, but with important improvements, that significantly speed-up the calculations and improve the result quality. Among the advances are usage of fast Fourier transform, explicit account of symmetries, and a recursive-adaptive refinement procedure, which recursively increases the density of k-points sampling around the points which give the major contribution. Technical details of the implementation of Wannier19 will be published elsewhere, and the code is freely available on github.
The evaluation of Eq. (  2 ) is much more susceptible to computational inaccuracies than the AHE in Eq. (  1 ), in particular in terms of the BZ sampling at which the AHC is calculated. We find that to get a converged value of αxy A /T, we need a hyperfine grid of 432x432x432 k-points with a subsequent recursive refinement of 3% of the k-points. freely available without restriction online at: https://github. com/stepan-tsirkin/wannier19.
HbVar (http://globin. bx. psu. edu/hbvar) is a widelyused locus-specific database (LSDB) launched 20 years ago by a multi-center academic effort to provide timely information on the numerous genomic variants leading to hemoglobin variants and all types of thalassemia and hemoglobinopathies. Here, we report several advances for the database. We made clinically relevant updates of HbVar, implemented as additional querying options in the HbVar query page, allowing the user to explore the clinical phenotype of compound heterozygous patients. We also made significant improvements to the HbVar front page, making comparative data querying, analysis and output more user-friendly. We continued to expand and enrich the regular data content, involving 1820 variants, 230 of which are new entries. We also increased the querying potential and expanded the usefulness of HbVar database in the clinical setting.
The HbVar database of hemoglobin variants and thalassemia mutations is one of the oldest and most widely used locus-specific databases (LSDBs), not only from the globin but also from the wider genetic database community. Hb-Var was launched 20 years ago, in 2001. It was built from Nucleic Acids Research, 2021, Vol. 49, Database issue D1193 previous compilations of variants in books  (2, 3) , converting this information into a publicly available LSDB to provide timely information to interested users, e. g. the globin research community, patients and their parents, and providers of genetic services and counseling. HbVar was developed in such a way to allow for regular data entry updates and corrections, as new hemoglobin variants and thalassemias continue to be discovered. In addition, with a comprehensive query interface, HbVar enables the user to easily access the stored information particularly for the research community, but it is also an aid for physicians in diagnosis.
UPDATES TO EXISTING DATA Since the launch of HbVar (5) and the previous database updates in 2004  (6) , 2007  (7)  and 2014  (8) , HbVar information has been expanded by more than 230 additional entries and data corrections, made continually by the database curators. Importantly, Dr. Philippe Joly (H ôpital Edouard Herriot, Unité de Pathologie Moléculaire du Globule Rouge, Lyon, France) and Dr Serge Pissard (Mondor Institute of Biomedical Research, Department of Genetics, Creteil, France) have recently joined the HbVar team as data curators. In order to identify new hemoglobin variants and thalassemia mutations not previously documented in the database, we manually scanned articles from the specialized journal Hemoglobin, which frequently publishes new hemoglobin variants and thalassemia mutations, and where applicable, previously undocumented variants and additional information for existing variants have been entered into HbVar.
THE NEW HbVar HOME PAGE In order to better capture the data content, interrelated databases and recent updates and user statistics, the HbVar home page has been completely rebuilt. Firstly, the HbVar logo has been redesigned to capture the original concept as well as the Hb molecule notion in a more vibrant manner. Secondly, the query the database functionality now occupies a more central arrangement in the database to facilitate activity by the end-user, compared to the previous situation. Also, we included, in a tabular format, links to important HbVar functionalities and features that are grouped in different rows in the HbVar curators and contact information are provided at the end of the new HbVar home page.
CLINICALLY RELEVANT QUERY PAGE UPGRADES HbVar database has been considered a beneficial resource in hemoglobin research since its establishment. As such, since its last update, we opted to focus on clinically relevant updates that would also make HbVar more useful to the clinical community as well. Below, we describe two new features that aim to help clinicians in better exploiting the wealth of information available in HbVar. Both features are selfexplanatory with a brief description at the top of each query window to facilitate the user.
Compound heterozygotes phenotype Given the many genomic variants that yield different Hb variants and thalassemia mutations, and most of them in high allelic frequencies  (6, 9) , there are often compound heterozygous cases that have different clinical features and laboratory findings  (13). Knowing the specific clinical features of a combination of certain variants is crucial to establish accurate diagnosis. For example, a common misdiagnosis can be the combination of an HBB and an HBD gene variant that leads to normal HbA 2 levels. The normal levels of HbA 2 means that these cases can easily escape the attention of the physician but identifying them can be of utmost importance especially in the case of prenatal diagnosis. Therefore, we developed a tool to allow the HbVar users from the clinical community to explore the clinical features associated with combinations of globin gene alleles in compound heterozygotes (a total of 309 entries of the database).
49, Database issue Figure  1. The new compound heterozygotes phenotype tool of HbVar. A user can select the desired columns for the table output display from a wide variety of options, according to the available HbVar data. The count of the number of rows that have data in each column is provided in brackets after each column name (A). Upon selection of the columns, a table is generated and the user can narrow down his search by selecting the data from the filters at the left side of the page (B). the associated allele, where the query returns 38 results and explore the clinical features that he has previously selected in the table output. Each HbVar entry is a hyperlink that takes the user to the respective HbVar entry page (Figure  1 ). The query output can be also exported in a. csv file format.
SNP coordinates converter With the different numbering systems to determine a genomic position, there is often ambiguity as to the position of a specific variant, especially among clinicians who often need urgently to assess clinical information of a specific variant. We have therefore developed a tool that provides this positional information and specifically converts the genomic position provided in the common number system to the various other systems, such as the official Human Genome Organization (HUGO) genomic DNA-based description, the Human Genome Variation Society (HGVS) coding DNA reference sequence, the DNA-based description using the GenBank reference sequences NG 000007. 3 and NG 000006. 1 and lastly, the common protein-based description. This tool is available at http://globin.
DATABASE ACCESS Since their launch in January 2001, the HbVar database and associated resources at the Globin Gene Server [http: //globin. bx. psu. edu], such as the online Syllabi, are regularly used worldwide. Also, HbVar is very frequently accessed by Facebook and mobile devices. Users frequently contact the curators and the rest of the HbVar team members in order to submit new hemoglobin variants and/or thalassemia mutations, report missing information for existing mutants, identify inconsistencies and/or erroneous entries, and even propose collaborative projects related to HbVar data records. Since its last update, and as seen in the 'User statistics' page that is now available (http://globin. bx. psu. edu/hbvar/ usage graphs. html), the number of annual users now exceeds 15000 for the query page and 8000 for the Summary page (based on unique IP addresses).
FUTURE PROSPECTS HbVar has become, since its inception and first launch, a key data resource for information about DNA variants leading to hemoglobinopathies and is still considered one of the most important LSDBs from the various existing ones. Key factors that have contributed to its broad adoption and success are (a) its constant data update and improvements, mostly driven by the long-term devotion and enthusiasm of the data curators and other researchers involved in this project, coming both from Europe and the US, (b) its dynamic data querying and visualization tools, in conjunction with the UCSC and PSU genome browsers, that are constantly being upgraded to become more user friendly and (c) its interrelation with other stable and well-respected international databases. All these features allowed HbVar to maintain a positive impact on the research community and also allowed to attract funding on a continuous basis, dedicated or related to other projects.
This is particularly important for keeping HbVar operational, in an environment where dedicated funding opportunities for database development and curation are often very hard to secure, frequently resulting in the discontinuation of many useful databases. In order to ensure continuous HbVar data enrichment, we plan to implement a broader data searching strategy that includes text-mining tools and other electronic search procedures. This will complement the already existing tight links to the scientific journal Hemoglobin and also other resources such as the Human Gene Mutation database (www. hgmd. org. uk; (  14 )), next to existing databases with which HbVar has already existing bidirectional links  (7, 8). The recent emphasis that HbVar has given to expand its impact also among clinicians apart from researchers involved in globin research highlights its potential to make an impact in the clinical globin community, as well.