dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, it is important to go deeper into data analysis (and even look for other datasets), in order to monitor social distancing in smaller units. In addition, in relation to the calculation of the Covid-19 reproduction number, it is possible that issues of under-reporting of cases and deaths may have occurred, and this must be taken into consideration when analysing trends. As future work, we plan to analyse the relationship between the rigidity of social distancing measures and the movement of people in other regions of the world, especially in those locations that started the entry into a second wave of Covid-19 in September.
2 ). To monitor the formation and annihilation of twins, magnesium specimens were cut such that all parent grains have their c-axis perpendicular to the LD, i. e. parallel to the BD, so that during the compressive loading steps, twins nucleate with their c-axis towards the LD while during the subsequent tensile loading steps, twins annihilate. Finite element analysis was used to determine the geometry of the samples such that they did not buckle before yielding. Two specimens were deformed in situ to (a) confirm the repeatability of our experimental data and (b) to determine the limit at which the specimen would buckle. The 3D-XRD experiment was done at ID-11, the European Synchrotron Radiation Facility (ESRF), using a monochromatic X-ray beam of 50. 239 keV energy. Specimens were mounted on an ADMET deformation rig to apply uniaxial compressive and tensile load while collecting diffraction patterns.
Methods 3D-XRD experiment and data analysis. The experiment was conducted at ID-11 of the European Synchrotron Radiation Facility (ESRF), Grenoble, France. The specimens were mounted on an ADMET rig where the strain was applied at the rate of 5. 5 × 10 -5 1/s. The macroscopic force was measured by a 500 N load cell at the precision of 1mN. During the experiment minimum creep was recorded; for example at Plasticity-C the recorded strain was -3000µε before pattern collection and -2831µε at the end of pattern collection. At each loading step, in order to measure tensorial stress for each grain, specimens were rotated from -53°to 53°a nd then from -234. 5°to -125. 5°with 0. 25°rotation increment while collecting diffraction patterns. Diffraction patterns were collected on a 2048 × 2048 pixels director with the pixel size of 47.
2 µm. Python codes were developed to post-process the collected diffraction patterns using ImageD11 and the subroutines embedded in Fable (https://github. com/FABLE-3DXRD). After calculating the background image and subtracting it from diffraction patterns, peasksearch was done by applying a series of threshold intensity level of the form 50 × 2 n (for 0 < n < 8). This means that when n = 8 and n = 0 were used, only peaks with intensities higher than 12800 and 50 were analyzed. This allows for separating high-intensity peaks first, and then move towards the low-intensity ones. Once diffraction peaks were identified, they were indexed by looking for expected angular relationship between peaks falling on the different Debey-Sherrer rings. At this stage, it was possible to tune single-crystal parameters at the Preload to calculate a "zero" stress for each measured layer.
The blind mocks use known underlying cosmology but has an unkown value for the growth rate which is revealed only after the analysis is finished. This allowed us to asses the presence of any systematic biases in the measurements, arising from limitations in the theoretical RSD models. This study is part of a series of papers analysing the complete eBOSS sample from data release 16 (DR16). Table  1  provide a full list of the papers involved in obtaining cosmological constraint from eBOSS DR16. This paper is organised as follows. We first describe the eBOSS ELG sample in § 2. The models of redshift space distortions are described in § 3. The N-body simulations used in this paper are described in § 4. The details on method to obtain summary statistics from galaxy catalogues is given in § 5. The models for ELGs are described in § 6.
Tracers The eBOSS ELGs are selected based on high [O II ] flux and are expected to be mostly star forming galaxies typical of the population at high redshift. An earlier study about ELG selection with the SDSS infrastructure was performed by  Comparat et al. (2013a,b)  and a pilot survey of ELG testing different target selection algorithms is reported in  Comparat et al. (2016). The ELG sample in eBOSS is selected from intermediate release (DR3/DR5)  (Raichoor et al. 2016)  of the -photometry of the Dark Energy Camera (DECam) Legacy survey (DECaLS;  Dey et al. 2019). The target selection rules for ELGs in the North Galactic Cap (NGC) and South Galactic Cap (SGC) are slightly different due to the availability of deeper data in the SGC. The ELG selection has two parts, the first of which is to select star forming galaxies corresponding to the [O II ] emission and the second is to preferentially select galaxies around in 0.
MultiDark Planck 2 (MDPL2) The MultiDark Planck (MDPL2;  Klypin et al. 2016 ) simulation is publicly available through the CosmoSim database 1   (Prada et al. 2012; Riebe et al. 2013). MDPL2 is a N-body simulation run, consists of gravity-only, generated using the Gadget-2 code. The simulation assumes a flat ΛCDM cosmology with Ω  = 0. 307, Ω  = 0. 048, ℎ = 0. 67,   = 0. 96 and  8 = 0. 82. This simulation uses 3840 3 particles with mass of 1. 51 × 10 9 ℎ -1  in a periodic box of side length 1000 ℎ -1 Mpc. A halo catalogue using the ROCKSTAR 2  phase space halo finder  (Behroozi et al. 2013 ) at an effective redshift of  ≈ 0. 86 snapshot was constructed. ROCK-STAR starts with a Friends-of-Friends (FoF) group catalogue and analyses particles in full phase space (i. e. position and velocity) in order to define halo properties and robustly identify the substructures.
In principle, one could re-fit the basic HOD parameters along with each of the additional free parameters, but given the large errors in the HOD parameters and our focus on generating a variety with approximately the linear bias of eBOSS ELG sample, we did not performed such a refit. The projected correlation function measured from eBOSS data and various MultiDark based mock catalogues are shown in the upper panel of Figure  1. The black empty points show the measurement from the eBOSS sample with jackknife errors. The various coloured lines represent the projected correlation function measured from the mock catalogues. Solid lines are for HMQ models and the dashed lines are for SHOD models. The details of the different models are given in Table  3. Note that the differences in the projected clustering for different models mainly stems from the fact that we did not try to refit each model.
ACKNOWLEDGEMENTS SA and JAP are supported by the  European Research Council  through the  COSFORM Research Grant  (# 670193 ). VGP acknowledges support from the  European Union 's  Horizon 2020 research and innovation programme  ( ERC grant  # 769130 ) and by the  Atracción de Talento  Contract no. 2019-T1/TIC-12702  granted by the  Comunidad de Madrid in Spain. SH and KH acknowledge support under the  U. S. Department of Energy  contract  W-7405-ENG-36  at  Argonne National Laboratory. GR acknowledges support from the  National Research Foundation of Korea (NRF)  through Grants No. 2017R1E1A1A01077508  and No. 2020R1A2C1005655  funded by the  Korean Ministry of Education, Science and Technology (MoEST). RP and SE acknowledge support from the  French National Research Agency  under Grants No.
ANR-16-CE31-0021  ( ANR eBOSS ), No. ANR-11-LABX-0060  ( OCEVU LABEX ) and No. ANR-11-IDEX-0001-02  ( A*MIDEX  project). Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS-IV acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is www. sdss. org. SDSS-IV is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Harvard-Smithsonian Center for Astrophysics ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU) / University of Tokyo , the  Korean Participation Group ,  Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University ,  New York University, University of Notre Dame, Observatário Nacional / MCTI , The  Ohio State University, Pennsylvania State University ,  Shanghai Astronomical Observatory, United Kingdom Participation Group ,  Universidad Nacional Autónoma de México ,  University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University , and  Yale University.
In addition, this research relied on resources provided to the eBOSS Collaboration by the  National Energy Research Scientific Computing Center (NERSC). NERSC is a  U. S. Department of Energy Office of Science User Facility  operated under Contract No. DE-AC02-05CH11231. The CosmoSim database used in this paper is a service by the  Leibniz-Institute for Astrophysics Potsdam (AIP). The MultiDark database was developed in cooperation with the Spanish MultiDark Consolider Project  CSD2009-00064. The authors gratefully acknowledge the  Gauss Centre for Supercomputing e. V. (www. gauss-centre. eu) and the  Partnership for Advanced Supercomputing in Europe (PRACE , www. prace-ri. eu)for funding the MultiDark simulation project by providing comput-ing time on the  GCS Supercomputer SuperMUC at Leibniz Supercomputing Centre  (LRZ, www.
Mammographic density measurement A radiology technician performed mammography in both Jalisco and Veracruz. Cranio-caudal views were taken on each breast. Analog films were digitized using an Astra 2400S (Umax, Fremont, CA). Mammograms of both states were combined, and mammographic density was measured by a single observer on the cranio-caudal view of the left breast using Mamgr, a computer-assisted program developed at the Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, and based on previously reported mammographic density assessment methods  [26]. In a validation study, intraclass correlation coefficient between mammographic density measurements performed with the Mamgr software versus with the Cumulus program was 0. 87 (n = 100 mammograms), while the intra-observer intraclass correlation coefficient was 0.
Covariate data Data on dietary habits were collected through a 139item food frequency questionnaire (details previously published  [28] ). Information on frequency of consumption and portion size were used to calculate nutrients and energy intakes using the United States Department of Agriculture food-composition database and the Mexican National Health and Nutrition Survey database. Three dietary patterns were identified by principal component analysis ("Fruits and Vegetables," "Western," "Modern Mexican")  [28]. Intakes and frequency were also used to estimate the Healthy Eating Index (HEI) 2015 total score  [29]. Insulin-like growth factor 1 (IGF-1), IGF binding protein 3 (IGFBP-3), C-peptide, C reactive protein (CRP), leptin, and adiponectin analyses were performed in the laboratory of the Biomarkers Group at IARC  [30].
In this analysis, we were able to investigate the associations between several circulating metabolites and mammographic density measured at the same time, accounting for BMI and other potential confounders for which data have been collected in the Mexican Teachers' Cohort. The study design also allowed us to explore potential lifestyle, dietary, anthropometric, and metabolic correlates of key metabolites. In addition, women included in this study were not using oral contraceptives at the time of clinical examination, which is a strength given previous reports of different associations between metabolites and breast cancer risk according to hormone use  [17] , and blood was collected in fasting state for all women. Nevertheless, an important limitation of this work is that blood samples were collected only once, hence raising reproducibility issues regarding metabolites concentrations.
Availability of data and materials Access to the Mexican Teachers' Cohort resources can be requested using the dedicated form available at http://www. esmaestras. org/investigadores/ acceso_propuestas. php. Eligible individuals must be affiliated to an academic or research institution. Outside users are advised to informally consult MTC's principal investigator Dr. Lajous [mlajous@insp. mx] regarding the availability of data to address a specific research question. After preliminary approval, outside users prepare and submit a brief proposal that is reviewed by the MTC scientific committee.
Acknowledgements Initial stages of this research were funded through the  2018 University of Wisconsin-Milwaukee Research Growth Initiative (RGI) program  (SK, IM). SK also acknowledges partial support from the  Russian Science Foundation  (Contract  18-12-00231 ) (model development and theoretical considerations; sections 1-3) and from the  Russian Ministry of Education and Science  (project  14. W03. 31. 0006 ) (numerical experiments and interpretation of the results; sections 3-6). WKD was supported by  NSF  grants  OCE-1829856  and  OCE-2023585 , as well as by the  CNRS/ANR  grant  ANR-18-MPGA-0002.
Code availability The updated code alongside basic instructions on its use (see readme file there), as well as restart files for the six simulations described in this paper, are publicly available from GitHub at https://github. com/GFDANU/q-gcm (Kravtsov et al. 2021). To use the code, one should replace the routines and scripts of the original source code (publicly available at http://www. qgcm. org with the reference to https://github. com/GFDANU/q-gcm) summarized in Table  3  by their updated versions (contained in the new MQ-GCM folder on GitHub) and compile/run the resulting executable in the same way as before (see  Hogg et al. , 2014).
Yield data We rely on publicly available data at subnational level 1 over the 15 countries defining West Africa as defined by the Food and Agriculture Organization of the United Nation (FAO) classification (excluding islands). These are Benin, Burkina Faso, Côte d'Ivoire, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo. Two distinct datasets, based on public statistical offices, are available for time periods up to 23 consecutive years between 1984 and 2015 (a) FAOCountrySTAT  [43]  and (b) AgroMAPS  [44]. FAOCountrySTAT and AgroMAPS cover administrative levels 1 and 2 and inform annual production (in tons) and annual harvested areas (in ha). We compute yields from these two variables. In total, there are 201 geographical units at administrative level 1 over West Africa.
e. we select those with at least nine years with gaps of length inferior to three consecutive years). These three criteria are completed in 316 time-series from FAO-CountrySTAT and 267 time-series from AgroMAPS. In a final step, we merge both datasets and remove redundant values, obtaining a total of 399 yield times series at administrative level 1 for time periods of length 9-23 years between 1984 and 2015 (see figure  S2 ). Note that in the merging process we have prioritized FAOCountrySTAT data because it is a more recent dataset. In the supplement we present the length, time-span and origin of each of the 399 selected time-series (see table  S2 ). We detrend each yield time-series to remove any signal due to low frequency variability, for example expected from long-term technological changes or low-frequency climate variability.
Climate data We compute four annual precipitation variables based on the CHIRPS precipitation dataset. CHIRPS merges satellite information on cloud temperatures and rain gauge data to estimate daily precipitation from 1981 to 2020 at a 0. 05 • × 0. 05 • resolution. We only use grid cells where at least 1% of the areas are cultivated (see supplements section A for more detail on the method used) to compute the aggregated climate variables of interest at the administrative level 1. We consider (a) the yearly sum of precipitation based on the calendar year (mm -1 ); (b) monsoon length, defined as the number of days between onset and retreat days. The onset monsoon day is defined as the day following a sequence of rainy events as in  [45]. If the onset day is estimated to have occurred after the 1 of October, it is filled out as missing value by default.
Note that both of the above-mentioned methods chosen to estimate SDR confidence intervals do not rely on Gaussian assumptions. Next, we estimate SDR for each of the five alternative crops considered in our study over the totality of West Africa based on the following random-effect model: log ( SDR i,j ) = µ i + b j + e ij ( 3 ) where µ i is the mean log of SDR for crop i (i. e. cassava, maize, millet, sorghum or yams) and b j is a random regional effect and e ij is the residual error. Model (  3 ) is fitted to the data using the method REML implemented with the function lmer of the package lme4 of R  [47]. During the fitting process, the values of SDRs are weighted by their variances relying on  [46]. The fitted model is used to estimate the mean log variability ratio of rice to each alternative crop species.
The estimated log ratios are then back transformed to estimate the variability ratios. The uncertainty is described by computing the 95% confidence interval. Rice to alternative crops yield variability are considered significantly different when the confidence intervals do not include one. To assess the role played by irrigation in offsetting the effects of monsoon variability, we identify areas where irrigated rice is predominant (i. e. superior to 80% of the total rice area) according to the SPAM2000 dataset  [48]  (see supplement section B for details). We then assess the impact of 12 climate indices on relative rice yield variability (i. e. the SDR) in the areas where rainfed systems (i. e. lowland and upland) are predominant. We also identified the areas where irrigation is predominant according to two other datasets, namely MIRCA 2000 and GAEZv3, and compared the results obtained with these two alternative datasets.
This sensitivity analysis does not reveal any substantial difference (see supplements section B). When yield data for several alternative crops is available in one given area, several SDR can be computed (i. e. one per crop species). A random regional effect is included to relax the assumption that SDR common to one area are independent. We test possible effects of the climate indices on the 261 SDR altogether based on a model with all the alternative crops together and location fixed effects: log ( SDR i,j ) = α + βx j + b j + e ij ( 4 ) where α and β are fixed parameters (common to all species), x j is one of the 12 climate indices measured in area j, b j is a random regional effect and e ij is the residual error. Model (  4 ) is fitted to the data using the method REML implemented with the function lmer of the package lme4 of R  [47].
Note though, that our initial data treatment and selection procedure addresses such possible heterogeneities (see section 2). We also compared our dataset with data from alternative sources to assess the consistency of our yield standard deviations estimates. We relied on simulated rice and maize from a global gridded yield dataset  [50]  and qualitatively compared the distribution of yield variability estimated from these data to (a) the ones estimated from our administrative level 1 data and (b) national estimates from USDA [51] and FAOSTAT [16] datasets (see figure  S15 ). We find that the mean standard deviations measured from  [50]  are similar to the ones estimated with administrative level 1 data when grouped by countries. Yield variability distribution estimated from  [50]  noticeably tends to be narrower.
Finally, SDRs are based on normalized detrended yields. Note that detrending or the detrending method chosen has little to no effect on our estimates since the time-span of yield timeseries is rather short (see table  S2  and figure  S4 ). Climate indices are also associated with uncertainties in particular regarding observed precipitation datasets for observation-poor regions such as West Africa  [54, 55]. Satellite retrievals are useful in that context, especially when corrected with in situ observations, but also present some challenges  [56]. Different datasets can be used in order to characterize observational uncertainties  [57] , but there is no other available dataset at the high spatial and temporal resolution needed in this study. The estimated yield variability differences we find here may be due to the fact that the areas encompassed by rice cultivation cover the entire West African sub-continent, i.
Topographic conditions (i. e. plateaus, hydromorphic slopes, valley bottom, floodplains, rivers, lagoons and deltas) cropping systems (in particular water management practices) and agronomic factors (e. g. fertilizers, pesticides or crop cultivars) certainly explain part of these differences. The effects of these factors may be direct (e. g. precipitation accumulation in valley bottom) or indirect (increased yield average and variance through fertilization). Note that results with and without areas with predominant irrigation reveal negligible to small effects on our conclusions. Geographically explicit information on the use of fertilizers and pesticides would obviously be needed to formally test the response of yield variability to increased input use. A unified database at the scale of West Africa would be very relevant to precisely evaluate the direct and indirect effects of agronomic practices on the relative stability of rice.
Such a database may be built from, national and subnational statistics, field or farm scale surveys and quantitative expert elicitation. Our results suggest that, at constant production systems, expanding West African rice production may impact the stability of the caloric supply mix produced in this region. This stability may be enhanced or hindered depending on regional stability and climatic specificities. For example, rice production may be enhanced in the areas where rice yield stability is significantly higher. Similarly, we have shown that alternative food crops may, for some speciesregions combinations, improve the relative stability of the regional caloric supply, including when climatic conditions are less favorable. In terms of imports, the three biggest rice suppliers of West Africa (i.
Acknowledgments This work benefited from the  French  state aid managed by the  ANR  under the 'Investissements d'avenir' programme with the reference  ANR-16-CONV-0003. This work was supported by the  metaprogram Adaptation of Agriculture and Forest to Climate Change (AAFCC)  of the  French National Research Institute for Agriculture, Food and Environment (INRAE). The authors thank  Damien Beillouin  for his help in analyzing fractions of the data.
, 2004; Zeh et al. , 2009)  point to a shortlived emplacement, between 3110 and 3100 Ma, for the GMS suite. This study focuses on the Heerenveen batholith, the smallest and best studied of the GMS suite. For comparison, we also include whole-rock data as well as some zircon analyses from the north-eastern margin of the adjacent Mpuluzi batholith.