dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analyses We tested the main and interactive effects of the plant diversity treatment (low versus high diversity), the landscape context (proportion of semi-natural habitats), and the distance to the center of the cover crop treatment (0, 15, 30 m) on the abundance, richness and community composition of two guilds of natural enemies (soil and vine) and on pest predation rates. We used the proportion of semi-natural habitats as a proxy of landscape complexity  (Muneret et al. , 2018). The proportion of semi-natural habitats resulted from the sum of proportions of forests, meadows and fallows. We used the regional database PIGMA (http://portail. pigma. org) and calculated the proportion of different habitat types in a 1000 m radius around each site using ArcGIS 10. 1 (ESRI). We used generalized linear mixed effect models (GLMMs) to analyze abundance and taxonomic richness of natural enemies and biological pest control potential with the R package lme4  (Bates et al.
ACKNOWLEDGEMENTS This study was partly funded by  Europe H2020 program Societal Challenges  ( H2020-SFS-2014 , Grant No. 633999 ). L. B. was funded by the research project  SECBIVIT , which was funded through the  2017-2018 Belmont Forum and BiodivERsA joint call for research proposals , under the  BiodivScen ERA-Net COFUND  programme, with the funding organisations:  AEI/Spain ,  BMBF/Germany ,  ANR/France ,  NWO/Netherlands ,  UEFISCDI/Romania ,  FWF/Austria  (Grant number  I 4025-B32 ) and the  NSF/USA  (Grant # 1850943 ). We thank the vineyard managers " Les vignerons de Tutiac " for hosting the experiment and  Olivier Boissières  for facilitating.
2016). Our methodology is outlined in Section 3. Section 4 describes the foreground lens sample and Section 5 gives a brief description of the weak lensing data used in this paper. The various methodologies used to compute ∆Σ are described in Section 6. Section 7 presents tests on the homogeneity of the BOSS samples. Our results are presented in Section 8 and discussed in Section 9. Section 10 presents a summary and our conclusions. We use a flat ΛCDM cosmology with Ωm = 0. 3, H0 = 70 km s -1 Mpc -1. We assume physical coordinates to compute ∆Σ 8. 8 see Appendix C in  Dvornik et al. (2018).
General approach and limitations In this paper, we use weak lensing data from CS82, CFHTLenS, HSC, KiDS, SDSS, and DES. A description of these data are given in Section 5. For the lens sample, we select a common set of lenses from BOSS (see Section 4). Figure  1  displays the footprints of different surveys considered in this paper. Table  1  gives the overlap between BOSS and various lensing surveys 9. Currently, apart from the SDSS lensing catalogue, the overlap between BOSS and existing lensing surveys is typically of order 100-200 deg 2 , however, this overlap will rapidly expand over the next few years to reach of order ∼1000 deg 2. One of the main assumptions behind our methodology is that the lens sample selects a homogeneous sample of dark matter halos across the BOSS footprint. However, there may be inhomogeneity in the BOSS lens sample.
Computation of ∆Σ Prior to computing ∆Σ we agreed that all teams would compute the signal under the following set of assumptions: (i) A fixed fiducial cosmology (as given in Section 1). (ii) A fixed radial binning scheme. We use 10 logarithmically spaced bins from 0. 05 Mpc to 15 Mpc. (iii) Physical transverse distances are used for the computation of ∆Σ. (iv) Data points are compared at the mean r value of the bin (see justification below), where r is a physical transverse radius. This value is the same for all surveys. (v) The lens and random files provided to each team correspond to the intersection between the BOSS footprint and the footprint of each shear catalogue. (vi) Our fiducial test uses systematic weights that are applied to lenses to ensure that the spatial variations of the lenses follow those of the randoms (see Section 4.
CFHTLenS CFHTLenS analysed 172 square degrees of imaging data from the wide component of the CFHT Legacy survey (ugriz imaging to a 5σ point source limiting magnitude of iAB = 25. 5). The observing strategy reserved the best seeing (seeing < 0. 8") conditions for the lensing i-band filter, the primary object detection filter, and followup with the other bands in the poorer seeing conditions. The data reduction for CFHTLenS was conducted with the THELI pipeline  (Schirmer et al. 2004; Erben et al. 2005)  following the procedures outlined in  Erben et al. (2013). The dataset shares a similar data processing pipeline to KiDS, where the shape measurement of galaxies was conducted using the lensfit model fitting code  (Miller et al. 2013). Shear multiplicative bias terms were characterised as a function of the signal-to-noise ratio and galaxy size using image simulations, thereby allowing for the calculation of the multiplicative bias term for an arbitrary selection of galaxies.
2019). With an effective, unmasked area of 360 deg 2 , this dataset has an effective number density of n eff = 6. 93 galaxies arcmin -2. Galaxy shapes were measured from the r-band data using a self-calibrating version of lensfit  (Miller et al. 2013; Fenech Conti et al. 2017). A weight, ws, is also assigned based on the quality of the shape measurement. Utilising a large suite of image simulations, the multiplicative shear bias was deemed to be at the percent level for the entire KiDS ensemble  (Kannawadi et al. 2019). The redshift distribution for KiDS galaxies was determined via four different approaches, which were shown to produce consistent results in a cosmic shear analysis  (Hildebrandt et al. 2020). The preferred method of that analysis, used here, is the 'weighted direct calibration' (DIR) method, which exploits an overlap with deep spectroscopic fields.
DES The DES survey conducted its first year of survey operation (Y1) between  August 31, 2013 and February 9, 2014 (Drlica-Wagner et al. 2018 ) from the 4-meter Blanco Telescope and the Dark Energy Camera  (Flaugher et al. 2015). DES Y1 covers two noncontiguous areas near the southern galactic cap: The "SPT" area (1321 deg 2 ), which overlaps the footprint of the South Pole Telescope Sunyaev-Zel'dovich Survey  (Carlstrom et al. 2011) , and the "S82" area (116 deg 2 ), which overlaps the Stripe-82 deep field of the Sloan Digital Sky Survey (SDSS;  Annis et al. 2014). Each area within these footprints was revisited three to four times to reach sufficient photometric depth in the four griz DES bands. In this paper, we only use the S82 area which overlaps with BOSS. For the DES Y1 data two independent shape catalogues were created: METACALIBRATION  (Sheldon & Huff 2017; Huff & Mandelbaum 2017) , and IM3SHAPE  (Zuntz et al.
Each source galaxy has quantities denoted P phot and F phot which indicate what kind of redshift it was primarily trained on (e. g. photo-z, spec-z, grism-z). By computing the gg-lensing signal with various values of P phot and F phot ,  Speagle et al. (2019)  showed that the gg-lensing signals are stable with respect to the origin of the training redshifts. To compute the uncertainty of the ∆Σ signal, lens and random samples are grouped into 41 roughly equal-area sub-regions. A N = 10000 bootstrap re-sampling is used to estimate errors for ∆Σ. The weighted mean redshifts of the four lens samples are zL = 0. 23, zL = 0. 36, zL = 0. 49, zL = 0. 59. The code used to compute ∆Σ (DSIGMA) is publicly available at https: //github. com/johannesulf/dsigma. The systematic error is estimated to be of order 5% (roughly Gaussian and 1σ).
6). This test will help to determine if unknown systematic effects are present in the data. Unless mentioned otherwise, all of the results in this section were blinded according to the scheme presented in Section 3. 3. It was agreed before un-blinding that any homogeneity trends in the lens samples greater than 3σ would be discussed. Our lensing signals and code used to make the main figures are available at https://github. com/ alexieleauthaud/lensingwithoutborders.
ACKNOWLEDGEMENTS Author contributions: The authorship list reflects the two lead authors (AL, AA), those who measured the lensing signals and contributed key ideas to the paper (SS, DG, UL, SH, NR, TV, YL, CH, CB) followed by an alphabetical group. This paper has gone through internal review by the  DES, HSC , and KiDS collaborations. We acknowledge use of the lux supercomputer at  UC Santa Cruz , funded by  NSF   MRI grant   AST 1828315. This material is based on work supported by the  U. D Department of Energy, Office of Science, Office of High Energy Physics  under Award Number  DE-SC0019301. AL acknowledges support from the  David and Lucille Packard foundation , and from the  Alfred. P Sloan foundation. CH acknowledges support from the  European Research Council  under grant number  647112 , and support from the  Max Planck Society  and the  Alexander von Humboldt Foundation  in the framework of the  Max Planck-Humboldt Research Award  endowed by the  Federal Ministry of Education and Research.
Based in part on observations at  Cerro Tololo Inter-American Observatory at NSF's NOIRLab (NOIRLab Prop. ID  2012B-0001 ; PI:  J. Frieman ), which is managed by the  Association of Universities for Research in Astronomy (AURA)  under a cooperative agreement with the  National Science Foundation. The DES data management system is supported by the  Na-tional Science Foundation  under Grant Numbers  AST-1138766  and  AST-1536171. The DES participants from Spanish institutions are partially supported by  MICINN  under grants  ESP2017-89838 ,  PGC2018-094773 ,  PGC2018-102021 ,  SEV-2016-0588 ,  SEV-2016-0597 , and  MDM-2015-0509 , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. Research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Program  (FP7/2007-2013) including  ERC  grant agreements  240672 ,  291329 , and  306478.
Typical objective of an agile organization is to adapt specific product team demands by • designing an approach which fosters agility of product teams by keeping compliant; • fostering lean governance by reference/base to the source requirements. Various different more or less suitable solution approaches to implementing compliance governance exist: • A central governance unit for security, privacy, Free/Open Source Software (FOSS) compliance etc. These organizations tend to establish one big governance framework to address all (edge) cases. This may lead to a one-size-fits-all approach, bureaucracy, and frustration in agile teams. • Local Governance units for domain-specific instances of governance like ISMS. This kind of organization tends to multiply efforts for compliance in all phases of the life cycle with plan, build, run the local governance instantiation.
Moreover, security testing (like pen-testing or vulnerability assessments) has to be performed in a repeatable way and documented. The accountable and qualified person for this task is authorized to perform/supervise the reviews. The reviews are conducted by independent persons in intervals (18. 2. 1) and aligned with the security policies and standards (18. 2. 2). The developers using cryptographic libraries need authorizations that are aligned with the product usage taking into account e. g. encryption restrictions in China or export restrictions from the European Union e. g. to North Korea (18. 1. 5). These controls have to be instantiated by the software development organization and teams. The Intellectual Property (IP) rights (18. 1. 2) have to be ensured with appropriate procedures. Free/Open Source Software (FOSS) and proprietary licenses have to be fulfilled.
1. 5) is ensured. Furthermore, the developers establish a knowledge base from the analysis and resolving learnings of security incidents (16. 1. 6). Vulnerabilities. Vulnerability management is established to obtain technical vulnerabilities, act timely and risk-appropriate (12. 6) Legal. The developers using cryptographic libraries need authorizations that are aligned with the product usage taking into account e. g. encryption restrictions in China or export restrictions from the European Union e. g. to North Korea (18. 1. 5). The Intellectual Property (IP) rights (18. 1. 2) have to be ensured with appropriate procedures. Free/Open Source Software (FOSS) and proprietary licenses have to be fulfilled. An appropriate asset registry has to be maintained, in particular a Software Bill of Material (SBOM), which includes a listing of the reputable sources.
Art. 5 §2 The controller shall be responsible for, and be able to demonstrate compliance with, paragraph 1 ('accountability'). Art. 6 §1 Processing shall be lawful only if and to the extent that at least one of the following applies: a) the data subject has given consent to the processing of his or her personal data for one or more specific purposes; b) … Art. 6 §4 Where the processing for a purpose other than that for which the personal data have been collected is not based on the data subject's consent … the controller shall, in order to ascertain whether processing for another purpose is compatible with the purpose for which the personal data are initially collected, take into account, inter alia: a) any link between the purposes for which the personal data have been collected and the purposes of the intended further processing; b) the context in which the personal data have been collected, in particular regarding the relationship between data subjects and the controller; c) the possible consequences of the intended further processing for data subjects; d) the existence of appropriate safeguards, which may include encryption or pseudonymization.
Personal data shall be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes (Art. 5) -keep in mind to have consent for data usage in the context of testing, bugreproduction and training of Machine Learning (ML) algorithms. The request for data subject's (user/person) consent shall be presented in a manner which is clearly distinguishable from the other matters, in an intelligible and easily accessible form, using clear and plain language. Any part of such a declaration which constitutes an infringement of this Regulation shall not be binding. Be able to demonstrate the consent of a person to legitimate the data processing (Art. 7). Where the processing for a purpose other than that for which the personal data have been collected is not based on the data subject's consent (like for ML training data or test data) the controller (developer/tester in the testing context) shall, in order to ascertain whether processing for another purpose is compatible with the purpose for which the personal data are initially collected, take into account, inter alia: period of their storage and their accessibility (Art.
Participants The IMAGEN project collected neuroimaging data at eight sites (France, United Kingdom, Ireland, Germany) at age T1 and T2. The participants underwent the procedures described below at T1 and T2. After the quality check on neuroimaging and behavioral data, there were 1304 participants (female: 681; mean and standard deviation age: 14. 44 ± 0. 41 years; modal Pubertal Development Scale pubertal development status: female = 4-advaned pubertal, male=3-midpubertal) at T1 and 1241 participants (female: 639, mean and standard deviation age: 19. 09 ± 0. 76 years) at T2 included in the study. There were 787 participants in both T1 and T2. The project was approved by all local ethics research committees, and informed assent/consent was obtained from participants and their parents/guardians.
, 2010). The effect of MRI site was also controlled by adding it as a nuisance covariate in all statistical analyses. 2. 6. fMRI data processing 2. 6. 1. Preprocessing Analyses were performed using Statistical Parametric Mapping software package SPM12 (Wellcome Trust Centre for Neuroimaging, London). Time-series data were first corrected for slice-timing, then corrected for movement, non-linearly warped onto MNI space using a custom EPI template, and Gaussian-smoothed at 5 mm-full width half maximum.
Software and visualization fMRI data processing and network construction were performed using MATLAB (version 9. 6. 0; Natick, Massachusetts: The MathWorks Inc) and SPM12. GRENTNA was used to calculate graph-based measures  (Wang et al. , 2015). The longitudinal analyses were performed using R (version 3. 6) and lmerTest  (Kuznetsova et al. , 2017). MRIcronGL was used for brain data visualization  (Rorden and Brett, 2000). Open-visualization (https://github. com/jorvlan/open-visualizations) and R package "interaction" were used for data visualization. R package "emmeans" were used for post-hoc analysis. Computations were performed, in part, on the Vermont Advanced Computing Core.
Discussion In the present study, we employed neuroimaging data from a largescale longitudinal study to examine reward system maturation from early adolescence to young adulthood. Voxel-wise LME models revealed  decreased brain response in subcortical regions such as bilateral caudate, VS, thalamus, midbrain but increased brain response in the frontal cortex (e. g. , IFG, MFG) during reward anticipation at T2 compared to T1. Increased functional connectivity with VS and a more topologically efficient task-modulated functional network during reward anticipation were found. In the exploratory analysis comparing participants who engaged in relatively high alcohol use at T2 with those who did not, significant interactions between alcohol use and time (T1 vs. T2) were found in functional connectivity between right VS and left MTG as well as the characteristic shortest path length of the taskmodulated network.
Declaration of Competing Interest The authors declare the following financial interests/personal    ≤2 ) at T1 but a high AUDIT score (≥8) at T2 were designated as the high alcohol use group (HA; orange). Those who had a low AUDIT score at both T1 (≤2) and T2 (≤7) were designated as the low alcohol use group (LA; blue). Each dot represents one participant with their T1 and T2 data connected by lines. Middle: The interaction results between time (T1 vs. T2) and alcohol group (LA vs. HA) for the functional connectivity between left middle temporal gyrus and right VS. To visualize this effect, functional connectivity within a spherical ROI with a 5-mm radius centered at the MNI coordinate of the left middle temporal gyrus was extracted and compared using the LME model. Right: The interaction results for the characteristic shortest path length.
Acknowledgments This work received support from the following sources: the  National Institute on Drug Abuse (NIDA)  grant  R01DA047119 ; the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313),  Human Brain Project  ( HBP SGA 2 ,  785907 , and  HBP SGA 3, 945539 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute of Health (NIH)  ( R01DA049238 , A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers; and  R01MH085772 ,  Axon, Testosterone  and  Mental Health during Adolescence) , the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B ;  Forschungsnetz IMAC-Mind   01GL1745B ), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-2 ,  SFB 940 ,  TRR 265 ,  NE 1383/14-1 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ), the  National Institutes of Health (NIH)  funded ENIGMA (grants  5U54EB020403-05  and  1R56AG058854-01 ).
Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004 ,  AAPG2019 -GeBra ), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe ), the  Fondation de France  (  00081242 ), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ), the  Mission Interministérielle de Lutte -contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013 ), the  Fédération pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ), and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence, the  National Institute for Health Research (NIHR) Biomedical Research Centre (BRC ) at  South London and Maudsley NHS Foundation Trust (SLaM)  and  King's College London (KCL).
The dominant fish species are dab (Limanda limanda), plaice (Pleuronectes platessa), and herring (Clupea harengus)  [36, 37]. (b) Fish survey and ecological traits Fish abundance data were acquired through the International Bottom Trawling Survey (IBTS; https://datras. ices. dk/Data_ products/Download/Download_Data_public. aspx), a fishery monitoring campaign conducted in the North Sea every year in January-February since 1983. For this monitoring, the North Sea is gridded in survey cells of 1°longitude by 0. 5°latitude (as defined by the International Council for Exploitation of the Sea ICES), hereafter referred to as 'ICES rectangles'. Two 30-min hauls are performed in each ICES rectangle every year with a grand opening bottom trawl (GOV; 10-mm stretched mesh size in the codend) towed at an average speed of 4 knots.
For each haul, all captured individuals are counted and identified at the most accurate taxonomic level possible. Over the entire North Sea and study period, the majority of taxa (116) were identified at species level and the remaining taxa (  14 ) at genus level (hereafter, the term 'species' is used to refer to both levels of taxonomic identification). Species' abundances were standardized to numbers of individuals per km 2. The final fish abundance dataset included 130 species across 154 ICES rectangles over 33 years, thus providing a table with 130 columns and 4924 rows (some years did not include all 154 survey cells). Fish ecological traits expected to be implicated in the response to environmental changes and/or fishing pressure were collected from public databases  [39] [40] [41]  (electronic supplementary material, table  S1 ).
(d) Vulnerability of functionally distinct species We obtained species' IUCN (International Union for Conservation of Nature) statuses  [45]  using the rredlist R package  [46]  and compared the statuses across functional distinctiveness groups. The IUCN Red List classifies species according to their risk of extinction, based on criteria such as the rate of decline in population size, geographical range, number of mature individuals, and fragmentation of species distribution  [45]. We performed χ 2 -tests of royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 288: 20201600 homogeneity to compare the proportion of IUCN statuses between distinctiveness groups. Species vulnerability to fishing is defined as the inherent capacity to respond to fishing pressure based on species maximum growth rate and strength of density dependence  [47].