dataesr/scientific-paragraphs-categorization huggingface dataset:
[5] , but less restrictive than the one made in ref. [22]. It allows for many data points with |P hT | Q but also with 0. 2 Q < |q T | < Q. In section 4, we will discuss variations of the baseline SIDIS cut in eq. (3. 2) that give phenomenological support to our choice. As for the datasets included in the present analysis, the main difference with ref. [5]  is that we include the new release of COMPASS data  [68]. In this dataset, the vector-boson contributions have been subtracted. For the HERMES dataset we consistently select the vector-meson-subtracted dataset (. vmsub set). Moreover, we select the zxpt-3D-binning for HERMES multiplicities, since it presents a finer binning in |P hT |. The breakdown of the entire SIDIS dataset included in the present analysis is reported in table 3. The second column of table  3  shows the number of data points (N dat ) that respect the kinematic cuts for each dataset, with a total number of 1547 data points.
Error treatment The considered experimental datasets are released with a set of systematic and statistical uncertainties. As already pointed out in ref. [7] , a proper treatment of the experimental uncertainties is extremely important in order to obtain a reliable extraction of TMDs. Thus, we choose to treat systematic uncertainties as fully correlated only if, in the corresponding publication, it is explicitly specified that they are correlated. The statistical uncertainties, instead, are always considered as uncorrelated. At variance with ref. [7] , in this analysis we do not make use of the iterative t 0prescription  [97]  for the treatment of correlated normalization uncertainties. This prescription is usually introduced to avoid the underestimation of the predictions caused by the so-called D'Agostini bias  [98].
JHEP10(2022)127 4 Results In this section, we present the results obtained for the extraction of unpolarized quark TMDs from a global analysis including both DY and SIDIS data (see section 3). This work represents an important upgrade with respect to ref. [7]. In fact, in the SIDIS process a single hadron is measured in the final state, allowing us to extract information about fragmentation functions. Therefore, the final result of this work is the simultaneous extraction of both TMD PDFs and FFs at N 3 LL -. At the moment, this is the most precise extraction of TMDs that has been achieved on more than two thousand data points. In section 4. 1 we present the quality of the fit, in section 4. 2 we discuss the extracted TMD distributions, and in section 4. 3 we investigate the effect of variations on the baseline fit configuration.
These values should be very close to each other. Table  4  reports the breakdown of the χ 2 0 values normalized to the number of data points (N dat ) for DY and SIDIS datasets. As already discussed in ref. [7] , in the presence of bin-by-bin correlated uncertainties the total χ 2 can be expressed as the sum of two contributions χ 2 = N i exp i -th i σ i 2 + χ 2 λ = χ 2 D + χ 2 λ , ( 4. 1) where χ 2 D is given by the standard formula for N experimental data points exp i and statistical and uncorrelated uncertainties σ 2 i = σ 2 i,stat + σ 2 i,uncor , but involving theoretical predictions shifted by the correlated uncertainties, th i = th i + k α=1 λ α σ (α) i,corr , (4. 2) where σ (α) i,corr is the α-th (100%) correlated uncertainty associated with the i-th experimental data point and λ α is the so-called nuisance parameter.
In eq. (4. 1), the χ 2 λ is a penalty term due to the presence of correlated uncertainties and is completely determined by the JHEP10(2022)127 nuisance parameters, χ 2 λ = k α=1 λ 2 α. (4. 3) The optimal value of the nuisance parameters is obtained by minimizing the total χ 2 in eq. (  4. 1) with respect to them. Because the shifted predictions in eq. (4. 2) provide a better visual assessment of the fit quality, we will consistently display them for all observables used in the fit. From table 4, the global χ 2 0 value is 1. 06, indicating that the description of the whole dataset is very good. 12 This means that the fit is able to simultaneously describe experimental data coming from two different processes over a wide kinematic range. As can be seen in tables 2 and 3, the low-energy dataset comes from fixed-target DY experiments and SIDIS observables, while the high-energy dataset comes from collider experiments at the LHC and Tevatron at energies higher by more than two orders of magnitude.
SIDIS For HERMES and COMPASS multiplicities, which represent about 75% of the total number of data points considered in this work, the description obtained by our global analysis is very good. From table  4 , the values of χ 2 0 /N dat are almost always smaller than 1. In the case of HERMES multiplicities, we note that the largest contribution to the χ 2 0 comes from the π + channel, for both proton and deuteron targets. This result is consistent with the findings in both refs. [5, 15]  and  [22]. Since kaon multiplicities are affected by larger statistical errors, and the collinear FFs for kaons display large uncertainties, the corresponding χ 2 0 value is lower. The comparison between theoretical results for the SIDIS multiplicities of eq. (2. 30) and HERMES data for the production of charged pions and kaons off a deuteron target is shown in figure  4.
Drell-Yan DY data represents approximately 25% of the full set of analyzed data. From table  4  it is evident that most of low-energy DY data from fixed-target experiments (E605, E288, E772), but also from PHENIX and STAR, can be fitted with low χ 2 values, much lower than high-energy DY data from collider experiments like, e. g. , those at the LHC. As already pointed out in ref. [7] , this most likely originates from the fact that low-energy DY data are affected by larger errors and collinear PDFs at these kinematics have larger uncertainties. From table  4 , we also note that the quality of our fit for the ATLAS datasets is poor. In particular, the description worsens for the first two low-rapidity bins of both ATLAS 7 TeV and ATLAS 8 TeV datasets, the worst case being at |y| < 1 for ATLAS 7 TeV.
Several effects might be responsible for this result. Since the experimental observable is a normalized cross section, systematic errors cancel in the ratio producing measurements with very small error bars. Fitting these data is very difficult, also because small theoretical effects can give significant contributions to the χ 2. Moreover, different implementations of phase-space cuts on the final-state leptons could lead to modifications in both the shape and the normalization of the theoretical observable (see, e. g. , refs. [32, 105, 106] ). We  leave this issue for future studies. At variance with ref. [22] , we obtain our results without excluding any extra data points on top of the ones exceeding the maximum value of |q T |/Q in eq. (3. 1). It is interesting to comment the results of the fit for those datasets that were not included in the previous analysis of ref.
[7]  (see section 3). For E772, we are able to obtain a good description only for data points above the peak of the Υ resonance. For Q < 9 GeV, the quality of the fit worsens. At variance with ref. [22] , we keep the Q < 9 GeV bins because there is no evident motivation to exclude them. The new CMS dataset at √ s = 13 TeV is important because it extends the kinematic coverage considered in ref. [7]. This dataset is very nicely described, even better than the ones at the smaller center-of-mass energies √ s = 7, 8 TeV. This is probably due to the fact that the CMS 13 TeV dataset is densely binned in rapidity. In order to visualize the quality of our fit, in figures 8-11 we present the comparison between experimental data and theoretical results for a representative selection of the DY dataset.
In the upper panels of each plot, we display the cross section differential in |q T |, while in the lower panels we show the ratio of data to theory. As for the SIDIS case, the light-blue bands are the 68% CL theoretical results. Figure  8  displays the DY cross section for the E288 dataset at beam energy E beam = 200 GeV for different bins in Q. For DY fixed-target observables, we calculate the cross section at mean values of |q T | (not integrating upon the bin limits). Hence, we display the 68% CL uncertainty as a band rather than a series of rectangles. We remark that JHEP10(2022)127 the uncertainty band is larger for lower Q bins; this trend is induced by larger correlated uncertainties for smaller invariant masses of the lepton pair. In figure  9 , we compare the theoretical results for the cross section for DY in pp collisions at the Tevatron.
As in previous figures, the lower panels display the ratio of experimental data to the 68% CL theoretical results. For √ s = 7, 8 TeV, the quality of the fit is comparable to that in ref. [7]. For the new dataset at √ s = 13 TeV, the agreement in the displayed rapidity bin is excellent, but remains very good also for higher rapidities. In figure  11 , we compare the theoretical results for the DY cross section in pp collisions normalized to the fiducial cross section for the ATLAS data at √ s = 7 TeV. From left to right, we consider three representative bins at increasing rapidity |y|. The leftmost one corresponds to the worst described bin in our global fit, with χ 2 /N data = 13. 5. As in previous figures, the lower part of each panel displays the ratio of experimental data to the 68% CL theoretical results.
To this aim, we perform a new global fit at NNLL. However, when lowering the perturbative accuracy, it is possible to obtain acceptably good fits only by excluding those datasets whose precision requires the highest theoretical accuracy. Specifically, we found that only by removing the ATLAS dataset we were able to achieve an acceptable global description at NNLL accuracy. As a matter of fact, in table 6 the value of χ 2 in this configuration, namely for fixed-target DY and SIDIS, is lower than at N 3 LL -where ATLAS data is included. Because of the difference in the perturbative accuracy as well as in the dataset, we do not expect to get compatible values for the best fit parameters between the NNLL and N 3 LL -fits. For instance, we obtain λ = 12 ± 10 GeV -1 and λ F = 340 ± 280 GeV -2 at NNLL, to be compared to λ = 1.
Global fit at NLL We performed also a global analysis at NLL accuracy. Similarly to the NNLL fit discussed above, it might be useful to have also a global fit at NLL accuracy for contexts where it is not possible to reach the highest accuracy. However, in order to obtain acceptable χ 2 values at this order, we had to exclude all collider DY data and the E772 fixed-target DY dataset. Thus, we reduced the dataset to the SIDIS data and the remaining fixed-target DY data, i. e. , E605 and E288. We point out that in our approach the integral of the W -term at NLL is equal by construction to the SIDIS q T -integrated collinear cross section. As a consequence, the value of the prefactor ω in eq. (2. 52) is automatically equal to 1. Moreover, for this fit we consistently used MMHT2014 at LO for the collinear PDFs and we used DSS at NLO for collinear FFs.
As shown in table 1, at NLL both collinear PDFs and FFs should be evaluated at LO, but we choose FFs at NLO because no recent extractions at LO are currently available. As can be seen in table  6 , we obtain low χ 2 values for all included datasets. It is interesting to compare this result with that of the PV17 analysis  [5]. In that work, the normalization of COMPASS data was fixed by the first bin in P 2  hT. Here we demonstrate that we can obtain an excellent description of the most recent COMPASS data without any adjustment of the normalization.
Cut in |q T |/Q A crucial ingredient of any phenomenological analysis of TMDs is the introduction of the kinematic cut |q T |/Q 1 on the dataset to ensure that TMD factorization is valid. Our default choices are discussed in section 3. It is interesting to study how the global quality of our fit changes upon variations of this cut in order to get quantitative information on the range of validity of TMD factorization. To this purpose, we changed the parameters c 1 , c 2 , and c 3 in eq. (3. 2) devised for the SIDIS data, while keeping the constraint |q T |/Q < 0. 2 for the DY data. We refer the reader to ref. [7]  for an analogous study of the effect of the |q T |/Q cut on the DY data. We consider five different configurations for the cut on SIDIS data: (e) A fifth cut inspired by the PV17 analysis  [5] , namely the same as in the previous case but with c 1 = 0.
3 < Q < 1. 73 GeV, 0. 02 < x < 0. 032, 0. 3 < z < 0. 4. The upper panel of the plot displays the multiplicity while the lower panel shows the ratio of experimental data to the theoretical predictions. The solid circles indicate data points included in the fit while empty squares refer to those that do not survive the cut. Remarkably, the agreement remains very good up to |P hT |/Q 0. 5, well beyond the largest |P hT | allowed by the cut. We also stress that this behavior is not specific of the considered bin but is a general feature also of other bins, as well as of the HERMES dataset. In conclusion, from our analysis it emerges that the validity of the TMD formalism in the kinematic region covered by COMPASS and HERMES seems to extend well beyond the customary cut |q T |/Q 1. This evidence justifies in a quantitative way our choice for the cut |q T |/Q in eq.
We constructed the full TMDs by combining the perturbative components, regularized by means of the b * prescription defined in eq. (2. 33), and nonperturbative parts described as a sum of Gaussians and weighted Gaussians (see eqs. (2. 38 ) and (2. 39)). We used 21 free parameters: 11 related to the TMD PDF, 9 for the TMD FF, and one for the Collins-Soper kernel driving the TMD evolution. We assumed these parameters to be the same for all quark flavors. The TMD formalism is applicable only if observed transverse momenta are much smaller than the hard scale Q of the considered process. The details of our data selection JHEP10(2022)127 are explained in section 3. In the DY case, for the final lepton pair transverse momentum q T we adopted a simple criterion, |q T | < 0. 2 Q, which is common to other works in the literature.
4. The fit is performed with the bootstrap method, leading to an ensemble of 250 replicas. We reach a very good overall agreement with data, with χ 2 /N dat = 1. 06 for the central replica (defined in section 4. 1). The description of all individual datasets is satisfactory, except for a few cases, in particular for the ATLAS data (see table  4  and figure  11 ). The discrepancy with the ATLAS data may be due to corrections not fully included in our analysis (see, e. g. , ref. [32] ) which, in spite of being small, can have a large impact in the comparison with very high precision data. The resulting TMDs are shown in figures 13 and 14. It is interesting to note that they deviate from a simple Gaussian shape (especially for the FFs) and the TMD shape changes in a nontrivial way as a function of x or z.
Acknowledgments We thank  Mariaelena Boglione ,  Osvaldo Gonzalez Hernandez ,  Emanuele Nocera ,  Ignazio Scimemi ,  Alexey Vladimirov  for stimulating discussions. This work is supported by the  European Union 's  Horizon 2020 programme  under grant agreement No. 824093  ( STRONG2020 ). AS acknowledges support from the  European Commission  through the  Marie Skłodowska-Curie Action SQuHadron  (grant agreement ID: 795475). CB is supported by the  DOE  contract  DE-AC02-06CH11357.
A set of secondary analyses will be adjusted for covariates of clinical interest using generalized linear mixed regression models. Discussion: This will be the first study evaluating the effect of an SDM intervention on patient adherence in BD. This is also an innovative protocol because it proposes the development of an evidence-based tool that should help patients and clinicians to initiate discussions regarding the use of BD treatment. Trial registration: The study has been registered with ClinicalTrials. gov as NCT03245593.
Allocation procedures We will randomly assign centers (cluster randomization) to either the intervention arm (SDM centre) or the control arm (CAU centre). Randomization will be performed using SAS software (SAS Institute, Cary, NC, USA) by the methodologist of the study (LB). Given the study nature, participants, practitioners and investigators cannot be blinded. For this study, practitioners will assure follow-up and care while investigators will be carrying out regular assessments.
Data collection In this study, offered care and its assessment will be considered as two different processes carried out by distinct professionals: practitioners (psychiatrists delivering care) and evaluators (psychiatrists carrying out assessment). This method will provide sufficient time for practitioners to use the SDM tools: the decision aid and the personal decision-making guide. Practitioners will include BD patients during a routine consultation. This will occur 4 weeks after the remission of mood episode. They will collect socio-demographical and clinical data: gender, age, family and socioprofessional status, level of education, age of onset of bipolar disorder, history of mood episodes (number and duration), number of hospitalizations, ongoing treatment, physical or psychiatric associated comorbidities.
Statistical analyses The data will be analysed using SPSS version 17. 0 software and SAS/STAT® version 9. 2. The primary (i. e. , adherence CRS score) and secondary (i. e. , decisional conflict, satisfaction with care, involvement in decision making, beliefs about treatment, therapeutic relationship, knowledge about information for medical decision and clinical outcomes) endpoints will be analysed on the individual patient level using cluster level summaries, which will account for the correlation between patients in the same ward. The endpoint scores will be compared between control and intervention arms. No interim analysis is planned. The primary analysis will be performed on the intentionto-treat population corresponding to all included individuals in each participating wards. A secondary analysis will be performed on the per-protocol population corresponding to all included individuals who complete the CRS questionnaire.
Acknowledgements We are grateful to the collaborators ( Pr. Frank Bellivier ,  APHP -Hôpital Fernand, Paris ;  Dr. Raoul Belzeaux ,  APHM -Hôpital Sainte Marguerite ,  Marseille; Pr. Philippe Courtet  and  Dr Emilie Olié ,  CHU Montpellier ,  Montpellier; Pr Thierry D'Amato  and  Dr. Filipe Galvao ,  CH le Vinatier, Bron ;  Pr Bruno Aouizerate  and  Dr. Sebastien Gard ,  CH Charles Perrens, Bordeaux ;  Pr Marion Leboyer ,  Hôpital Henri Mondor , Créteil;  Dr. Jean-Alexandre Lesturgeon ,  Hôpital Sainte-Marie , Clermont-Ferrand; Pr. Nicolas Authier ,  Dr. Damien Richard  and  Dr Frederic Libert ,  CHU Clermont-Ferrand, Clermont-Ferrand ;  Pr. Mircea Polosan ,  CHU Grenoble, Grenoble ) for making this research possible. We also thank  Leonie Gazel , project manager,  CHU Nîmes  and  Lorrie Lafuente  for her help in administering the study and contacting the participants.
Screening process, data collection and analysis All titles and abstracts were independently screened by two investigators (M. L. M. and C. P. ) to search for potentially eligible papers, using the literature review management software DistillerSR V R (Evidence Partners, Ottawa, Ontario, Canada). The full text of articles was systematically screened if there was no abstract available, or if the abstract was insufficiently detailed to allow a proper assessment of the eligibility criteria. All eligible full-text articles were independently screened by two reviewers (M. L. M. and C. P. ), who selected relevant articles based on our predefined inclusion and exclusion criteria. The reference list of all included articles was also screened to look for potential additional papers. One investigator (M.
Participants came from low-, middle-and high-income countries, from 12 countries and four different continents. There are, however, some limitations that need to be acknowledged. We only searched the MEDLINE database for our systematic review and we did not systematically explore the grey literature. 92 However, we did screen relevant web sites and the reference lists of all included articles. Clinimetric properties of QIs were not assessed in our study, and need to be further validated in specific settings. Clinimetric properties are characteristics of indicators such as measurability (feasibility), applicability, acceptability, reliability, potential for improvement and sensitivity to change. 10, 89, 93 Our objective was to develop a list of generic QIs that can be assessed and adapted  Quality indicators assessing antibiotic use in outpatients.
Funding This work was supported by the  Innovative Medicines Initiative (IMI) Joint Undertaking  (grant agreement no. 115618  -Driving re-investment in R&D and responsible antibiotic use-DRIVE-AB-www. drive-ab. eu). Resources are composed of financial contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and  European Federation of Pharmaceutical Industries and Associations (EFPIA)  in-kind contribution.
This chapter provides a historical overview of the construction and renewal programmes of large housing estates in Paris and its surrounding suburbs. We examine neighbourhood level data on two large housing estates to provide insight into the processes of poverty and ethnic concentration within these sites. We also examine the impact of urban renewal programmes on demographic and physical change. We argue that while the urban form of the large housing estate is gradually disappearing from the housing landscape, poverty and ethnic concentration have not disappeared, and micro-fragmentation between different social levels has become more pronounced. By including individual residential trajectories and mobilities in our analysis-and going beyond the traditional gentrification/ displacement nexus-we demonstrate that current renewal policies are at risk of creating new peripheries of exclusion and segregation at a regional level.
In the second part of the chapter, we examine neighbourhood level data from two housing estates in different suburbs of Paris, in order to provide a nuanced picture of their respective trajectories and the approaches pursued by each municipality to improve the built environment and social life. The first site, the '4000' housing estate, comprises 4000 units and is located in the suburban municipality of La Courneuve, in the northeast of Paris. The '4000' estate alone represents 26. 6% of La Courneuve's total housing stock. The second site is located in the suburban municipality of Orly, in the south of Paris, and comprises 5400 units. The estate represents 58. 6% of Orly's total housing stock. In the final part of the chapter, we draw on these cases to critically analyse the impact of urban transformation on social outcomes, highlighting common trends and diverging trajectories of change, ranging from impoverishment to micro-fragmentation.
In pursuing these questions, we come up against three major data limitations. First, in relation to housing, no official data on 'housing estates' is collected in France. Historians agree upon a general set of criteria for the grands ensembles, but it remains a rather loose definition. Though their boundaries have shifted as a result of various policy changes, our analysis will be based around the concept of 'priority neighbourhoods', as defined by France's City Policy-a social and urban policy that has specifically targeted housing estates since the 1980s. Second, in relation to individuals, comprehensive data on income levels in France has only been available since 2002, making it difficult to accurately establish trends in poverty levels over time. Furthermore, very little of this data is disaggregated to a neighbourhood level.