dataesr/scientific-paragraphs-categorization huggingface dataset:
The future period is divided into two time slots: 2021-2050 for the near future and 2071-2100 for the far future. Monthly data are used for all variables calculated from the model and reference datasets. The annual means are obtained by averaging the monthly values and the Mean is obtained by averaging over the three RCMs, which are also averaged over the 3 GCMs. The model evaluation of the RCM is conducted for target variables (Tcell, PVP, and WPD) and further variables (WSPD, Tas, and RSDS) relevant for the calculation of the target variables (Section 2. 3) using common performance metrics, such as the root mean square error (RMSE), the mean absolute error (MAE), and the correlation coefficient r using the Equation (  8 ),  (9) , and  (10) , respectively. RMSE = n ∑ i=1 (P i -O i ) 2 n (8) MAE = 1 n n ∑ i=1 (|P i -O i |) (9) r = ∑ n i=1 (O i -O)(P i -P) ∑ n i=1 (O i -O) 2 ∑ n i=1 (P i -P) 2 ( 10 ) where P is the RCM data, O the reference data at timestep i, and n the number of data points.
O and P are the mean value of the reference and RMC data, respectively. The validation of the model output focuses on important climate features (e. g. , annual patterns and cycle) in addition to temporal statistics using diagnostic tools such as the Taylor Diagram. Relative to the reference period, the changes in future PVP and WPD over the region are analyzed and no bias adjustments are applied. Indeed, bias requires reference data that reflect the true weather of the location  [82]. The reference datasets used in this study are subject to some biases that were pointed out in previous studies  [83, 84]. A recent study by Dieng et al. [85]  showed that the change signals of wind speed and solar irradiance are largely preserved after bias correction over West Africa. The projected changes in temperature are estimated as the difference between the future and reference periods (absolute change).
In this study, the agreement of the RCMs is designated by the colored areas and the disagreement by uncolored or white areas. For the validation of the models, a comparison of the model outputs and the reference datasets (ERA5 and SARAH-2) is conducted. Figures  3  and 4  show the simulated and observed spatial patterns of the annual averages for all the variables of this investigation, for the reference period 1971-2000. The RCMs and the ensemble mean show a relatively good representation of the spatial distribution of the different variables. All spatial correlations are greater than 0. 8. Despite this performance of the RCMs, there are some pronounced biases. A high bias of PVP and WPD is noticed for all the single RCMs and especially for the Sahel zone. There is a bias ranging from -300 to 150 kWh m -2 for PVP and 50 to 150 W m -2 for WPD, with CCLM having a strong underestimation of -300 kWh m -2 of PVP in the Guinea sub-region.
However, for WSPD, while we find an overestimation of about 2 m s -1 over West Africa, results from the studies of Sawadogo et al. [16]  and Ogunjobi et al. [17]  present a similar overestimation, but only for Guinea and Savannah, and underestimation for the Sahel. Model biases can be related to many factors. These biases could arise from both the driving GCM and the RCM, but differences between the GCMs are usually larger than those between the RCMs, except for coastal areas and regions with complex topography  [89]. This study used the CORDEX-CORE datasets, which provide high-resolution regional climate information. Some comparison studies between the global climate models (CMIP5, CMIP6) and regional climate models (CORDEX, CORDEX-CORE) showed a better performance of the CORDEX-CORE in simulating drought and rainfall indices over the region  [25, 27].
I The wind power projection shows a predominant increase over West Africa, with a projection of about 20 to 40% by the ensemble mean and for the two future periods. The RCMs convey more consistency in the projection of the other climate variables than the wind speed and the wind power density. The latter is subject to some divergences, with REMO expecting a decrease, RegCM and Mean an increase, and CCLM a high increase. The CORDEX-CORE provides, in a consistent manner, a high-resolution dataset of RCM projections for West Africa. However, the current projections are only based on three RCMs, as they are the only modelling groups that participated in the CORE initiative (to date). Therefore, a relatively small ensemble of RCM projections is used to determine the inherent uncertainty of climate projections.
To reduce the uncertainties in the climate projections, more simulations should be available from the CORDEX-CORE, similar to the CORDEX data (~50 km). Another limitation of this work is that we only consider the RCP8. 5 scenario. The results of this study could be improved by considering other scenarios like the RCP2. 6 and 4. 5 scenarios. Future studies could also assess the robustness of the projected change in wind and solar energy from CORDEX-CORE over the West Africa region. Nevertheless, the study shows useful results, which are an important contribution to research on the future changes in solar PV and wind energy potential over West Africa. The results of this study can help governments and policymakers in targeting RES for.
Funding: This publication was produced with the financial support of the  German Ministry for Education and Research (BMBF)  through the  West African Science Service Center on Climate Change and Adapted Land Use (WASCAL)  and the  Prince Albert II of Monaco Foundation. The contents of this document are solely the liability of  Aissatou Ndiaye  and under no circumstances may be considered as a reflection of the position of the  Prince Albert II of Monaco Foundation  and/or the IPCC. The work was also done as a part of the  ENERSHELF  (Grant number:  03SF0567D ) project funded by the  BMBF.
Data Availability Statement: The data presented in this study are openly available at https:// cordex. org/data-access/esgf/ (1 October 2022); https://www. ecmwf. int//en/forecasts/datasets/ reanalysis-datasets/era5 (1 October 2022); https://wui. cmsaf. eu/safira/action/viewDoiDetails? acronym=SARAH_V002_0 (1 October 2022).
Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries.
MATERIALS (SUBJECTS) AND METHODS Model development study population EOC is a highly heterogeneous phenotype with five major histotypes for invasive disease-high-grade serous, low-grade serous, endometrioid, clear cell, and mucinous histotype. The mucinous histotype is the least common and its origin is the most controversial with up to 60% of diagnosed cases of mucinous ovarian cancer often being misdiagnosed metastasis from nonovarian sites  [11]. Therefore, in this study, we performed PRS modeling and association testing for all cases of invasive, nonmucinous EOC. We used genotype data from 23,564 invasive nonmucinous EOC cases and 40,138 controls with >80% European ancestries from 63 case-control studies included in the Ovarian Cancer Association Consortium (OCAC) for model development.
The distribution of cases by histotype was high-grade serous  (13, 609) , low-grade serous  (2, 749) , endometrioid  (2, 877) , clear cell  (1, 427) , and others  (2, 902). Sample collection, genotyping, and quality control have been previously described  [12]. Genotype data were imputed to the Haplotype Reference Consortium reference panel using 470,825 SNPs that passed quality control. Of the 32 million SNPs imputed, 10 million had imputation r 2 > 0. 3 and were included in this analysis.
Model validation study populations We validated the best-fitting PRS models developed in the OCAC data in 657 prevalent and incident cases of invasive, nonmucinous EOC and 198,101 female controls of European ancestries from the UK Biobank. Samples were genotyped using either the Affymetrix UK BiLEVE Axiom Array or Affymetrix UK Biobank Axiom Array (which share 95% marker content), and then imputed to a combination of the Haplotype Reference Consortium, the 1000 Genomes phase 3 and the UK10K reference panels  [13]. We restricted analysis to genetically confirmed females of European ancestries. We excluded individuals if they were outliers for heterozygosity, had low genotyping call rate <95%, had sex chromosome aneuploidy, or if they were duplicates (cryptic or intended)  [12]. All SNPs selected in the model development phase were available in the UK Biobank.
We investigated transferability of the best-fitting PRS models to populations of non-European ancestries using genotype data from females of East Asian and African ancestries genotyped as part of the OCAC OncoArray Project  [14, 15]. Women of East Asian ancestries-2,841 non-mucinous invasive EOC and 4,828 controls -were identified using a criterion of >80% Asian ancestries. This included samples collected from studies in China, Japan, Korea, and Malaysia as well as samples collected from women of Asian ancestry in studies conducted in the US, Europe and Australia  [14]. Similarly, women of African ancestries-368 cases of nonmucinous invasive EOC and 704 controls-mainly from studies conducted in the US, were identified using a criterion of >80% African ancestries as described previously  [15].
We also assessed the performance of the best-fitting PRS models in women of European ancestries (>80% European ancestries) with the pathogenic BRCA1 and BRCA2 variants from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). We used genotype data from 18,915 BRCA1 (2,053 invasive EOC cases) and 12,337 BRCA2 (717 invasive EOC cases) pathogenic variant carriers from 63 studies contributing to CIMBA  [16]. Genotyping, data quality control measures, intercontinental ancestries assessment and imputation to the HRC reference panel are as described for the OCAC study population.
We used a two-stage approach to reduce computational burden without a corresponding loss in predictive power. The first stage was a SNP selection stage using a sliding windows approach, with 5. 5 Mb data blocks and a 500 kb overlap between blocks. SNP selection was performed for each block and selected SNPs were collated. Single SNP association analyses were then run, and all SNPs with a χ 2 test statistic of less than 2. 25 were excluded. The 2. 25 cutoff was arbitrary and selected to maximize computational efficiency without loss in predictive power. Penalized regression models were applied to the remaining SNPs using λ values of 3. 0 and κ values of 0. 0, 0. 2, 0. 4, 0. 6, 0. 8 and 1. 0. SNPs selected in any of these models were included in subsequent analyses. In the second stage, we fit penalized regression models to the training dataset with λ values ranging from 3.
Select and shrink using summary statistics (S4) The S4 algorithm is similar to the PRS-CS algorithm  [19] -a Bayesian method that uses summary statistics and between-SNP correlation data from a reference panel to generate the PRS scores by placing a continuous shrinkage prior on effect sizes. We adapted this method with penalization of rarer SNPs by correcting for the standard deviation resulting in the selection of fewer SNPs. We varied three parameters, a, b, φ, which control the degree of shrinkage of effect estimates. Φ, the overall shrinkage parameter, is influenced by values of a which controls shrinkage of effect estimates around 0 and b which control shrinkage of larger effect estimates. We generated summary statistics for each crossvalidation training set and selected the parameters that gave the best results on average from the cross-validation and applied these to the set of summary statistics for the complete OCAC data set to obtain the final set of weights.
Polygenic risk score performance The best lasso, elastic net, stepwise and S4 models from the model development stage were validated using two independent data sources: the UK Biobank data and BRCA1/BRCA2 pathogenic variant carriers from the CIMBA. In the UK Biobank data, we evaluated discriminatory performance of the models using the AUC and examined the association between standardized PRS and risk of non-mucinous EOC using logistic regression analysis. For the CIMBA data, we assessed associations for each version of the PRS and invasive non-mucinous EOC risk using weighted Cox regression methods  [20]. PRSs in the CIMBA data were scaled to the same PRS standard deviations as the OCAC data, meaning that per standard deviation hazard ratios estimated on CIMBA data are comparable to PRS associations in the OCAC and UK Biobank data.
Model development The results for the models based on individual level genotype data are shown in Table  1. The elastic net model had the best predictive accuracy (AUC = 0. 586). The optimal value of λ obtained from regularization paths for the MCP model was 3. 3 meaning the best MCP model was equivalent to the lasso model. The best-fitting model based on summary statistics was the S4 (AUC = 0. 593) and the LDPred model had the poorest performance of the methods tested (AUC = 0. 552). Therefore, the LDPred model was not considered for further validation in other datasets. All SNPs selected and the associated weights for each model are provided in Supplementary Tables  1 2 3 4 5 6.
DATA AVAILABILITY OncoArray germline genotype data for the OCAC studies have been deposited at the European Genome-phenome Archive (EGA; https://ega-archive. org/), which is hosted by the EBI and the CRG, under accession EGAS00001002305. Summary statisitics for the Ovarian Cancer Association Consortium are available in the NHGRI-EBI GWAS catalogue (https://www. ebi. ac. uk/gwas/home) under the accession number GCST90016665. A subset of the OncoArray germline genotype data for the CIMBA studies are publically available through the database of Genotypes and Phenotypes (dbGaP) under accession phs001321. v1. p1. The complete data set will not be made publically available because of restraints imposed by the ethics committees of individual studies; requests for further data can be made to the Data Access Coordination Committee (http://cimba.
The impact of trade agreements has also been studied  (Guillin, 2013; Marchetti, 2011). Nordås (2018)  finds that free trade agreements boost services trade by 75% and single-market trade by an additional 45%. Second, the development of databases compiling information on service regulations allows researchers to rely on direct evidence from applied regulations to estimate the effect of policies' restrictiveness on trade and ad valorem equivalents (AVEs). Based on the trade restrictiveness index produced by the Australian Productivity Commission,  Walsh (2008)  calculates the tariff equivalents of the barriers to services trade using a gravity framework and finds the highest tariff equivalents for the developing countries. Fontagné and Mitaritonna (2013)  compute AVEs for regulations in three sectors applied by emerging countries and find a negative impact on the price margins using data provided by Queen Mary University  (Findlay & Warren, 2000).
This database gathers qualitative information on the restrictions applied by each country in different sectors. Their results indicate the strong negative impact that the regulations can have, as more than half of the AVEs are larger than 50%. The third source of trade restrictiveness in services is developed by the World Bank. Jafari and Tarr (2014)  estimate the AVEs for 11 sectors and 103 countries using this source. Van der Marel and Shepherd (2013)  and  Lu (2018)  use a dyadic version of the World Bank index, allowing for the possibility that the regulations applied by both the exporter and the importer can affect trade flows. They find a negative impact on both bilateral services trade and on exports. Gooris and Mitaritonna (2015)  preserve the discrete nature of the data and evaluate restrictiveness through four levels, from totally open to totally closed.
This method allows them to underline the nonlinear impact on services trade, as they find that minor restrictions, in comparison to restriction-free environments, foster trade in certain sectors. Borchert et al. (2020)  use a joint World Bank-WTO STRI database to measure the evolution of trade restrictions. They find higher income economies to be more open in general than emerging economies but they observe sector heterogeneity. The fourth source of trade restrictiveness is produced by the OECD. Benz and Jaax (2020)  relies on these STRIs to estimate their impact on trade and calculate AVEs for 5 services sectors and 46 countries. Their results confirm the negative effect services trade barriers have, using cross-section and panel database. Using the same source, Nordås and Rouzet (2017) find a negative and significant impact of STRI on both exports and imports, illustrating the importance of having an open and procompetitive regulatory environment to strengthen the international competitiveness of service exporters.
DATA Bilateral and domestic trade data come from the World Input Output Database (WIOD). Trade flows are averaged over the period 2012-2014 to smooth any year-specific shock. The STRI database comes from the OECD. This dataset provides information on regulations affecting trade in services in 22 sectors across all OECD member countries as well as Brazil, China, India, Indonesia, Malaysia, the Russian Federation, and South Africa, representing over 80% of global trade in services. The policy measures are categorized as five policy areas: (1) restrictions on foreign ownership and other market entry conditions, (2) restrictions on the movement of people, (3) other discriminatory measures and international standards, (4) barriers to competition and public ownership, and (5) regulatory transparency and administrative requirements.
Even if non-discriminatory measures might represent barriers to trade, they might have also been implemented for prudential or social objectives, that is: producers in the distribution services sector, the pre-packaging of products is subject to mandatory nominal quantities. Thus, non-discriminatory measures are not accounted for, in the context of this analysis. Composite indices measure the identified restrictions across five standard policy categories, and their value ranges between 0 and 1. Complete openness to trade and investment obtains a score of zero, while being completely closed to foreign services providers is associated with a score of one. However, discrepancies in terms of sectoral classification, countries and time span between WIOD and the OECD STRI database constrain the matching between these two sources.
11 The sample is reduced to eight sectors-construction, professional services, telecommunications, air transport, maritime transport, land transport, financial services, and logistics services. 12 STRI database is not available before 2014 and the WIOD data is available up to 2014, which hinders the creation of a panel database over the period 2012-2014. Cross-sectional analysis presents some limitations compared to a panel data analysis. First, the recent development of bilateral trade database, such as the OECD-WTO services trade database would allow me to estimate the impact of STRI on trade over the period 2014-2018 with exporter-time and importer-time fixed effects. However, the non-availability of domestic trade flows does not allow me to control for exporter-sector-time and importer-sector-time fixed effects, consistent with structural gravity equation.
Indeed, as explained by  Yotov (2021) , the introduction of domestic trade flows in the dependent variable allows the estimation of non-discriminatory policies in a model in line with theory with the correct set of fixed effects. The introduction of importer-sector and exporter-sector fixed effects is consistent with the definition of inward and outward multilateral resistance terms, which are at the heart of general equilibrium analysis of trade policy. Second, panel data structure would also allow me to control for any country-pair trend, through the use of bilateral country fixed effect. To control for this possible bias, I add several bilateral trade cost determinants along with the level of restrictiveness in each country-sector. The trade cost determinants used in the regression, namely, bilateral and intranational distance, common language, colonial link and contiguity, come from the CEPII GeoDist database, developed by  Mayer and Zignago (2012).
I control for the presence of a trade agreement covering trade in goods or in services between the two countries using the dynamic gravity dataset collected by  Gurevich and Herman (2018). Several empirical studies demonstrate that broad aspects of governance institutions are relevant factors that shape the effects of policy measures reducing trade barriers  (Beverelli et al. , 2017; Ing et al. , 2019) , I thus use a legal origin variable developed by  Porta et al. (1999). Third, longitudinal data would also allow me to observe services trade liberalization across countries and sectors. However, across years, sectors and countries, the discriminatory STRI value does not vary a lot (Figure  A3 ). 13.
Descriptive statistics The STRI database contains information on the policies affecting international trade in services. As I use the sub-index focusing only on discriminatory measures, the restrictiveness index is equal to 0 for domestic trade. This different value of the restrictiveness depending on the provenance of the flow, allows me to estimate the effect of STRI, in a correctly specified gravity equation with importer-sector fixed effects. The database covers (i) core measures that are common for all sectors, such as the foreign equity share limit, and (ii) sector-specific measures that capture the characteristics of each sector. For instance, in the professional services sector, the licensing system and related procedures are captured in great detail. The STRI scoring methodology uses binary scores.
Beverelli et al. (2017)  found that decreasing services trade restrictiveness has a positive indirect impact on manufacturing sectors that use services as intermediate inputs in production. The welfare gains from service liberalization could thus be higher if trade in goods were included in the model. I leave to further research to explore this mechanism in a "global" model including trade in both goods and services and discussing changes at the global economy level. 11 The countries available in the dataset are listed in Table  A2. 12 The STRI for the land transport sector is the average value of rail freight transport and road freight transport. 13 The mean discriminatory STRI value is 0. 18 across the 5 years, with a standard deviation of 0. 09. 14 Each member country nominated an expert, and in addition, several international organizations covering the sector in question were invited.
ACKNOWLEDGMENTS I am indebted to  Yoto Yotov , and three anonymous referees for insightful comments and suggestions. The author is grateful to  Peter Egger ,  Gabriel Felbermayr ,  Lionel Fontagné ,  Alessandro Nicita ,  Gianluca Orefice  and participants to GSIE & ifw Kiel seminars, Deep FTA workshop, TIISA & FIW conferences for useful comments and suggestions.