dataesr/scientific-paragraphs-categorization huggingface dataset:
This first step is an important step before performing any hydrological analysis or using the available datasets in real time flow forecasting  (Levy et al. , 2017). In our study we found that the uncertainty, as measured by the standard deviation of annual precipitation errors, had the highest values in the CPC dataset for most of the basins. This result agrees with the finds of  Rozante et al. (2010) , when comparing the performance of TRMM-MERGE product with the gauge based product OBS90, showing a superior quality of the MERGE product over the gauge-based CPC product. When the precipitation data sources were combined, the new dataset displayed remarkably similar annual uncertainty to the one evaluated from the source which had the smallest standard deviation values. However, the uncertainty variability among the river basins was reduced in the combined dataset and became more similar to the one displayed by the reference dataset we used in this study, the non-real time MSWEP precipitation dataset.
This result is in line with the results obtained by  Beck at al. (2017) , where the authors reported the superior quality of their merged product (based on satellite, rain gauges and reanalysis) when the product and the individual sources were evaluated against ground observations of precipitation. The data source presenting the lower standard deviation of annual precipitation errors is usually the one with the higher weight when combining the daily precipitation data. The proposed method uses the annual uncertainty to define the blending weights but blends the precipitation datasets at a daily time step on the river basin scale. Therefore, the reduction of uncertainty that results from the combination of individual datasets also impacts the quality of the precipitation at shorter time scales.
At the annual scale, large errors of each data source were smoothed during the combination process in several cases (15 out of the 41 basins), and the resultant precipitation dataset displayed lower standard deviation of annual precipitation errors. At the daily scales, where the hydrological models are usually calibrated and validated for hydrological applications such as streamflow forecasting and climate change impact assessment, the performance was better for practically all river basins, reflecting lower uncertainty from the combination of precipitation data sources also at this shorter time step. These results are the first indication that the methodology proposed for the combination of the different sources of data can lead to a precipitation forcing dataset that is more robust and display lower uncertainty at different time scales.
When calibrating the hydrological models, we generated nine simulations, three for each precipitation forcing (TRMM-MERGE, CPC, and their combination). Following the traditional split-sample test, there were two calibration-validation procedures applied each to half of the sample. Additionally, one calibration covered the entire data period of almost twenty years. The results were evaluated using performance indicators (NSE, KGE, MAE, and R 2 ) and it was shown that, for most of the basins, the results in terms of performance of simulated flows are similar among the different periods. This enabled the use of the hydrological model as a tool for the selection and validation of the precipitation datasets, including the combined precipitation dataset. This can be an interesting strategy when the focus of a precipitation dataset analysis is its specific use for hydrological applications, as, for instance, in seasonal streamflow forecasting, where the traditional method of ESP (Ensemble Streamflow Prediction) for issuing reliable hydrological forecasts requires long series of homogeneous historic precipitation data.
Wong et al. (2021)  also illustrated the interest of evaluating precipitation products in terms of streamflow simulations. They showed that, at the scale of a large river basin in Canada, the best precipitation product evaluated against the precipitation-gauge stations did not necessarily display the best streamflow performance across the sub-catchments of the river basin. The authors also highlighted the benefit of using streamflow as it presents smaller uncertainties when compared to other hydrological information that can be derived from a hydrological model. Our results are also in agreement with the conclusions drawn by  Beck et al. (2017 Beck et al. ( , 2019)) , which evaluated a large group of observed precipitation data sources and concluded that the combination of data sources into one reanalysis dataset provided better estimates of precipitation.
The unique MSWEP (Multi-Source Weighted-Ensemble Precipitation) dataset of global terrestrial precipitation dataset provides a high-resolution (3-hourly temporal and 0. 1 • spatial resolution) reanalysis dataset. While it was used in our study as a reference dataset to evaluate (and validate) the combined dataset built, its direct use for real time operations is not possible given the fact that this dataset it is not yet processed to be available in real time. Combined precipitation datasets are particularly useful when water managers have to operate over large areas, from tens to hundred square kilometers, and cannot compile different data sets in real time for each river basin under their management. The use of a unique and robust precipitation dataset is an asset in operational settings and continental-wide applications.
The fact that our combined dataset performs well compared with the MSWEP dataset also opens the opportunity for the future use of the MSWEP dataset when it will become available in real time. The performance results of this study can be compared with other large-scale model experiments in terms of overall performance. For the NSE criterion, our results show the performance is higher than 0. 60 for 97 % of the basins and the average NSE over the study area is 0. 75. The KGE criterion is also higher than 0. 60 for all basins and the average value is 0. 77. The average coefficient of correlation is 0. 67 for the study basins. Siqueira et al. (2018) , using a distributed hydrological model (MGB) and the MSWEP data from  Beck et al. (2017)  as precipitation forcing, obtained NSE values for discharge and water levels higher than 0.
60 for 55 % of the studied cases and KGE values higher than 0. 60 in 70 % of the studied cases of streamflow simulation. According to the authors, the global models (WaterGap, LISFLOOD, and HTESSEL/CaMaFlood) showed more than 40 % of the basins in South America with an NSE and a KGE lower than zero. When comparing the HEC-HMS models calibrated in this study with the other experiments realized in South America, we can see that the results and the performance of the HEC-HMS models are robust and sufficient for the next step of the study, which is the application of the combined precipitation dataset and the calibrated model for seasonal streamflow forecasting. The combined precipitation dataset has a similar evolution of precipitation uncertainty (standard deviation of annual areal precipitation errors) as the reference MSWEP dataset, notably in the periods before late 1990 s and early 2000 s.
The median values are however higher (generally, 1 % higher), as well as the variability of standard deviation of annual areal precipitation errors among river basins. Although differences in median values are more important in the period 2005-2010, median values become comparable in the more recent period. Despite having more information available in the recent period, since satellite data is added to the TRMM-MERGE dataset, the uncertainty of the combined precipitation dataset is not reduced, as can be seen in the MSWEP dataset, which presents a decrease in the variability of the standard deviation of the errors among the river basins in the period after 1998. These differences can be explained by the fact that the MSWEP dataset uses more information and more consistent data than real time datasets.
Errors and inconsistencies are often present in real time datasets, such as the ones used to build the combined dataset in this study, so it is expected that this impacts the quality of the precipitation data. Despite these issues, an important aspect of the combined precipitation dataset is that it provides a more homogeneous dataset over the region, since no regions in the study area displayed specific patterns of errors. Patterns were identified previously in the CPC and TRMM-MERGE precipitation datasets  (Reis et al. , 2019) , with differences between the datasets growing toward the northwest of Brazil. After the combination of the datasets, the uncertainty did not show any clear dependency on the spatial location of the river basins. The TRMM-MERGE dataset used in this study was discontinued in May 2020, and now CPTEC provides a merge product called GPM-MERGE, based on the GPM satellite product  (Rozante et al.
, 2018; Skofronik-Jackson et al. , 2018). It uses the new IMERGE retrieval algorithm, which "fuses the early precipitation estimates collected during the operation of the TRMM satellite  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015)  with more recent precipitation estimates collected during the operation of the GPM satellite (2014 -present)" (https://gpm. nasa. gov/data/imerg). The new CPTEC GPM-MERGE dataset maintains the same gage stations used at the TRMM-MERGE product and the same MERG algorithm  (Rozante et al. , 2010) , which gives preference to the use of the station data over the satellite information  (Rozante et al. , 2020). In their study comparing the old product TMPA-V7, based on the TRMM mission, with the new products IMERGE and the GSMaP-G from JAXA,  Rozante et al.
(2018)  found that the IMERG-E and TMPA-V7 show a similar behavior in terms of Critical success index (CSI), Adjusted Equitable Threat Score (ETS), Probability of Detection (POD), False Alarm Ratio (FAR) and Bias, with a better performance for IMERGE. These characteristics make the new GPM-MERGE the natural substitute of the discontinued TRMM-MERGE, with similar behavior, but with a better performance and higher resolution of 0. 1 • x 0. 1 •. Due the similarity of the products, we believe the results obtained in our study with the TRMM-MERGE will remain valid with the use of the new GPM-MERGE (available after May 2020). The same is valid for the MSWEP dataset  (Beck et al. , 2019). The dataset used in this study is the product version V2. 2, with daily precipitation data and a 0. 1 • spatial resolution.
A comparison of the combined precipitation generated from the methodology proposed in this study with the reference MSWEP, our benchmark dataset, showed that the combined dataset has a level of uncertainty comparable with the benchmark. The examples of simulated flows showed that the calibrated models can correctly represent the long-term flow variations in different regions and climates in the study area. The MSWEP product is however in constant evolution. At the time of writing, its current version V2. 8 (MSWEP V2. 8 2021) provides data with daily and 3 hourly time resolutions and with the same 0. 1 • spatial resolution. As mentioned earlier, the complete version is still not available in real time, due to the latency of some of the products used in its production (MSWEP, 2021). However, this latency is reducing year after year, and we believe that in the next years we will have new real time products.
Conclusions In this study, a sequence of steps was described that can be used to blend different real-time precipitation datasets, validate the results, and obtain a better near real-time observed precipitation forcing dataset for river basins in South America (Brazil and neighboring countries). The methodology proposed allowed us to build a long historical period of precipitation estimates at the river basin scale, which can be used in future hydrological studies such as streamflow forecasting. The main conclusion of this study is that the use of hydrological data and modeling is an asset to combine and validate precipitation datasets from different sources at large river basins in views of providing a blended dataset for hydrological applications in places where rain gauges are scarce or non-existing.
Our study showed that a combination of existing precipitation datasets, weighted by the annual uncertainty of each original source, reduces precipitation uncertainty at the river basin scale. The methodology proposed allowed us to adapt, for each river basin, the proportion of each precipitation source to be considered in the combined dataset, taking thus into account the specific hydroclimatic characteristics of each river basin. The uncertainty reduction was also observed when we analyzed the results at the daily time step, with better results in terms of daily hydrological simulation when using the combined dataset than when using each precipitation source individually. A drawback of combining data sources is the fact that datasets are often not available for the same period. In this study, we show that a possible practical solution is to extend the period of the combined precipitation dataset to cover the longest possible period, given the original datasets, by using the double-mass curve correlation.
The validation of such extension was also illustrated using a hydrological model. The model allows users to evaluate, in terms of simulated discharges, if the performance of a precipitation dataset in the extended period, where not all data sources are available, remains similar and consistent to the one in the original period, where all data sources were available to build the combined precipitation dataset. Hydrological modeling proved to be a useful tool to evaluate the performance of different sources of precipitation data, as also suggested recently by  Levy et al. (2017) , who highlighted the importance of the problem of "data selection uncertainty" when analyzing nine datasets in Brazil. Ongoing research focuses on the different uses of the combined precipitation dataset generated in this study.
Acknowledgments The first author acknowledges  CEMIG -Energy Company of Minas Gerais (Companhia Energética de Minas Gerais S. A. ) -Brazil  for funding the research and the international visits and thanks the different institutes contributing to the research ( UFMG, Brazil ;  INRAE, France ;  ECMWF, UK ;  Deltares , The Netherlands). We also acknowledge the data providers: NOAA -US; ONS -Brazil; CPTEC -Brazil; MSWEP -US.
66 0. 34 31) HPP MANSO 198 266 200 0. 57 0. 43 32) HPP TELES_PIRES 87 190 102 0. 69 0. 31 33) EBEC 69 171 68 0. 71 0. 29 34) HPP TAPAJOS 89 340 106 0. 79 0. 21 35) PUERTO_SILES 72 81 73 0. 53 0. 47 36) PENA_AMARILLA 152 136 139 0. 47 0. 53 37) MIRA_FLORES 148 105 110 0. 41 0. 59 38) INC_GUAJARA_MIRIM 93 93 89 0. 50 0. 50 39) INC_ HPP_SANTO_ANTONIO 111 131 104 0. 54 0. 46 40) HPP STO_ANTONIO_DO_JARI 228 257 137 0. 53 0. 47 41) HPP FERREIRA_GOMES 250 461 271 0. 65 0. 35 less river basins with low values of KGE. For individual river basins showing the lowest values of KGE with the combined dataset, the differences regarding the KGE with the TRMM-MERGE are lower than 8 %.
Alternatively, while assuming the fulfillment of the drag-free requirements along any axis, the effect of a coarse attitude control has been studied. For this purpose, in-flight data from the GFO's attitude have been employed (from ref. [32] ) and fed into the performance model obtaining the performance shown in Figure  19. Note that, in principle, this scenario is independent of orbital altitude since the triaxial drag-free control capability is retained. This kind of coarse attitude control produces, in particular, a variation of the satellite pitch/yaw angles in the measurement bandwidth which translates into a transformation error degrading the ΔaD measurement performance by about two orders of magnitude around 1 mHz independently on the orbit altitude. A similar degradation is produced by the lack of drag-free control on the lowest orbit considered for the NGGM scenario.
Alternatively, while assuming the fulfillment of the drag-free requirements along any axis, the effect of a coarse attitude control has been studied. For this purpose, in-flight data from the GFO's attitude have been employed (from ref. [32] ) and fed into the performance model obtaining the performance shown in Figure  19. Note that, in principle, this scenario is independent of orbital altitude since the triaxial drag-free control capability is retained. This kind of coarse attitude control produces, in particular, a variation of the satellite pitch/yaw angles in the measurement bandwidth which translates into a transformation error degrading the ∆a D measurement performance by about two orders of magnitude around 1 mHz independently on the orbit altitude. A similar degradation is produced by the lack of drag-free control on the lowest orbit considered for the NGGM scenario.
Methods Consensus exercise conducted through a two-day workshop on the ethical barriers to precision psychiatry held in February 2021. The workshop was organized by the Prevention of Severe Mental Disorders (PSMD) Cluster and funded by the European Brain Research Area (EBRA),  ( Morris et al. , 2016 )  building on two existing Networks of the European College of Neuropsychopharmacology ECNP (Prevention of Mental Disorders & Mental Health Promotion and Bipolar Disorders). Workshop participants were selected to reflect a multidisciplinary professional background, including key leaders from psychiatry, oncology, big data research, genomics, imaging, clinical implementation, ethics, philosophy, and healthcare policies. Individuals with lived experiences of mental disorders or their carers/family members [from the Global Alliance of Mental Illness Advocacy Networks-Europe (GAMIAN, https://www.
Search strategy and selection criteria A multistep literature search was performed for articles published from inception until January 1 st , 2022. First, the Web of Science database (Clarivate Analytics) was searched, incorporating the Web of Science Core Collection, BIOSIS Citation Index, KCI Korean Journal Database, MEDLINE, Russian Science Citation Index, and SciELO Citation Index. Independently, PubMed was searched. Several combinations of the following key words and their acronyms were used: ethic * , precision psychiatry, artificial intelligence, bipolar disorders, psychosis. Key papers were critically (non-systematically) selected based on the topic covered and the quality of research. We supplemented these publications with earlier landmark papers and studies conducted in developing countries.
, 2019 ). Health disparities contribute to algorithmic biases  ( Walsh et al. , 2020 ) : datasets used to train, test, and validate the models are too often insufficiently representative, under/oversampling individuals with diverse ancestries or vulnerable subgroups (e. g. the disproportionate number of diagnoses of schizophrenia in ethnic minorities)  ( Leslie et al. , 2021  ) (Panel 2), leading to higher error rates for members of marginalized communities  ( Leslie, 2020 ). Care should be taken to ensure that clinical prediction models do not discriminate in risk assessment, in particular with respect to sociodemographic data (e. g. ethnicity is a socially constructed set of categories), which are much more interpretive. Another issue relates to the so far neglected dimension of generalisation capacity is the between-sex applicability and age-dependency of prediction models.
These concerns should be addressed by the implementation of strict data governance and security policies that comply with the local regulations (eSupplementary 3), and can be achieved through early and sustained multidisciplinary interactions with individuals and regulatory bodies. These best methods and techniques to achieve data security and privacy, informed consent management, maintaining the trustworthiness of data and adhering to legal regulations are common to other areas of precision medicine and have already been discussed (eSupplementary 4)  ( Thapa and Camtepe, 2021 ). Specific ethical concerns apply to EHR databases, which often report indirect measures; the data often do not directly reflect the health of the patient but also clinicians' and patients' interactions with the system.
Acknowledgements We would like to thank the  European Brain Council (EBC)  and the  European Brain Research Area  staff for their support in coordinating the organisation of this meeting. The Prevention of Severe Mental Disorders (PSMD) cluster is supported by the  European Brain Research Area  project. EBRA has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  825348. The views expressed in this article are the personal views of the authors and may not be understood or quoted as being made on behalf of or reflecting the position of the  European Medicines Agency  or one of its Committees or working parties.
g. FAST Track Principles) ( Leslie et al. , 2021 ) Generalisability of precision psychiatry models is poorly Foster collaborative replication science against tested, in particular in specific/vulnerable subgroups deeply-phenotyped federated databases with a specific focus on vulnerable groups The real-world value of precision psychiatry is unclear because Foster implementation research (early identification of a few models are currently implemented implementation barriers, demonstrated cost-effectiveness and scalability, establishing an integrated infrastructure for model refinement, updating and comparability) Citizens are not well prepared to deal with risks for mental Fostering mental health literacy in order to consolidate disorders self-determination and the alliance between stakeholders People vary in terms of disclosure preferences Ask whether/how much the users want to be shared the results of precision psychiatry; conduct interpretive research; address the issue of reporting incidental findings.
The second was from a spatial point of view, where we searched for interconnections between traditions from contemporaneous settlements and the 'Petit-Chasseur' necropolis, ultimately seeking to identify constellations of practice. To do so, we based our study on the revised chronological sequence of the region for the 3300-1600 BCE time frame, which included the full numerical and typological data from the settlements' ceramic assemblages  (Carloni et al. , 2020). We also linked domestic and funerary contexts, bringing together the pottery technological data from the settlements and the data from the necropolis' assemblage published in the study by  Derenne, Ard, and Besse (2020). The ceramic technical data of the Upper Rhône valley assemblages reveal, from 2500 BCE onwards, the appearance of new traditions, together with a significant change in the way the local communities interacted with the megalithic necropolis of 'Petit-Chasseur'.
Materials and Methods: From Macrotraces to Technical Traditions Between 2017 and 2021, a large research project examined the ceramic traditions of the late fourth to the mid-second millennium BCE in the Upper Rhône valley (Switzerland). Raw material procurement and selection  (Carloni, 2022)  were investigated separately from the later steps of the pottery chaîne opératoire, such as forming, surface treatment, and decorationhereafter referred to as "pottery technology"  (Derenne, 2021). A first joint publication already combined a portion of the two datasets to offer insights into the Bell Beaker assemblage of the 'Petit-Chasseur' necropolis  (Derenne, Carloni, & Besse, 2022) , and a full synthesis should be proposed once both datasets have been published in detail. The present article constitutes the third and final stage of the complete study on the pottery technology of the area, aiming at investigating the integration of the Bell Beaker phenomenon in this local context.