dataesr/scientific-paragraphs-categorization huggingface dataset:
We defined the experimental group as all study participants allocated to any of the 4-month experimental regimens and the control group as all study participants allocated to the 6-month standard regimen. The protocol for each study was reviewed and approved by ethics committees and regulatory committees described in the original publications, and all participants provided written informed consent  [1] [2] [3] 9. Data acquisition, management and harmonization. Integrated and standardized individual-level data in each of the trials were obtained through the Platform for Aggregation of Clinical TB Studies (TB-PACTS; https://c-path. org/programs/ tb-pacts/). Data sharing was directed by comprehensive data contribution agreements with sponsors. Before data were pooled, we compared trial protocols, case report forms and data dictionaries to harmonize databases.
Data queries were resolved through direct consultations with each trial team and Critical Path Institute data managers. After pooling data, data inputs were checked for missing or duplicated values, for consistency and for plausibility. Final dataset specification is available in Supplementary Tables  1  and 2 , and access to original databases is available through TB-PACTS. Data from DMID 01-009 were obtained directly from the sponsor. Further information on data acquisition and availability is described in the Life Sciences Reporting Summary. Efficacy outcomes. The primary efficacy endpoint of the pooled analysis was time to an unfavorable outcome for a maximum of 24 months after start of treatment (OFLOTUB study participants were followed until 24 months after start of treatment, and RIFAQUIN and REMoxTB study participants were followed for 18 months), as defined according to each trial protocol and described in the original publications.
Culture positivity on either medium was used for analyses, with preference for solid culture if available. Univariate Cox proportional hazard analysis for merged MGIT and LJ culture data (as described above), MGIT data only and LJ data only showed similar results in each treatment group (Supplementary Table  12 ). Treatment adherence was calculated as the number of days that doses were taken divided by the prescribed number of days. For participants with an unfavorable event during the treatment phase, the adherence calculation was adjusted for duration completed; for example, full adherence was assigned for study participants who took all doses up to the time of the event if the event appeared during treatment. Individuals with missing data between the predefined sets of predictors were excluded from the multivariate analysis (summary on analysis populations available in Supplementary Table  3 ).
For the OFLOTUB analysis (6/7 weekly dosing strategy for 24 weeks), HRs were reported for total doses of 112 to 143 (on average 5/7 doses per week) relative to 144 (on average 6/7 doses per week). We have used an arbitrarily lower cutoff of 112 total doses, as it coincides with 4 months of treatment on a 7/7 dosing strategy and most of the data were clustered above this cutoff point. We have performed sensitivity analysis with cutoffs of at least 130 (exact number of doses if participant took 5/7 doses for 26 weeks) for the REMoxTB and RIFAQUIN analysis and 120 (exact number of doses if participant took 5/7 doses for 24 weeks) for the OFLOTUB analysis. Each analysis was adjusted for study country. All data management, analyses and visualization were performed using R statistical software (version 3.
Acknowledgements The analyses conducted for this study were sponsored by the  World Health Organization  and the  Bill and Melinda Gates Foundation  ( A127340 ). Partial support for P. N. and P. P. J. P. was also received from the  US National Institute of Allergy and Infectious Diseases (NIAID ;  1R01AI104589 ). The authors alone are responsible for the views expressed in this publication, and they do not necessarily represent the decisions, policy or views of the  World Health Organization. Data used in the preparation of this article were obtained from the  Platform for Aggregations of Clinical TB Studies  (TB-PACTS; https://c-path. org/programs/tb-pacts/). TB-PACTS is a public partnership launched in May 2015 by  Critical Path Institute , the  Special Programme for Research and Training in Tropical Diseases , the  Global Alliance for TB Drug Development, and St.
Institut für Anästhesiologische Pathophysiologie und Verfahrensentwicklung, Universitätsklinikum Ulm, Helmholtzstrasse 8/1, 89081 Ulm, Germany Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Allocation In the production system, by-products are produced, such as chlorine gas (Cl 2 ) during the production of lithium from lithium chloride (LiCl) and various by-products from petroleum refining (see the Supplementary data). Since economic value is the driver of most industrial processes  [22, 23] , economic allocation based on price was applied to partition the environmental impact between products and by-products  [24] : = ∑ P n x n x i i i i i i ( 2 ) where P i is the partitioning factor of product i, n i is the mass of i produced and x i is its price. Price data (2005) were obtained from the Ecoinvent database  [21]. In addition to economic allocation, in a sensitivity analysis we apply allocation by mass and no allocation, i. e. allocating all impact to the main product. Mass allocation has been advocated because mass relationships are more fundamental than the prices of products  [24].
Data acquisition Li/S cell production data were obtained from Deng et al. [14]. For production of lithium foil, we use data on extrusion and lithium metal production from the Ecoinvent database  [21]. For production of the C/S composite cathode, we assume a 66% sulfur loading, and a melt diffusion process  [44]  and heating. Due to lack of specific data on industrial melt diffusion, production data for generic specialty chemicals from Geisler et al. [37]  are employed regarding yield, steam and electricity use. Elemental sulfur production data were obtained from the Ecoinvent database  [45]  (v2. 2, no data in v3. 3). No use of solvents is reported for the melt diffusion. Production of CMK-3 is based on Jun et al. [46] , but scaled linearly from g to kg scale for the input materials: mesoporous silica SBA-15, sucrose, sulphuric acid (H 2 SO 4 ) and hydrofluoric acid (HF).
Water was reported as solvent for CMK-3 production. Yield, electricity and steam requirements are again from Geisler et al. [37]. The amount of HF is calculated stoichiometrically based on Fogler et al. [47] , who describe its reaction with silica, mimicking the silicabased SBA-15. While data on production of sucrose, H 2 SO 4 and HF are all available in the Ecoinvent database  [21] , production of SBA-15 is not. It is therefore based on Jun et al. [46]  (scaled from g to kg) and Geisler et al. [37]  regarding yield and energy requirements. Again, water was reported to be the solvent used for SBA-15 production. Three input materials are required for SBA-15 production: tetraethyl orthosilicate, hydrochloric acid (HCl) and Pluronic P123. While the two first materials are available in the Ecoinvent database  [21] , Pluronic P123, a co-polymer of ethylene and propylene oxide in a 40/70 M monomer ratio  [48] , is not.
The ratio is applied to estimate the amounts needed, while the amount of seed propylene glycol required  [48]  is disregarded. Again, yield, electricity and steam requirements are based on Geisler et al. [37]. No use of solvent was reported for Pluronic P123 production. Detailed data regarding material input for the C/S composite, CMK-3, SBA-15 and Pluronic P123 can be found in Table  3. Although inert gas is often used during production of specialty chemicals  [37] , none is reported for the specialty chemicals considered here  [44, 46, 48]. Data for carbon black are available in the Ecoinvent database  [21] , while data for graphene are obtained from Arvidsson et al. [40]  for its production by ultrasonication that requires electricity, graphite and diethyl ether solvent. 90% solvent recovery, which is not uncommon in the chemical industry  [37] , is assumed.
Data for natural graphite and diethyl ether production were obtained from the Ecoinvent database  [21]. The aluminium current conductor was modelled as aluminium wrought alloy processed to a foil, available in the Ecoinvent database  [21]. Production data for DME were obtained from the Ecoinvent database  [21]  and for DIOX from Deng et al. [14]. For the complex production system of LiTFSI there is a lack of data sources. Therefore, we use data from Deng et al. [14] , who constructed a model based on 11 input materials, and generic data from Geisler et al. [37] , thus in a similarly generic way as done here for the materials in Table  3. Data for LiNO 3 were obtained from Dai  [49]  and data for the PP-PE-PP separator were obtained from an LCA study of a LIB  [50]. Data for electricity and heat production were obtained from the Ecoinvent database  [21].
Acknowledgements Chalmers Battery Initiative  is gratefully acknowledged for funding this study. The initiative is part of the profile Materials for Energy Applications, which is jointly managed by the  Areas of Advance Materials Science and Energy at Chalmers University of Technology. RA thanks the  Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (Formas)  for providing additional funding (grant number  2014-322 ). The continuous support for batteryrelated research to PJ from several  Areas of Advance at Chalmers University of Technology  ( Materials Science, Energy , and Transport) is also gratefully acknowledged.
Variable Loadings and Reliable design For a long time, deterministic design protocols have been used but their high level of conservatism tends to disqualify them today. Huge efforts have been recently made to successfully propose probabilistic design methods, taking account of the loading randomness, which is a key point for fatigue. If one wishes to estimate the variability in the use of a structure by a given population and the possible dispersion at which may be subject the resistance of this same structure, it is fundamental to experimentally measure this variability and therefore to collect as much information as possible by establishing experimental databases. These latter will then contribute to the improvement of constitutive models and fatigue criterion as mentioned in the following sections.
This enables to create a customer database in terms of vehicles usage. To be able to know the vehicles' usage conditions is far from sufficient to estimate a severity of use. Indeed, it is also essential to know the conditions of loading of this structure we want to design on all the specific types of roads, while taking into account the driver behaviour variability. In order to estimate both the drivers' behaviour and structure loads for a specific type of road, the structure one want to design has to be equipped with sensors. This vehicle is then driven on multiple courses taken as references for every possible "real-life" situations by a panel of professional drivers. This produces a matrix of elementary life situation of loading for the vehicle and by the way the studied structure, mixing both road and driver types and helps to understand the loading environment on the structure to be designed and then to adapt the design protocol.
(a) Shows weekly cases of the 2014-2017 Zika virus epidemic in the Americas using data from  [33, 38]  and the Pan American Health Organization (PAHO) available from https://github. com/andersen-lab/Zika-cases-PAHO. (b) Shows weekly cases from the West African Ebola epidemic published by the World Health Organization (WHO). (c) Shows weekly cases of the 2015-2016 Yellow fever epidemic in Angola and the Democratic Republic of Congo, published by WHO  [40]. (d) Shows weekly cases from 2012 to 2017 Middle Eastern Respiratory Syndrome outbreak available from https://github. com/rambaut/MERS-Cases. and spread using, for example, information on the reservoir or host movements  [35, 95]. In turn, phylogenetic information may complement epidemiological analysis by providing more evidence on the transmission routes that are common in an outbreak  [96].
In the context of the Zika outbreak in Florida, combining genomic data from the outbreak with epidemiological analysis revealed that the outbreak was driven by a large number of introductions rather than by persistent local transmission. In the recent yellow fever outbreak in southern Brazil, linking epidemiological, spatial and genomic data and techniques could provide insights into the transmission potential and risk of urban transmission  [102]. One dataset and analysis alone would have not been strong enough to make such conclusions  [33]. Inferences about epidemic processes made using mathematical models rely on a number of assumptions. Geographic modelling approaches, mostly informed by spatial ecology, attempt to fill gaps where no data has been observed, hence inferences may be uncertain, as the underlying ecological process may be poorly understood and dynamical aspects of the invasion process are ignored.
Acknowledgements. MUGK is supported by  The Branco Weiss Fellowship -Society in Science , administered by the  ETH Zurich  and acknowledges funding from a  Training Grant  from the  National Institute of Child Health and Human Development  ( T32HD040128 ) and the  National Library of Medicine of the National Institutes of Health  ( R01LM010812 ,  R01LM011965 ). OGP acknowledges support from the  Oxford Martin School. SC  acknowledges financial support from the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (Grant  ANR-10-LABX-62-IBEID ), the  Models of Infectious Disease Agent Study of the National Institute of General Medical Sciences , the  AXA Research Fund. NRF is supported by a  Sir Henry Dale Fellowship  ( 204311/ Z/16/Z ), internal  GCRF  grant  005073  and  John Fell Fund  grant  005166.
Acknowledgements This paper is an output of the  Community based Adaptive Learning in management of Conflicts and Natural Resources in Bangladesh and Nepal (CALCNR)  project. The views expressed are those of the authors alone. We are grateful to our colleagues and the leaders and members of all of the participating Community Based Organisations for their assistance and cooperation. We thank four anonymous reviewers as well as editors, reviewers and advisors from the  Conflict and Cooperation in Management of Climate Change  programme for their constructive comments.
Acknowledgements The technical help of the STnano cleanroom facility is gratefully acknowledged. This work was financially supported by  EC  through the  ERC Proof-of- Concept  project  FlexNanoOLED  ( GA-766936 ) and the  Marie Curie ITN  project  iSwitch  (GA No. 642196 ), the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02 , and the  International Center for Frontier Research in Chemistry (icFRC) , and by the  Slovenian Research Agency , program  P1-0055. N. P. acknowledges the  Slovene Human Resources Development and Scholarship Fund.
Catchment set and data A daily data set of 240 catchments across France (Fig. 1 ), set up by  Ficchí et al. (2016) , was used. The climate data of the SAFRAN daily reanalysis  (Vidal et al. , 2010)  were used as input data. Precipitation and temperature were spatially aggregated in each catchment since the GR4J model is lumped. Potential evapotranspiration was calculated using a temperature-based formula  (Oudin et al. , 2005). Full details on this data set are available in  Ficchí et al. (2016). Observed flows were retrieved for each catchment outlet from the Banque HYDRO (http://www. hydro. eaufrance. fr/ (last access: 29 August 2018),  Leleu et al. , 2014). The availability of data covers the 2005-2013 period. To avoid requiring a snow model, the catchments with less than 10 % of precipitation falling as snow were selected.
Acknowledgements. The authors thank  Météo France  for providing the data used in this work. We also wish to thank  Alban De Lavenne ,  Laure Lebecherel ,  Maria-Helena Ramos  and  Cedric Rebolho  for the discussions on the different aspects of the issues using the logarithmic transformation with KGE. We thank  Andrea Ficchí  for his work on the database and  Linda Northrup  for her correction of the English language of an earlier version of the paper. Finally, we extend our thanks to  Harald Kling  for discussions on this issue. We thank the topical editor,  Bettina Schaefli , for her careful reading of the paper, her suggestion on the modified Box-Cox transformation and the following discussions. We also thank the two reviewers,  Lieke Melsen  and  Björn Guse , for taking the time to read our paper and for their remarks that helped us to make the paper and the figures more understandable.
Data availability. The daily flow data can be downloaded from the Banque HYDRO website (http://www. hydro. eaufrance. fr/, last access 29 August 2018). The climatic data from the SAFRAN reanalysis used in this paper (daily precipitation and temperature) are not freely available. The data was provided to Irstea following a convention between the two institutes. However, the analyses can be reproduced using open data and would lead to similar conclusions. Author contributions. LS made the technical development and the analysis. The paper was written by him, GT and CP.
In recent years model-data interaction has improved through use of probabilistic techniques to inform and reduce the uncertainty of model parameters, while also taking into account observational uncertainty. This study builds on previous work, through access to a richer representation of the plant-soil ecosystem at multiple European forest sites, than was previously available. Given this rich dataset, we asked which observational datasets were most effective in reducing uncertainty in model predictions and model-data differences. Also, since there is a lack of consensus about whether it is more beneficial to calibrate forest sites separately or together we revisited this question with a particular emphasis on which is most effective in reducing model-data differences and uncertainty. We performed single dataset Bayesian calibrations (BC) and compared the results with a calibration with all the observations included.
We also compared calibrations where each pine forest site was calibrated separately with a calibration where all the pine sites were calibrated together. While measurements of plant and soil carbon stocks were more sparse, their inclusion in the BC were more important for reducing model-data differences and uncertainty in the above and belowground carbon pools than the greater numbers of carbon and water flux data. Our results suggest that use of calibration data representing just a few aspects of the ecosystem could be problematic, since improved model-data fits for the parts of the system represented by the data could be at the expense of other part of the system, where the model-data fit worsened. The single dataset calibrations helped to diagnose where there may be inconsistencies between different datasets or between the model and data or both.
These inconsistencies hampered the reduction in model-data differences in the calibration with all the observations present. As expected, we found a strong relationship between the quantity of data included in the calibration and the uncertainty reduction after BC, finding the largest reduction in uncertainty when all the observations were included. For some ecosystem variables uncertainty reduced after calibration but model-data differences increased. This would suggest that there were deficiencies in the model or systematic errors in the data or both. These results advocate the use of calibration datasets which represent the rich diversity of the ecosystem under investigation but where model discrepancies and data systematic errors are explicitly represented in the BC. While separate calibrations at each forest site generally reduced model-data differences more than calibrating at all the sites together, parts of the ecosystem that were sparsely observed benefited more from the multi-site calibration.
Calibration data Soil water content (SWC) was measured hourly and continuously using Time Domain Reflectrometry (TDR) probes at typically three depths  (5, 20 and 50cm). Data was available for most sites. The data was averaged to daily values to match the timestep of the model. Atmospheric/surface carbon and water flux data were taken from the CarboEurope Integrated Project (CEIP) database, the European Fluxes Database Cluster (http://gaia. agraria. unitus. it/home) or the GHG Europe portal (http://www. europe-fluxdata. eu/ghg-europe). Soil respiration, NO 2 and NO were measured in static chambers at the forest sites. Given the difficulty in collecting manual soil measurements that closely match what is calculated in the model, we opted to create an annual integrated metric for these soil flux data.
Data from CEIP and other project (e. g. FLUXNET) databases as well as publications (Table  2 ) were used for canopy height (H), leaf area index (LAI), diameter at breast height (DBH), soil organic matter (CSOM), roots (CR), stems, branches, leaves (CLBS) and litter (CLITT). Additional data for above and belowground carbon stocks were provided by the global database assembled by  Luyssaert et al. (2007). 2. 1. 3 Soil depth and soil water retention data used to set prior ranges. Measurements of wilting point, field capacity and rooting depth were available at a number of sites from the CEIP database and also peer-review publications (Table  2 ) and are listed in Table  4  below. Saturation measurements were inferred from the largest soil water content measurement recorded at each site. Where measurements or publications were not available or could not be found, wilting point and field capacity were estimated using tabulated values from the German soil texture classification Ad-Hoc-AG  Boden (2004) , by linking soil texture, from the CEIP and other project (e.
g. FLUXNET) databases, or peer-reviewed publication, to characteristic values of soil water potential and volumetric soil water content by means of pedo-transfer functions  (van Genuchten, 1980)  https://www. bgr. bund. de/DE/Themen/Boden/Netzwerke/Adhocag/Downloads/ Ergaenzungsregel_1_18. pdf?__blob=publicationFile&v=2. Where information on rooting depth from measurements or publications were not available a rooting depth of 1m was assigned.
Brief description of the model BASFOR The BASic FORest simulator, BASFOR, (https://github. com/MarcelVanOijen/BASFOR) Van Oijen et al. (  2005 ) is a deterministic forest model. The model simulates carbon and nitrogen cycling in trees, soil organic matter and litter. BASFOR is built from well known process representations. Light absorption is calculated by Beer's law. GPP is calculated as light absorption times a light-use efficiency (LUE). NPP is calculated as a fixed ratio of GPP. LUE is temperature-, CO 2 and soil water contentdependent and may be reduced if insufficient nitrogen is taken up by the plants. Potential nitrogen uptake scales with root system surface area. Actual nitrogen uptake is the minimum of demand, determined by tissue N-concentration, and potential uptake. Allocation of assimilates follows allometric rules, but water stress may limit leaf area index (LAI).