dataesr/scientific-paragraphs-categorization huggingface dataset:
We present the uncertainty analysis of simulations with default (parametrized with satellite thermal data over all lakes and in situ measurements) and calibrated (with in situ temperature measurements for each lake) model parameters as well as the sensitivity analysis of the latter. Overall, the 90% confidence uncertainty range is largest for hypolimnion temperature simulations with a median of 8. 5 ºC and 2. 32 ºC respectively with default and calibrated parameter values. There is less uncertainty associated with epilimnion temperature simulations with a median of 5. 42 ºC and 1. 85 ºC before and after parameter calibration. This dataset will help provide insight into the thermal functioning of French lakes. It provides over six decades of epilimnion and hypolimnion temperature data, crucial for climate change studies at a regional scale.
The dataset will also be of great advantage for decision making by stakeholders. of water temperature differs in terms of approach and frequency, from decades (Piccolroaz et al. , 2020)  to a few years (Sharma et al. , 2015), thus rendering it challenging to investigate warming trends. Due to the difficulties in conventional in situ monitoring, which is often expensive, the coupling of modelling and satellite remote sensing data has become fundamental in the field of limnology (Nouchi et al. , 2019). Modelling provides means to interpolate both temporal and spatial gaps. It thereby allows us to acquire information about surface water temperatures, which are globally the focus of lake climate change studies, and deep-water temperatures which are as critical though often disregarded in this context.
Several numerical models that vary in complexity exist for conducting water temperature simulations, the most accurate being deterministic or process-based models. Nevertheless, regional or global deterministic modelling efforts over long periods are usually hindered by the lack of sufficiently detailed input data (e. g. , meteorological and field data) to run the models  (Kim et al. , 2021). For practical and operational purposes, simpler models (semi-empirical, statistical or hybrid physical-statistical based models) with less requirements for forcing data, have been mostly applied to assess the impact of climate change on lake ecosystems and study them (Piccolroaz et al. , 2020; Toffolon et al. ,     2014; Sharma et al. , 2008). For conducting long-term simulations over a considerable number of lakes, this type of models is especially useful for detecting trends in time series, which with short datasets is not accurately achievable  (Gray et al.
, 2018). The performance of numerical models depends highly on the calibration of their parameters as well as on the quality of the input data. Satellite remote sensing is an effective way to monitor surface water temperature on a synoptic scale  (Schaeffer et al. , 2018; Sharaf et al. , 2019)  and provide a complementary source of data to in situ measurements for model calibration or validation purposes (Allan et al. , 2016; Babbar-Sebens et al. , 2013). In lakes thermal functioning, which is often hindered by the lack of water temperature observations. The present dataset will provide new insights about the thermal behavior of French lakes. This will be of great advantage for stakeholders, as it should allow them to take better management strategies under climate change. 11.
However, long-term datasets of in situ temperatures are usually scarce and mostly limited to large lakes  (Layden et al. , 2015). Moreover, the sampling https://doi. org/10. 5194/essd-2022-457 Preprint. Discussion started: 14 February 2023 c Author(s) 2023. CC BY 4. 0 License. particular, thermal infrared sensors onboard the Landsat satellites are very adequate for retrospective analysis of surface water temperature with a spatial resolution adapted for small to medium size lakes and reservoirs at a bimonthly acquisition frequency. Landsat 4 and 5 TM (Thematic Mapper), 7 ETM+ (Enhanced Thematic Mapper) and 8 TIRS (Thermal InfraRed Sensor) provide surface temperature data at spatial resolutions of 120, 60 and 100 m respectively. Landsat series records of surface water temperature can be used to validate 3D hydrodynamic models when in situ measurements are scarce  (Sharaf et al.
, 2021)  and to spatially assess the quality and suitability of aquatic habitat for biological communities  (Halverson et al. , 2022). Although, satellite thermal data is limited to the surface, its integration into model calibration could improve the accuracy of simulations over the surface layer and the water column  (Javaheri et al. , 2016). Here we present on a regional scale, a long-term dataset, LakeTSim (Lake Temperature Simulations), of daily epilimnion and hypolimnion temperature simulations for the period 1959-2020 over 401 French lakes monitored under the Water Framework Directive (WFD) including natural and artificial lakes, reservoirs and gravel pits. We present the OKPLM (Ottosson-Kettle-Prats Lake Model) used to produce water temperature simulations and its performance. Further, we provide the uncertainty analysis of simulations with default (parametrized with satellite thermal data over an entire set of lakes) and calibrated (with in situ temperature measurements for each lake) model parameter values as well as the sensitivity analysis for the latter.
The goal of publishing this dataset is to provide new insight about surface and deep-water temperatures of lakes in France especially for those that are not monitored regularly through conventional methods. This long-term dataset is valuable for developing temperature indicators for identifying warming trends, extreme events and possible changes in the mixing regime https://doi. org/10. 5194/essd-2022-457 Preprint. Discussion started: 14 February 2023 c Author(s) 2023. CC BY 4. 0 License. among others. These indicators will contribute to assess the impact of climate change on lakes thermal functioning and its influence on the biological community structure and trophic webs.
3. 1. The OKP Lake Model description The OKPLM (Ottosson-Kettle-Prats Lake Model) is a two-layer semi-empirical data model adapted from  Kettle et al (2004)   where 𝑇 𝑒 is the epilimnion temperature (°C), 𝑖 is the day number, 𝐴, 𝐵 and 𝐶 are calibration parameters, 𝑆 is the solar radiation (W m -2 ) and 𝑓( * ) is an exponential smoothing function with 𝑇 𝑎,𝑖 * defined as: 𝑇 𝑎,𝑖 * = 𝑇 𝑎,𝑖 -𝑀𝐴𝐴𝑇 (2) where 𝑇 𝑎,𝑖 is air temperature (°C) and 𝑀𝐴𝐴𝑇 is the annual mean air temperature (°C). 𝑇 ℎ,𝑖 = 𝐴 • 𝐷 + 𝐸 • 𝑔(𝑇 𝑒,𝑖 ) (3) where 𝑇 ℎ is the hypolimnion temperature (°C), 𝐷 and 𝐸 are calibration parameters and 𝑔(𝑇 𝑒,𝑖 ) is an exponential smoothing of 𝑇 𝑒. The OKPLM is integrated into a Python 3 package, "ALAPROD" (A LAke MODElling project-PRODuction) which for the present study was used to simulate epilimnion and hypolimnion water temperatures (Danis, 2020).
ALAPROD is part of a software environment called ALAMODE. In addition to lake water temperature, this package can also be used to make simulations of stream water temperature, hydrodynamics and stream flow rates. In this package OKPLM can be run in two modes: the "default" mode where model parameters use the parameterization presented in  Prats & Danis (2019) , and the "calibrated" mode where model parameters are calibrated individually for each lake by using in situ temperature measurements. The "default" parameterization provides expressions of the model parameters as a function of lake characteristics (latitude, altitude, surface, volume, depth). The expressions for epilimnion module parameters were derived using surface temperatures estimated from Landsat infrared data acquired between 1999 and 2016 over French lakes  (Prats et al.
Input data The OKPLM was forced with two sources of meteorological data extracted from the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) analysis system  (Durand et al. , 1993)  and the S2M (SAFRAN-SURFEX/ISBA-Crocus-MEPRA) meteorological reanalysis  (Vernay et al. , 2015 (Vernay et al. , , 2022 ). The SAFRAN system provides meteorological variables at an hourly time step estimated through interpolation and assimilation processes with an 8 km square grid. Average daily data from the nearest grid cell was selected for each study site. The difference in altitude between the study site and the grid cell was accounted for by applying an adiabatic elevation correction on air temperature. The S2M model chain combines the SAFRAN meteorological analysis and the SURFEX/ISBA-Crocus snow cover model including MEPRA (Modèle Expert d'Aide à la Prévision du Risque d'Avalanche).
It is more adapted to mountainous regions as it has a spatial definition where spatial heterogeneity is taken into consideration. The S2M reanalysis uses a vertical resolution of 300 m and is the result of simulations performed over mountainous zones called "massifs" each corresponding approximately to an average surface of 1000 km 2. These massifs represent the spatial variability of processes in mountainous regions. Average daily data was used for each study site. In situ temperature profiles, geographical and morphological data of the study sites were extracted from the PLAN_DEAU database managed by INRAE (l'Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement) and R&D consortium ECLA (ECosystèmes LAcustres) at Aix-en-Provence, France.
Lake simulations For this study, we considered 401 lakes (Figure  1 ) located in Metropolitan France monitored according to the Water Framework Directive (WFD). Here we refer to lakes as natural lakes, reservoirs, artificial lakes and gravel pits. The present lake dataset includes 54 natural lakes, 302 reservoirs, 38 artificial lakes and 7 gravel pits that have characteristics ranging between 0 and 2279. 7 m for altitude, 0. 8 and 309. 7 m for maximum depth, 0. 08 and 577. 12 km 2 for surface area and 5 × 10 4 and 8. 9 × 10 10 m 3 for volume. The OKPLM was run using "default" and "calibrated" parameters with two sources of meteorological data "SAFRAN" and "S2M" over specific sets of lakes. Among the total number of study sites (n=401), the model was forced using SAFRAN and S2M meteorological data respectively for 210 and 21 lakes with "default" model parameters, and for 164 and 6 lakes with "calibrated" model parameters.
The geomorphological characteristics of the simulated lakes with each of the abovementioned configurations are shown in Table  1. "Calibrated" model parameters are adopted when in situ temperature measurements are available; conversely, "default" parameters are used. S2M data are more representative of mountainous meteorological conditions than SAFRAN data and were thus used for simulating the water temperature in lakes situated at altitudes higher than 900 m. package "CUSPY" (Calibration, Uncertainty analysis and Sensitivity analysis in PYthon), which is part of the software environment "ALAMODE" (Danis, 2020) and acts as an interface to the package "pyemu"  (White et al. , 2016). Parameter values were calibrated for lakes with enough available in situ data (temperature profiles and bathymetry).
Our goal in publishing the present dataset is to expand knowledge about the water temperature of French lakes and provide data, with enough details and reliability, that it could be implemented in different studies where water temperature is implicated for understanding specific processes or interactions, in particular under climate change. Hence the significance of the present findings. The present study, making use of a semi-empirical model to provide long-term data about water temperature, was necessary for several reasons. Equipping a large number of lakes with thermal sensors is challenging and labor-intensive, it comes with a high financial cost that is often not available. Consequently, historical and even current water temperature datasets are often scarce, which can be problematic for studying the impact of climate change, as it requires high frequency data over a long duration of time for accurate analysis.
In general, the higher the sampling frequency and duration, the better the data is suited to estimate or analyze specific processes or warming trends. The sampling frequency and length of a series longer than 30 years seem to be the most appropriate  (Gray et al. , 2018). Although, the duration and frequency of a dataset have a major role in reflecting accurate representations, their influence is scarcely addressed when it comes to climate change studies related to warming trends in water temperature. This dataset is very useful for climate change studies; it could be used for developing and analyzing several temperature indicators (e. g. , annual or seasonal maximal and minimal temperature values, temperature exceeding certain thresholds with biological implications, etc. ). Further, mixing and stratification dynamics are important to characterize as they drive lake biogeochemistry.
Code availability The respective codes for the "CUSPY" (Prats-Rodríguez and Danis, 2023a) and "OKPLM" (Prats-Rodríguez and Danis, 2023b) packages, which can be used to conduct sensitivity and uncertainty analysis and to run the OKP Lake Model, are available at https://github. com/inrae/ALAMODE-cuspy and https://github. com/inrae/ALAMODE-okp as well as ZENODO.
Data availability The LakeTSim dataset  (Sharaf et al. , 2023)  for epilimnion and hypolimnion water temperature simulations and supporting information are available at doi:10. 57745/OF9WXR. The file "00_Data_description. txt" contains a description of the dataset. The geographical (longitude and latitude) and morphological (surface area, volume and maximum depth) data for the 401 lakes are presented in the file "01_Lake_data. txt" in addition to the name, type, altitude and the identification code for each lake. The data for daily epilimnion (tepi) and hypolimnion (thyp) temperatures simulated with the OKPLM are presented in text files available in the folders "02_LakeTSim_SAFRAN_OKPdefault_data", "03_LakeTSim_SAFRAN_OKPcalibrated_data", "04_LakeTSim_S2M_OKPdefault_data" and "05_LakeTSim_S2M_OKPcalibrated_data".
Conclusions We present the LakeTSim dataset and the semi-empirical OKP Lake Model for simulating water temperature in Lakes. We applied the model over a set of 401 French lakes for the period 1959-2020 to derive daily simulations of epilimnion and hypolimnion water temperatures, here referred to as the LakeTSim dataset. Previous efforts to assess the model's performance show an overall acceptable representation of epilimnion and hypolimnion temperatures when compared to in situ measurements. The uncertainty analysis of simulations demonstrates that more uncertainty is associated firstly with default simulations, secondly hypolimnion compared to epilimnion temperatures and, thirdly deep lakes in particular reservoirs (maximal depth > 8 m). Although the calibration significantly decreases the uncertainties related to both the epilimnion and hypolimnion, in some cases they are still considerable for the latter.
Manag. , 20, 91-109, https://doi. org/10. 1080/07438140409354354, 2004. Ottosson, F. and Abrahamsson, O. : Presentation and analysis of a model simulating epilimnetic and hypolimnetic temperatures in lakes, Ecol. Modell. , 110, 233-253, https://doi. org/10. 1016/S0304-3800(98) 00067-2 , 1998. Piccolroaz, S. , Woolway, R. I. , and Merchant, C. J. :  Global reconstruction of twentieth century lake surface water temperature reveals different warming trends depending  on the climatic zone, Clim. Change, 160, 427-442, https://doi. org/10. 1007/s10584 -020-02663 -z, 2020. Prats-Rodríguez, J. and Danis, P. -A. : inrae/ALAMODE-cuspy: cuspy v1. 0, https://doi. org/10. 5281/ZENODO. 7585606, 2023a. Prats-Rodríguez, J. and Danis, P. -A. : inrae/ALAMODE-okp: okplm v1. 0, Weyhenmeyer, G. A. , Williamson, C.
E. , and Woo, K. H. : A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009, Sci. Data, 2, 1-19, https://doi. org/10. 1038/sdata. 2015. 8, 2015. Shatwell, T. , Thiery, W. , and Kirillin, G. : Future projections of temperature and mixing regime of European temperate lakes, Hydrol. Earth Syst. Sci. , 23, 1533-1551, https://doi. org/10. 5194/hess-23-1533-2019, 2019. Snowling, S. D. and Kramer, J. R. : Evaluating modelling uncertainty for model selection, Ecol. Modell. , 138, 17-30, https://doi. org/10. 1016/S0304-3800(00) 00390-2 , 2001. Toffolon,  M. , Piccolroaz,  S. , Majone,  B. , Soja, A. M. ,  Peeters , F. , Schmid, M. , and Wüest, A. : Prediction of surface temperature in lakes with different morphology using air temperature, Limnol. Oceanogr.
Background: Poly(ADP-ribose) polymerase (PARP) inhibitor maintenance therapy is standard of care for some patients with advanced ovarian cancer. We evaluated the efficacy and safety of PARP inhibitor rechallenge. Patients and methods: This randomized, double-blind, multicenter trial (NCT03106987) enrolled patients with platinum-sensitive relapsed ovarian cancer who had received one prior PARP inhibitor therapy for ≥18 and ≥12 months in the BRCA-mutated and non-BRCA-mutated cohorts, respectively, following first-line chemotherapy or for ≥12 and ≥6 months, respectively, following a second or subsequent line of chemotherapy. Patients were in response following their last platinum-based chemotherapy regimen and were randomized 2:1 to maintenance olaparib tablets 300 mg twice daily or placebo. Investigator-assessed progression-free survival (PFS) was the primary endpoint.
9  Although the greatest PFS benefit occurred in patients with BRCAm,  [1] [2] [3]  PFS benefit was also observed in patients without a BRCAm regardless of their HRD status. 2, 3, [10] [11] [12] [13]  In patients with newly diagnosed advanced ovarian cancer, PARPi maintenance therapy also provided a substantial PFS benefit,  [4] [5] [6] [7] 14  with a clinically meaningful OS benefit seen with maintenance olaparib in patients with BRCAm  15  and with maintenance olaparib plus bevacizumab in patients with HRD-positive tumors. 16  The patients showing the greatest PFS prolongation were those with BRCAm  4, 14, 17  or whose tumors tested HRD-positive with or without BRCAm. [5] [6] [7]  However, it is unknown if patients will benefit from rechallenge with PARPimaintenance therapy at relapse. The randomized, double-blind, placebo-controlled OReO/ENGOT-ov38 study (NCT03106987) is the first phase III trial to address the question of whether rechallenge with maintenance olaparib provides benefit to patients with PSROC who are in response to platinum-based chemotherapy and have previously received a PARPi.
Data cut-off (DCO) for the primary PFS analysis was to occur when both 85 PFS events had occurred in the BRCAm cohort and 74 PFS events had occurred in the non-BRCAm cohort. The final DCO was planned approximately 60 months after the first patient was enrolled or after 50% of deaths had occurred in either cohort, whichever occurred first. The BRCAm and non-BRCAm cohorts were analyzed separately. Efficacy data were analyzed in the full analysis set (all randomized patients), safety data were summarized in the safety analysis set (all patients receiving at least one dose of randomized treatment), and HRQoL data were summarized in all randomized patients with a baseline HRQoL assessment. The Kaplan-Meier method was used to analyze PFS, including median PFS and PFS rates at prespecified clinically relevant time points (6 months and 1 year), with the difference between olaparib and placebo assessed using a stratified log-rank test at the two-sided 5% significance level (strata were use of prior bevacizumab [yes versus no] and number of prior regimens of platinum-based chemotherapy [≤3 versus ≥4]).
8 months for placebo (2. 6-2. 9) in the non-BRCAm cohort (Supplement). At the time of DCO, the BRCAm cohort had also reached 50% data maturity for OS. Within each cohort, baseline characteristics were generally balanced between the treatment arms, although numerically more placebo than olaparib patients were in complete response, particularly in the BRCA-mutated cohort (Table  1 ; additional data are presented in Supplementary Table  S1  including histology type). Patients were heavily pretreated with >85% of patients in both cohorts having received ≥3 prior lines of any chemotherapy (Table  1 ); some patients had received prior non-platinum chemotherapy (Supplementary Table  S1 ). Only 7% of BRCAm patients and 14% of non-BRCAm patients were included at first relapse after having received a maintenance PARPi as part of first-line therapy.
In the BRCAm cohort, serious AEs were reported in five (7%) patients in the olaparib group and J o u r n a l P r e -p r o o f no patients in the placebo group, with treatment-related serious AEs reported in one (1%) patient in the olaparib group (anemia and neutropenia) and no patients in the placebo group. In the non-BRCAm cohort, serious AEs were reported in 11 (15%) patients in the olaparib group and two (6%) patients in the placebo group, with treatment-related serious AEs reported in three (4%) patients in the olaparib group (anemia [n = 1], neutropenia [n = 1], general physical health deterioration [n = 1]) and no patients in the placebo group. There were no AEs resulting in death in either cohort. Data on MDS/AML and new primary malignancies were collected beyond the 30-day safety follow-up period up to DCO (February 15, 2021).
No cases of MDS/AML were reported in the olaparib group. MDS was reported in one (3%) patient in the placebo group from the BRCAm cohort (onset of MDS was 292 days after the last dose of placebo). New primary malignancies occurred in one (1%) patient in the olaparib group (esophageal squamous cell carcinoma) and one (3%) patient in the placebo group (breast neoplasm) in the BRCAm cohort, and in one (1%) patient in the olaparib group (basal cell carcinoma) and no patients in the placebo group in the non-BRCAm cohort. No cases of pneumonitis were reported during the treatment or safety follow-up periods. AEs were usually managed by dose modification, with few patients requiring treatment discontinuation because of AEs in either cohort (Table  2 ). J o u r n a l P r e -p r o o f DISCUSSION OReO/ENGOT-ov38 is the first randomized placebo-controlled trial to report data for rechallenge with a PARPi in patients with PSROC.
J o u r n a l P r e -p r o o f Health, GSK/Tesaro, and Pfizer; consulting fees to their institution from Vaccibody; personal/institutional consulting fees from Gilead; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Agendia, J o u r n a l P r e -p r o o f   The data include patients with anemia, decreased hemoglobin level, decreased hematocrit, decreased red blood cell count, erythropenia, macrocytic anemia, normochromic anemia, normochromic normocytic anemia, or normocytic anemia. FIGURE LEGENDS c The data include patients with neutropenia, febrile neutropenia, neutropenic sepsis, neutropenic infection, decreased neutrophil count, idiopathic neutropenia, granulocytopenia, decreased granulocyte count, or agranulocytosis. d Thrombocytopenia occurred in less than 10% of the patients in each trial group in the BRCA-mutated cohort, but the data are provided to complete the profile of hematological adverse events.
ACKNOWLEDGEMENTS We thank  Dr Mansoor R. Mirza  for his contribution on the steering committee; the members of the independent data monitoring committee,  Dr Christian Marth ,  Dr Nicholas Reed , and  Dr Xavier Paoletti ; and  Alan Barnicle  ( AstraZeneca ),  Zahid Bashir  ( AstraZeneca ),  Steven Radziwonik  ( AstraZeneca ),  Rosie Taylor  ( AstraZeneca ),  Steve Keefe  ( Merck & Co. , Inc. ), and  Myriad Genetic Laboratories, Inc. for their contribution to this trial. This study was funded by  AstraZeneca  and is part of an alliance between  AstraZeneca  and  Merck Sharp & Dohme LLC , a subsidiary of  Merck & Co. , Inc. , Rahway, NJ, USA. Medical writing assistance was provided by  Gillian Keating MBChB , of  Cence , funded by  AstraZeneca  and  MSD.