dataesr/scientific-paragraphs-categorization huggingface dataset:
Empirical confidence intervals For each forecast day d, we compute empirical confidence intervals using the distribution of relative errors for the database given by E d ¼ fe d;k g N k¼1 , using the estimated optimal values for the parameters N median and μ. Assuming that the distribution of the relative forecast error for the current incidence trend curve s is similar to the one obtained for the database and determined by E d , we can empirically approximate the percentiles of the forecast distribution, F d , of the current curve, using the percentiles of E d. Indeed, let us denote by P p (X) the p-th percentile of a distribution X, then where s f d is the forecast estimated by the proposed method. A 95% central confidence interval for the incidence trend value is for example given by (P 0.
This fact has no influence for method preprocessing the data by a 7 day sliding average. Nevertheless, since we use daily estimates, a single weekly estimate has a negative impact on the quality of our forecast. To address this issue, when a country provides data on a day, but not on the previous days, we distribute equally the last accumulated value over the previous uninformed days before applying EpiInvert. 10 ) for the entire database: the red curve indicates the mean of the distribution that is greatly affected by the skewness of the distribution, which justifies using the median (the curve in green) instead of the mean. The median is indeed very close to zero, which proves the consistency of the approximation adopted in Eq  (12). From the outside to the inside, the shaded areas represent the estimated (1 -α k ) × 100% central prediction intervals ðl a k ; u a k Þ for α k = 0.
05, 0. 1, 0. 2,. , 0. 9. https://doi. org/10. 1371/journal. pcbi. 1010790. g004 Since EpiLearn forecasts the daily incidence, the weekly forecast is obtained by summing the forecasted raw daily incidence given by  (8). The quantiles of the associated weekly distributions are computed on the registered database of incidence curves by extending the procedure of the previous section which computes the confidence intervals of the forecasted incidence curve. In this case, we aggregate to the weekly scale first and then compute quantiles. The European Hub encourages teams to provide, for each model, m, each horizon week, h = 1, 2, 3, 4, and each forecast target, n, the prediction of the weekly incidence, f m,h, n , and 23 quantiles of the associated distribution. These quantiles correspond to the predictive median, M, and eleven (1 -α k ) × 100% central prediction intervals ðl a k ; u a k Þ, with α k = 0.
Conclusion Given the large number of factors that can influence a future evolution, forecasting the evolution of the incidence curve is clearly difficult. We saw in the discussion section that most standard approaches estimate the parameters of an evolution model (ARIMA, SIR, a logistic curve). In this work, we proposed EpiLearn, a method following a more empirical approach that estimates the forecast by a learning procedure using many samples of past incidences evolution in many countries. Using EpiInvert, an incidence decomposition method, we removed first the strong administrative weekly bias from the original raw incidence to estimate a smooth incidence trend curve. Using a large database of incidence trends, the forecast is computed as the median of the closest curves, in the past, to the current incidence trend curve.
Notes, audio recordings, and other relevant materials can be uploaded to document the decisions and progress made in each module. In developing the toolkit, a balance was sought between theoretical foundation, practical orientation, and usability. The ItFits-toolkit was built from scratch before the start of recruitment to the trial and underwent various rounds of conceptual and technical piloting with various user groups representing the perspectives of implementers, clinical stakeholders, and researchers. More information regarding the toolkit is available on the project website  [43]  and in the study protocol  [31]. This toolkit is freely accessible  [44].
The introductory training and calls were provided by members of the central research team involved in the development of the ItFits-toolkit. Data from the web-based platform showed that out of the 12 implementation teams, 10 (83%) progressed from at least one of their projects to module 4 during the exposure period. Qualitative data revealed that 2 of the remaining teams were in the process of applying their strategies, without recording it on the platform. All teams continued to use the toolkit after they completed the exposure period. A total of 31 projects (range 1-6 per organization) were initiated using the ItFits-toolkit. The term "project" referred to a process initiated in the ItFits-toolkit to develop and apply a tailored strategy in relation to the iCBT service. For 12 projects, a full cycle of all 4 ItFits-toolkit modules was completed.
Data Management Nested within the service delivery organizations, data were collected from implementers and service providers. Some data and outcomes (demographics, satisfaction, and usability) were collected once, whereas data on the primary and secondary outcomes were assessed every 3 months (Table  2 ). All questionnaires were translated using a forward-backward translation procedure  [31]. All data were collected through a secure web-based central Data Collection System (DCS), which allowed a standardized and structured data collection process. The DCS was developed specifically for this study and designed to prevent missing values or false entries and to enable automatic anonymization. Deidentified data supporting the findings of this study are available upon request. Investigators interested in using the data must provide a methodologically sound proposal directed to the corresponding author.
Data Analysis Data for the primary outcome were analyzed using a 3-level linear mixed-effects modeling (LMM) approach  [35]  with normalization as the dependent variable and time (as a discrete variable) and intervention (ie, the ItFits-toolkit use) as independent variables. To account for expected intervention lag effect, a fractional term for the ItFits-toolkit use parameter was used to reflect the 6-month exposure time (0, 0. 5, and 1). To account for a correlation structure in the outcome involving 3 nested levels, repeated measurements (L1) were clustered at the level of service providers (L2), and service providers were clustered at the organization level (L3). A temporal effect was assessed by testing the null hypothesis that the normalization level was constant over time when controlling for the effect of the ItFits-toolkit using ANOVA.
0 10. 0. The scale scores for SUS and CSQ were calculated using the respective prescribed scoring systems. For CSQ, summed item rating scores were used  [56]. For SUS, the summed item ratings were converted to a 0 to 100 scale using a curved grading scale, with 68 points to be interpreted as neutral  [58]. All observed data were included in the analyses following the intention-to-treat principle. We relied on the capability of linear mixed-effects models to estimate model parameters in case of missing values under the Missing at Random assumption  [59]. Data cleaning and analyses were performed using R (version 4. 0. 2; R Foundation for Statistical Computing)  [60]  in RStudio  [61]  using the following packages: dplyr  [62] , psych  [63] , ggplot2  [64] , and lmerTest  [65].
Ethics Approval and Consent to Participate Each local research team translated and adapted the generic study protocol to the local requirements and submitted it for review to competent local medical ethical review committees. All committees either approved the study or provided a confirmation letter that it was exempt from approval as the study was not considered as medical research. A portfolio of the ethical documentation can be accessed through Deliverable 23 (D9. 1, H -Requirement No. 1), which was submitted to the European Commission (Horizon 2020, Grant Agreement 733025). The study participants signed an informed consent form indicating the purpose of the study and the nature, use, and management of their data. The study protocol was published  [31] , and the study was registered with ClinicalTrials.
Recruitment and Sample Characteristics A total of 39 implementers in 12 mental health service delivery organizations used the ItFits-toolkit to implement the iCBT services. The group had a mean age of 42. 6 (SD 10. 1) years, and 69% (27/39) of the implementers were female. More than half (23/39, 59%) of the implementers had ≥6 years of work experience in mental health and were appointed as general project managers (13/39, 33%) or clinical researchers (12/39, 31%). A total of 456 iCBT service providers were included in this study (Table  3 ). The response rate of the service providers was high (78% across all 10 waves), resulting in 2884 complete data points. Approximately 30. 9% (141/456) of the service providers were replaced because of study dropout during waves 3 to 10. The group had a mean age of 41 (SD 11) years, and 68.
Uptake During the IAU condition, 3256 patients were referred to the iCBT services, of whom 588 (18. 05%) received adequate exposure to the iCBT services. During the ItFits-toolkit condition, 3935 individuals were referred to the iCBT services, of whom 842 (21. 39%) received adequate exposure. Over the course of the trial period, 7191 patients were referred and received log-in credentials to the iCBT services. Following local treatment protocols, 19. 88% (1430/7191) patients received meaningful exposure to the iCBT services, 72. 59% (5220/7191) dropped out of the iCBT service, 5. 71% (410/7191) were using the iCBT service, and 4. 35% (313/7191) did not start with the iCBT service when data collection was closed. Upon inspection of the data, changes in uptake over time followed an inconsistent and variable pattern, and no clear effect was visible with regard to the introduction of the ItFits-toolkit.
XSL • FO RenderX stepped-wedge cluster randomized controlled trial study design with repeated measures and a psychometrically validated implementation outcome measure, the study allowed for these variations to provide an accurate representation of real-world implementation practice. By randomizing the moment of introduction of the ItFits-toolkit in the implementation of mental health organizations, both conditions had an equal chance of being exposed to the events that occurred during the trial. Another strength was the systematic execution of the multisite study protocol within budget and time and with a centralized DCS providing high data quality  [31]. Nevertheless, the findings need to be interpreted with care and are indicative of whether the conceptualization and operationalization of the tailored implementation as packaged in the ItFits-toolkit is a feasible idea.
Some methodological limitations should be considered when interpreting the results. One aspect was that IAU activities cannot be undone once embarked upon. The results might be influenced by carryover effects and intervention lag effects within the service delivery organizations. Therefore, it was likely that the findings originated from the ItFits-toolkit plus usual implementation activities. Likewise, the effects of the ItFits-toolkit might have become apparent beyond the data collection period. A second methodological limitation relates to outcome normalization, as measured by the NoMAD. This questionnaire was developed with precision and methodological rigor  [45, 72]  and has been psychometrically validated in various studies in various settings  [46] [47] [48]. However, these studies have used cross-sectional samples, and the psychometric sensitivity of NoMAD to longitudinal change is yet to be explored.
Acknowledgments The authors would like to thank all implementers and service providers who participated in this study. The authors would also like to thank all ImpleMentAll consortium members for their nonauthor contributions. Specifically, the authors would like to thank the local research and implementation teams, the internal Scientific Steering Committee, and the  External Advisory Board  for their input in designing and executing the study. Members of the ImpleMentAll consortium were  Adriaan Hoogendoorn ,  Ainslie O'Connor ,  Alexis Whitton ,  Alison Calear ,  Andia Meksi ,  Anna Sofie Rømer ,  Anne Etzelmüller ,  Antoine Yrondi ,  Arlinda Cerga-Pashoja ,  Besnik Loshaj ,  Bridianne O'Dea ,  Bruno Aouizerate ,  Camilla Stryhn ,  Carl May ,  Carmen Ceinos ,  Caroline Oehler ,  Catherine Pope ,  Christiaan Vis ,  Christine Marking ,  Claire van Genugten ,  Claus Duedal Pedersen ,  Corinna Gumbmann ,  Dana Menist ,  David Daniel Ebert ,  Denise Hanssen ,  Elena Heber ,  Els Dozeman ,  Emilie Brysting ,  Emmanuel Haffen ,  Enrico Zanalda ,  Erida Nelaj ,  Erik Van der Eycken ,  Eva Fris ,  Fiona Shand ,  Gentiana Qirjako ,  Géraldine Visentin ,  Heleen Riper ,  Helen Christensen ,  Ingrid Titzler ,  Isabel Weber ,  Isabel Zbukvic ,  Jeroen Ruwaard ,  Jerome Holtzmann ,  Johanna Freund ,  Johannes H Smit ,  Jordi Piera-Jiménez ,  Josep Penya ,  Josephine Kreutzer ,  Josien Schuurmans ,  Judith Rosmalen ,  Juliane Hug ,  Kim Mathiasen ,  Kristian Kidholm ,  Kristine Tarp ,  Leah Bührmann ,  Linda Lisberg ,  Ludovic Samalin ,  Maite Arrillaga ,  Margot Fleuren ,  Maria Chovet ,.
Context. Stellar granulation generates fluctuations in photometric and spectroscopic data whose properties depend on the stellar type, composition, and evolutionary state. Characterizing granulation is key for understanding stellar atmospheres and detecting planets. Aims. We aim to detect the signatures of stellar granulation, link spectroscopic and photometric signatures of convection for mainsequence stars, and test predictions from 3D hydrodynamic models. Methods. For the first time, we observed two bright stars (T eff = 5833 and 6205 K) with high-precision observations taken simultaneously with CHEOPS and ESPRESSO. We analyzed the properties of the stellar granulation signal in each individual dataset. We compared them to Kepler observations and 3D hydrodynamic models. While isolating the granulation-induced changes by attenuating and filtering the p-mode oscillation signals, we studied the relationship between photometric and spectroscopic observables.
Results. The signature of stellar granulation is detected and precisely characterized for the hotter F star in the CHEOPS and ESPRESSO observations. For the cooler G star, we obtain a clear detection in the CHEOPS dataset only. The TESS observations are blind to this stellar signal. Based on CHEOPS observations, we show that the inferred properties of stellar granulation are in agreement with both Kepler observations and hydrodynamic models. Comparing their periodograms, we observe a strong link between spectroscopic and photometric observables. Correlations of this stellar signal in the time domain (flux versus radial velocities, RV) and with specific spectroscopic observables (shape of the cross-correlation functions) are however difficult to isolate due to S/N dependent variations. Conclusions.
Third, we aim to test the predictions from 3D hydrodynamical models of convection. For these purposes, we present the analyses of high-precision CHEOPS and ESPRESSO measurements of two bright stars with different effective temperatures. The paper is organized as follows. We describe the data reduction methodologies in Sect. 2. We refine the stellar parameters of these two targets in Sect. 3. We analyze the stellar granulation signals for each CHEOPS, TESS and ESPRESSO observations in Sect. 4. We study the links between granulation and stellar properties in Sect. 5. We compare our results with the predictions from 3D hydrodynamical convection models in Sect. 6. We constrain the link between spectroscopic and photometric signatures in Sect. 7. We investigate the relationship between the shape of the cross-correlation functions, flux (as measured by CHEOPS), and RV in Sect.
Observations and data reduction We selected HD 67458 (T eff = 5833 K) and HD 88595 (T eff = 6205 K) as good targets based on their stellar parameters, their low apparent magnitudes, their moderate level of magnetic activity, the a priori absence of identified planets, and the excellent CHEOPS and ESPRESSO visibility windows during the time that was allocated to our program. Our dataset includes 35 CHEOPS orbits, six dedicated nights at ESPRESSO/VLT, and five TESS sectors of observations. We note this is the first time that high-precision spectroscopic data with ESPRESSO have been taken during several full nights for single stars.
CHEOPS CHEOPS  (Benz et al. 2021 ) observed the two bright stars in the visible wavelength range (330-1100 nm). HD 67458 was observed during three visits of T ∼ 8. 15 h each, with a time sampling of ∆t = 34. 3 s. Each of these measurements resulted from 7 individual images taken with an exposure time of τ exp = 4. 9 s and stacked on-board by coadding them pixel-by-pixel. HD 88595 was observed during four visits of T ∼ 8. 15 h each, with a time sampling of ∆t = 37 s. Each of these measurements Notes. Columns are: file keys referring to the files name in the CHEOPS database, dates (UTC), total observation duration (T ), integration time (τ), number of observations (N), duty cycle (DC), and standard deviation of the light curve (σ TOT ). resulted from 10 stacked images taken with an exposure time of τ exp = 3.
ESPRESSO ESPRESSO  (Pepe et al. 2013 ) observed the two targets in highresolution spectroscopy (R ∼ 140 000) in the wavelength range λ ∈ [380, 780] nm, during six full nights 2. Each measurement resulted from an exposure time of τ exp = 60 s. The ESPRESSO observations have been taken simultaneously or contemporaneously with the CHEOPS observations (see Fig. 2 ). They are the first ESPRESSO observations taken during full nights for such a program. Among these six nights of observations at the VLT, one 3 encountered significant technical problems (PLC-ADC communication issues). We remove this RV time series from this study and are left with three RV time series for the solar-like star HD 67458, and two for the hotter star HD 88595. All ESPRESSO data were processed with the Data Reduction Software (DRS) provided with the instrument and publicly available from the ESO pipeline repository 4.
We refer to  Pepe et al. (2021)  for a short description of the main processing steps. Radial velocities were obtained by cross-correlation with binary F9 and F7 masks for HD 67458 and HD 88595, respectively. We note that we had to process the HD 88595 data enforcing an F7 spectral type to override the (wrong) F5 spectral type provided in the OCS. OBJ. SP. TYPE FITS keyword. Given the exquisite short-term RV precision required to characterize stellar granulation, we paid extra attention to the instrumental drift measured on fiber B and the blue-to-red flux balance in the CCF computation. Both effects may introduce instrumental RV systematics within the night if not properly calibrated out. From the processed RV time series of the solar-like star HD 67458, we removed the mean value and we clipped out the 2σ outliers for all nights.
TESS TESS  (Ricker et al. 2015)  observed the two stars in the redoptical bandpass (600-1100 nm). HD 67458 was observed in sectors {7, 8, 34}, and HD 88595 in sectors {9, 35}. In this work we made use of the short-cadence light curves released by the TESS team. However, we do not use the Presearch Data Conditioning Simple Aperture Photometry (PDCSAP) flux because sometimes they are affected by some systematic errors due to over-corrections and/or injection of spurious signals. The light curves we used were obtained applying Cotrending Basis Vectors (CBVs) to the Simple Aperture Photometry (SAP) flux as done in  Nardiello et al. (2021). The CBV were obtained by using the SAP light curves of the stars in the same Camera/CCD in which the targets are located and following the procedure described in detail in  Nardiello et al.
(2019 Nardiello et al. ( , 2020)). For the analysis of the light curves, we rejected the points with DQUALITY>0, as recommended by the TESS Science Data Products Description Document 5. Additionally, we clipped out the points corresponding to a sky background value >3040 e -/s in sector 35 (since this sector was affected by known technical issues such as thermal stability and telescope pointing). We finally clipped out the 3σ outliers of each sectors for both targets. The main characteristics of these observations are listed in  Notes. Columns are: date of observation (UTC), total observation duration (T ), integration time (τ), number of observations (N), duty cycle (DC), and standard deviation of the light curve (σ TOT ). Table  3 , and the lightcurves are shown in the bottom rows of Fig.
In the following, we use both datasets sampled at one point per minute, taken out of the initial higher cadence sequences. For GOLF observations, we used the level-2 GOLF data 6  calibrated as described in  Appourchaux et al. (2018). To be conservative, we only selected the year 1996 from this dataset since, after that, the detector was affected by an instrumental failure. This year corresponds to a solar cycle minima, leading then to a negligible impact of the magnetic regions (which is out-of-scope of the present study). We divided this time series into subseries of 8-h duration to compare them with our ESPRESSO set of observations. From the total number of available 8-h subseries, we selected 75 subseries with a condition of regular sampling (i. e. , we only took the subseries that avoid gaps).
Granulation signals in high-precision photometric and spectroscopic observations Stellar granulation generates stochastic fluctuations in both photometric and spectroscopic observations. These fluctuations are correlated over timescales from some minutes to several hours, depending on the stellar parameters. The objective of this section is to identify the contribution of the stellar granulation among the various sources of noise present in the CHEOPS, TESS and ESPRESSO datasets. We first look at the behavior of these fluctuations in the time domain, and how their amplitudes evolve over different timescales (Sect. 4. 1). We then analyze how they behave when using the common observational strategies to mitigate them (i. e. , long exposure time for RV data acquisition or light curve binning over short timescales), and we discuss these behaviors in the context of small exoplanets detection (Sect.
Amplitude of the granulation signal We assume that each time series results from the contribution of three phenomena: instrumental noise, stellar oscillations, and stellar granulation. We neglect stellar magnetic activity because (i) the chromospheric activity indicator log R ′ HK indicates that the two stars are relatively inactive (log R ′ HK ∼ -4. 9), (ii) no correlation between log R ′ HK and RV is observed, and (iii) our observations are very close in time so any signature of magnetic activity (spot or plages) should act as a trend over the nightly datasets. The instrumental noises are dominated here by the photon noise and affect differently the CHEOPS, TESS and ESPRESSO observations. The stellar oscillations (or p-modes) evolve on short timescales for both stars, typically over 10 min or less.
To this end, we filter each time series with a high-passband filter with cut-off frequencies ν cut. We visually determine these cut-off frequencies ν cut for each dataset by examining the flat regions of the different periodograms (see details in Sect. 4. 3). We then measure the standard deviation of each filtered time series (σ w ) that corresponds to one visit (CHEOPS), one night (ESPRESSO), or one sector (TESS). For TESS, we report the median value of the standard deviations calculated on each 8-h subseries contained in a given sector. For HD 67458, we obtain: σ w = {79, 81, 93} ppm (CHEOPS), σ w = {97, 93, 85} ppm (TESS), and σ w = {2. 1, 1. 6, 0. 95} m/s (ESPRESSO). For HD 88595, we obtain: σ w = {77, 75, 77, 78} ppm (CHEOPS), σ w = {181, 177} ppm (TESS), and σ w = {1. 37, 1. 49} m s -1 (ESPRESSO).