dataesr/scientific-paragraphs-categorization huggingface dataset:
This is highlighted with an initial characterization of some example datasets that could feed into EBV production (reflecting their status in the year 2018). that inform SP EBVs globally, for example, via central aggregators GBIF and OBIS, via taxon-region specific efforts with unrestricted data and metadata sharing or via a direct link or publication to global SP EBV producing infrastructure such as Map of Life. • Collection, recognition and sharing of inventory data and detection-relevant metadata. We highlight inventories and associated non-detection information as key for effective species population inference. All too often, these data are simplified to presence records, or other detection-relevant information, for example, on taxonomic scope or observer qualification, is not being shared.
We advocate for general infrastructure to support this capture. • Recognition of the relative, complementary value any primary biodiversity datasets have in the context of other biodiversity data, environmental data and models. This directly emerges from the SP EBV concept, and we suggest its use for designing monitoring efforts or incentivizing and supporting data collection. • Recognition of the role of agency-based and private remote sensing. Near-global remote sensing, in particular as conducted by United States' NASA, the European Space Agency ESA and other space agencies that make their data freely available, is a key enabler of species population inference, empowering beneficiaries of biodiversity change information worldwide. This arena of impact and societal value deserves stronger recognition and support by agencies, governments and business.
Acknowledgements This study and its definitions and recommendations are the official, consensus outputs of the Species Populations Working Group of  GEO BON  (https://geobon. org/ebvs/workinggroups/species-populations)and are the outcome of multiple workshops and group meetings in 2015 to 2018. We are grateful to the  GEO BON Secretariat  and the  German Centre for Integrative Biodiversity Research (iDiv)  for facilitation. We acknowledge  NSF  grants  DBI-1262600  and  DEB-1441737  and  NASA  Grants  80NSSC17K0282  and  80NSSC18K0435  to W. J. The research described in this paper was in part carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the NASA; government sponsorship is acknowledged.
II. FARWINDER DESIGN AND COST OF ELECTRICITY Using a velocity and power prediction program developed in previous work  [8] , we developed a preliminary design for the FARWINDER, Fig. 2. It is a 80 m long catamaran fitted with three 30 m Flettner rotors, acting as sails. The catamaran hull shape is inspired by the 86 m long 26 m wide HMAS Jervis Bay wave piercing catamaran  [17]. Flettner rotors are rotating vertical cylinders with their axis perpendicular to the wind. Thanks to the Magnus effect, the action of the wind on the cylinder generates a lift force which is perpendicular to both the wind direction and the axis of the cylinder. 30 m tall Flettner rotors are commercially available from the company Norsepower  [18]. The diameter of the rotor of the water turbine is 6 m. We estimated that this design can generate 1 MW of electric power for a true wind speed of 10 m/s  [19].
The cost for power production from coal or natural gas is in the middle (~35 -50 €/tonne). Therefore, we will retain 20 €/tonne for the low end of the range of CO2 production cost, and 200 €/tonne for the high end. Tab. 2 summarizes the cost data for the proposed FARWIND energy system. We recall that it comprises 112 FARWINDERs and 5 tankers (see previous section). For the FARWINDERs, only the cost of the power-tomethanol plant is considered in the capital cost because other cost sources are already taken into account in the cost of electricity available on-board. Using the cost data and assuming that the lifetime of the system is 20 years and that the capital recovery factor is 9% (interest rate of 7%), the methanol cost can be estimated  [43]. It leads to a cost in range of 1. 42 to 1. 68 €/kg (259 to 305 €/MWhth) depending on the cost of CO2.
More recently,  Williams et al. (2017)  have implemented a wave module in the semi-lagrangian sea ice model neXtSIM  (Rampal et al. , 2016)  and found that high waves conditions can induce a significant displacement of the sea ice edge. The implementation of FSDs in different sea ice models as done by  Zhang et al. (2015)  or  Horvat and Tziperman (2015)  has also allowed the assessment of the potential enhancement of lateral melt by wave-induced ice fragmentation  (Zhang et al. , 2016; Bennetts et al. , 2017) , but the representation of waves remains too crude to simulate the full effect of waves on the evolution of sea ice. In the present study, we go beyond the simple inclusion of the forcing of wave by sea ice or of sea ice by wave in models, by proposing a full coupling between a spectral wave model and a state-of-art sea ice model.
Acknowledgements. G. B. and F. A. are supported by  DGA ,  ANR  grants  ANR-14-CE01-0012   MIMOSA ,  ANR-10-LABX-19-01 ,  EU-FP7  project  SWARP  under grant agreement  607476 ,  ONR  grant number  N0001416WX01117. Part of this work has been carried out as part of the  Copernicus Marine Environment Monitoring Service (CMEMS) ArcticMix and WIzARd  projects. CMEMS is implemented by Mercator.
Patients Between April 2015 and January 2017, all consecutive patients referred for PET imaging with a diagnosis of NSCLC and who were treated with anti-programmed death-1 checkpoint inhibitor, nivolumab, were retrospectively selected from our database. Patients needed to have undergone a PET scan before the start of the treatment (SCAN-1) and to have at least a second evaluation PET scan 2 months after treatment (SCAN-2). Nivolumab was prescribed according to current recommendations and the clinicians' judgment. Treatment continuation was decided according to the clinical status and the evaluation of the imaging findings. To receive nivolumab, all patients had to have an Eastern Cooperative Oncology Group performance status < 2.
Statistical analysis Overall survival (OS) was defined as the time from the start of immunotherapy to death from any cause. The 1-year survival rate in responders and non-responders was recorded. We compared OS and the 1-year survival rate between responders and non-responders. The Kaplan-Meier method was used to estimate survival probability, with the level of significance estimated by the log-rank test. The difference between the continuous data for the two groups was determined by the Mann-Whitney test. Statistical analyses were performed using R Core Team (2014) software. The results were considered statistically significant when p < 0. 05.
Background: Analyses of replicates in sets of discrete data, typically acquired in multi-well plate formats, is a recurring task in many contemporary areas in the Life Sciences. The availability of accessible cross-platform data analysis tools for such fundamental tasks in varied projects and environments is an important prerequisite to ensuring a reliable and timely turnaround as well as to provide practical analytical tools for student training. Results: We have developed an easy-to-use, interactive software tool for the analysis of multiple data sets comprising replicates of discrete bivariate data points. For each dataset, the software identifies the replicate data points from a defined matrix layout and calculates their means and standard errors. The averaged values are then automatically fitted using either a linear or a logistic dose response function.
Background Contemporary research in the Life Sciences makes use of many experimental techniques that acquire data from multi-well plate assays. As intended by the manufacturers of plate readers, the acquisition software provided with the equipment can be used to process and analyse data generated by individual instruments. In particular, in busy laboratory environments, off-line analysis is, in our experience, the preferred option of many users; however, in most instances, this requires the purchase of software licences. Additionally, most of the commercially available software in this context is available only for Microsoft Windows operating systems, such as, for example, MultiCycle (ActiMetrics), Gen5 (BioTek), Multi-Mode Analysis Software (Molecular Devices), and Multiwell-Analyzer (Multi Channel Systems).
Few non-commercial software applications addressing multi-well plate data analysis have been reported. The majority of them have been specifically designed for particular tasks, such as the analysis of single-cell migration  [1]  or differential scanning fluorimetry data  [2]. The application BiAnaCa was reported as a tool for general biochemical assays  [3] , but does not appear to be available for download. The open source laboratory information system Brunn  [4]  embraces an end-to-end concept for multi-well plate experiments and has been implemented as a database system requiring the setup of server and clients. In addition to the mentioned applications, which all provide a graphical user interface, there are also libraries for the statistical package R  [5] , such as 'platetools' (https://cran.
r-project. org/package=platetools), 'plater' (https://cran. r-project. org/package=plater) and 'phenoScreen' (https://rpubs. com/Swarchal/phenoScreen); however, neither of them have any dose-response fitting functionality. Based on the concept of a program collection for structural biology and biophysical chemistry (PCSB)  [6] , we have developed several stand-alone portable applications for a variety of analytical tasks with an emphasis on intuitive and convenient usage. Here, we present a user-friendly application, called DRfit, for the analysis, visualisation and curation of bivariate discrete data obtained from multi-well plate assays. The software includes automated and manual fitting of averaged data from replicate data sets as well as convenient data import and export features. DRfit closes a current gap in the landscape of multi-well plate analysis software and provides a free cross-platform tool for interactive analysis of multi-well plate data for a wide variety of assays.
Implementation Design DRfit has been designed as an analysis pipeline to process replicate data obtained from end-point assays (discrete data) in a defined layout. In most cases, such data will be acquired in assays using multi-well plates, but it is also possible to enter data from individual experiments. Based on a user-specified plate setup, replicate data can be processed for multiple datasets. For each data set, the mean and variation (standard deviation, standard error, 95% confidence interval) will be calculated and an automated fit to a user-selected function will be attempted. Numerical results of the automated fitting are displayed in a spreadsheet. With a mouse right-click on a row in this spreadsheet, the user can trigger a plot window to visualise the averaged data points in a scatter plot with the superimposed fit.
From the fitting panel in this window, the user can trigger new data fits by entering start values for the fit function. Additionally, one can adjust the current fit parameters manually through the use of graphical sliders. In the design of this software, special emphasis has been placed on an intuitive and clean graphical user interface (GUI) that makes this software easy to use. The GUI is available in English, French and German. Data can be entered directly into the DRfit spreadsheets or imported from Microsoft Excel files. To allow tracking of calculations and data transformation, ASCII log files are output at every step throughout the analysis pipeline. For user convenience, data plots and superimposed fits can be written automatically into image files and all results from an analysis session can be exported at once into a Microsoft Excel workbook.
Architecture This software builds on the Java libraries that we have developed for the Program Collection for Structural Biology and Biophysical Chemistry  [6] ; in particular, it uses the GraphPanel class which was originally developed for the interactive analysis and fitting application SDAR  [7]. For automated fitting, the Levenberg-Marquardt minimisation module developed by J. P. Lewis (http://scribblethink. org/ index. html) has been implemented; import/export functionality of Microsoft Excel-formatted spreadsheets uses JExcelApi established by Andy Khan (http://jexcelapi. sourceforge. net/). DRfit is platform-independent and can be deployed on Windows, Linux or MacOS; a detailed software manual (see Additional file 1), example data sets (see Additional file 2), as well as a video tutorial are available on the project web site.
The so-called 3σ edit rule, based on the fact that the probability to observe data points further away from the mean than 3 standard deviations is only 0. 3%  [9] , has proven rather ineffective in practical applications  [10]. Owing to a recommendation by the International Organization for Standardization, Dixon's Q test has largely been replaced with the Grubbs test as a commonly used procedure  [11]. This latter test is based on the difference of the mean of the sample and the farthermost data, and also considers the standard deviation  [12, 13]. Capitalising on Java classes developed earlier for our DMAN software  [2] , we have implemented Grubbs' test for outliers in the present software. This can be actioned through Check for Outliers from the Tools menu bar item. If this feature is activated, an outlier test will be done as the very first step of the automated analysis pipeline.
Conclusions DRfit is a cross-platform software tool for processing multiple sets of discrete data points acquired as replicates. All data sets to be processed within one session need to possess the same layout. This current software has been designed with practicality and convenience in mind and features a range of processing options, including automated fitting and generation of image files as well as import/export functionality from/to Microsoft Excel spreadsheets. HMPDH and AS tested the software, provided translations and input to the software manual. AH wrote the software manual and the paper with critical input from all authors. All authors read and approved the final manuscript.
, 2016; Magua et al. , 2017; Tamblyn, Girard, Qian, & Hanley, 2018; R. van der Lee & Ellemers, 2015). Women applicants experience biased assessments based on pervasive gendered assumptions about quality and merit  (Witteman, Hendricks, Straus, & Tannenbaum, 2019). Author contributions have also demonstrated a gendered division of labour within research teams: women are more likely to perform the 'physical' labour and men the 'conceptual' labour  (Macaluso, Larivière, Sugimoto, & Sugimoto, 2016). Across all fields in the JSTOR database, including demography and pollution and occupational health, women are less likely to be associated with the more prestigious roles of first or last author  (West, Jacquet, King, Correll, & Bergstrom, 2013)  which often correspond to the Principal Investigator or senior researcher.
Women academics are often described as 'less productive,' and this observation has been conceptualized as a 'productivity puzzle'  (Albert, Davia, & Legazpe, 2016; Dehdarirad, Villarroya, & Barrios, 2015; Larivière, Vignola-Gagné, Villeneuve, Gélinas, & Gingras, 2011; Mauleón, Hillán, Moreno, Gómez, & Bordons, 2013; Reza Davarpanah & Moradi Moghadam, 2012). While these studies document a puzzle, they offer few explanations, due to the limited amount of data within large-scale bibliometric databases, such as Web of Science. Other studies posit that biases are at work in the discrepancy between roles of men and women; for example, a large bibliometric study of the Frontiers journals found that women represent 37% of authors, 28% of reviewers and 26% of editors across disciplines  (Helmer et al.
Data analysis We employed a thematic analysis of the interview data using the six steps suggested by Braun and Clarke  (Braun & Clarke, 2006). For familiarization, we reviewed the transcripts against the recordings. We read and re-read paper copies of each transcript. Second, we developed initial codes by hand. This allowed us to re-examine the codes in context and identify new codes. The first author developed a charting framework in Excel 2016 and M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 11 copied quotes against each code. We then reviewed the codes within each interview to ensure consistency. We grouped codes into broader categories of data (e. g. 'gender-blind'). We compared codes and quotes across interviews and grouped them into subthemes. We reviewed themes and reorganized data that did not fit the original theme.
1i ). The powder material (MIN-U-SIL 5) is > 99 % quartz, and according to the product sheet, very small amounts of Fe, Al, Ti, Al, Ca, Mg, Na, and K are also present. The SEM-SE image of the powder sample shows grains from submicron size to ∼ 8 µm (Fig. 1j ). The amount of adsorbed water in the powder is ∼ 0. 25 wt %, as measured by weight loss after heating at 120 • C. The powder re-adsorbs the same amount of water after ca. 1 h. In contrast, the amount of adsorbed water in the novaculite is below the detection limit using the same method. The grain size of the starting novaculite sample was determined from EBSD band contrast images (Fig. 1i ). Outlines of each grain were traced and grain sizes determined using Image J software as diameters of equivalent circles (root mean square; Fig. 2a ).
Our grain growth laws indicate that after the deformation stage, dynamically recrystallized grains can grow, especially under lower-crustal conditions, when the system has enough time for grain growth. As a consequence, when grain size piezometers are applied to natural quartz, we should bear in mind that these are minimum stresses because grain growth might have taken place. Even during the deformation stage, grain growth might compete with grain size reduction in dynamic recrystallization when the strain rate is < 10 -12 s -1. Di Carlo for use of the SEM. Takamoto Okudaira is also thanked for useful discussions. Reviews by Andreas Kronenberg and an anonymous reviewer are gratefully acknowledged. This research was supported by Grants-in-Aid for Scientific Research on Innovative Areas (KAKENHI 15K21755 and 26109005) from the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), by a Grant-in-Aid for Scientific Research (KAKENHI 19K04041) from the Japan Society for the Promotion of Science (JSPS), and by the Labex VOLTAIRE (ANR-10-LABX-100-01) from the Ministère de l'Enseignement Supérieur et de la Recherche.
In randomized clinical trials (RCT), the analysis is based on the intent-totreat principle to avoid any selection bias in the constitution of groups. However, estimates of overall survival can be biased when significant crossover occurs because the separation of randomized groups is lost. To handle these switches, the inverse probability of censoring weighting (IPCW) method has been proposed; however, it is still poorly used in RCT, notably because of its complex implementation. In particular, for time-to-event outcomes, it can be difficult to format data, especially when time-dependent covariates have to be managed, with different measurement times between patients. This paper aims to present the R package ipcwswitch with some guidance for the analysis of the treatment effect on survival in a hypothetical setting where all patients would have continued to take the randomization treatment.
Recently, several authors gave some practical guidelines to facilitate the use of the IPCW method. For example, Dodd et al. [12]  provided a Stata code to implement the weights and to fit the model. Willems et al. [11]  proposed a "user friendly" implementation of IPCW in R  [13]. Notably, using the survSplit function from the survival package  [14]  on a toy example, the authors provided a nice four steps algorithm, which can be easily reproduced. However, with regards to time-dependent covariates, the authors referred to the unfold function from the RcmdrPlugin. survival package  [15]. Using such a function could be not straightforward, especially when dealing with different observation times of measurement for the patients. Indeed, data processing for time-dependent covariates using the survSplit function could be tricky since it requires the additional building of a function that generates the time-dependent covariates from the output of survSplit (see, e.
g. , page 19 of Thomas and Reyes  [16] ). Then, up to our knowledge, there is no available straightforward R function dealing with time-dependent covariates when times of measurements are not the same. In this article, we propose the R package ipcwswitch to estimate the treatment effect on OS in the RCT setting, taking into account the actual treatment switches over time. The package is available from the Comprehensive R Archive Network at https://CRAN. R-project. org/package=ipcwswitch. We present a step-by-step tutorial on how the IPCW modeling can be used in this framework, i. e. , when informative non-compliance to the treatment allocated by randomization may occur. Note that our aim is not to assess the performances of the IPCW method. For this, we refer the reader to the works of Latimer et al.
(see e. g. ,Latimer et al. [4] ). By contrast, our aim is to provide tools for statisticians to facilitate the use of this method in the specific setting of RCT with a right-censored end point and time-dependent covariates with different times of measurement across patients. This paper is structured as follows. In Section 2 we give the key principles to IPCW. We describe a step-by-step overview of the IPCW implementation in Section 3. We illustrate the use of the package ipcwswitch in a case study, in Section 4. Some concluding remarks are given in Section 5.
As a result, values of the covariates are to be computed for all at-risk patients at each failure time (due to the treatment censoring or to the death) in the dataset. We used a counting process method with multiple records for each individual, where each record corresponds to a time interval during which all covariates remain constant. For further analyses, the required variables are the patient's id, the times of death lastdt and the treatment censoring times that correspond to the times of treatment switch swtrtdt, the status indicator status, the fixed covariates age, the time-dependent covariate ps1, ps2, ps3 along with their times of change randt, dt2, dt3, and the time of treatment interruption swtrtdt. Step 1: Data formatting -wide vs. long format Before computing weights using a time-dependent Cox model with timedependent confounders and time-dependent treatment, the estimation of the outcome models requires some tricky data formatting.
The wide format refers to a dataset with one row per subject, while the long format refers to a dataset with possibly several rows per subject. When data are provided in the wide format, some data processing are needed to build separated rows according to the observed changes in the time-varying confounders, the observed changes in the administered treatment (i. e. , treatment censoring time), and the end of the follow-up (i. e. , death or lost of follow-up). Functions are available to convert the wide format to the long format, such as the survSplit function from the survival package  [14]. Nevertheless, to our knowledge, there is no available straightforward R function dealing with time-dependent covariates when patients do not share the same dates of measurements. Thus, we developed the specific R package ipcwswitch.