dataesr/scientific-paragraphs-categorization huggingface dataset:
E. M. was supported by an Australian Government Research Training Program (RTP) Scholarship. M. K is grateful to F. Petrov and P. Zatitskii for fruitful conversations. We thank Johannes Schmitt for his help with the SageMath package and for his efforts adapting it to include the possibility of intersecting Chiodo classes of arbitrary parameters. This allowed us to test and correct previous versions of Theorems 1. 5 and 1. 6. We thank Alessandro Giacchetto for independent numerical checks of integrals of Chiodo classes via topological recursion.
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France).
, 2013; Cohan and Arastoopour, 2016). Understanding the effect of rainfall on wind power production is hence highly relevant. The Rainfall Wind Turbine or Turbulence project (RW-Turb), which is supported by the French National Research Agency (ANR in French), actually aims at contributing to the topic. The data presented in the paper were collected in the framework of this project. In order to properly address the topic, two distinct aspects should be studied properly; first, the rainfall effect on the energy resources, and second, the rainfall effect on the conversion process of wind power to electric power by the wind turbine. To this end, rainfall should not only be understood, as commonly done, as a simple rain rate expressed in millimetres per hour (mm h -1 ) but also by considering its full complexity through the spatial and temporal variability in the drop size distribution (DSD).
The data set shared is especially tailored for this point. Indeed, both the wind energy and torque available to wind turbines are basically proportional to the power Published by Copernicus Publications. of the instantaneous wind speed. As wind is neither constant nor uniform, taking into account its small-scale spatiotemporal fluctuations is crucial to properly quantify the integrals of these quantities, especially given that the wind turbines are located in the atmospheric boundary layer, which is an area of increased complexity due to the interactions with the ground. Improving turbulence understanding has been listed among the scientific challenges of this field in a recent joint paper by leading academics of the field  (van Kuik et al. , 2016,  for the European Academy of Wind Energy, EAWE).
The devices' functioning and the measurement campaign is presented in Sect. 2. The corresponding database and available tools are presented in Sect. 3. Before proceeding further, the purpose of the paper should be clarified to avoid any misunderstanding. It is a data paper that aims at presenting, in detail, a data set made available to the community. It does not aim at fully exploiting the data set for scientific studies; this will be done in further dedicated papers by the authors or community members using it. The data set was collected in a framework designed for application in wind energy, but potential applications of such a high-resolution rainfall and wind measurement campaign are much wider. For example, understanding rainfall processes remains a major challenge in the field of hydrology.
Disdrometer and associated outputs We used two OTT Parsivel 2 disdrometers (OTT, 2014) in this measurement campaign. OTT is the name of the manufacturer. Such a device gives access to the size and velocity of the drops passing through its sampling area. The data regarding the disdrometer are actually similar to the data already discussed in  Gires et al. (2018). Hence, the interested reader is referred to this paper and references therein for a detailed presentation of the devices and associated output. It will only be reiterated here that the main output of the disdrometer is actually a matrix with the number of drops recorded (n i,j ) during the time step t (30 s here), according to the classes of equivolumic diameter (index i and defined by a centre D i and a width D i expressed in mm) and fall velocity (index j and defined by a centre v j and a width v j expressed in m s -1 ).
Measurement period The data presented in this paper were collected between 1 March 2020 and 31 May 2020. Over this 3-month period, there is a limited number of missing time steps with only 3840, 4237, 3691, 3734, 3658, and 3658 missing minutes for, respectively, anemometer no. 1, anemometer no. 2, station no. 1, station no. 2, disdrometer no. 1, and disdrometer no. 2. In the worst case, it corresponds to approximately 3 % of the time. They mainly correspond to periods of power outages on the mast, which turns off the devices and the computer retrieving the data. They are mainly short (a few minutes) power cuts, except on 8-10 March during which the power remained out for more than 2 d. A few more minutes are missing with the anemometers. This is likely because of the sporadic loss of information by the retrieving computer due to the high sampling frequency.
Overall organization The structure of the database is actually inherited from the data management flow, which is basically similar for all the devices. -Raw data are initially collected in the form of txt files corresponding directly to the outputs of the device. The data are stored in files corresponding to 1 min of data for stations and anemometers and 30 s for disdrometers. -All of these files are then zipped for individual days and stored in a corresponding folder in Raw_data_zip/. -These raw files are then used to generate daily (30 s or 1 Hz) or hourly (100 Hz for anemometer) files in an easy-to-read format. The csv files, then zipped to limit their size) are used for stations and anemometers. The npy files are used for Parsivel. They are stored in the corresponding folders of the database for which the name is quite explicit.
3D anemometer data The raw data are made of one txt file per minute directly containing the output telegram of the device. They are called Raw_DeviceName_YYYYMMDD_HHMM. txt. Such a file is made of 6000 lines, corresponding to 1 min, at a sampling rate of 100 Hz, and having the following format: b'02+000. 03;-000. 02;-000. 01;+22. 9;0E;47 • 03', where the 3D wind is given, followed by the virtual sonic temperature and two status codes of the device. These files are then zipped per day and stored in the corresponding folder Raw_data_zip/. Finally, some csv files containing all the relevant data are generated for each device. Hourly files are used for the data at 100 Hz and daily files for the data at 1 Hz (obtained simply by averaging 100 successive time steps). They are then stored in the corresponding folder of the database (see Sect.
They are then stored in the corresponding folder of the database (see Sect. 3. 1). The format is (i) one line per 1 s time step starting on YYYY-MM-DD at 00:00:00 (UTC time). (ii) In each line, values of measurement are provided separated by semicolon, and (iii) Missing data are noted as nan. In each line, the order of the data is as follows: wind speed (m s -1 ) wind direction ( • ; starting clockwise from the north of the device) virtual temperature (.
Disdrometer data As for the other devices, the raw data are made of one txt file per 30 s time step containing all the collected data. It should be stressed that it corresponds to the raw data and is made available for expert users only. Most users will not need to access them and will be satisfied with the provided Python scripts. The precise format for these raw fields can be found in the Python scripts with the heading of corresponding functions. Finally, a daily file containing a list of the data collected by the devices for each time step is generated and stored on the corresponding folder of Daily_data_python_disdrometer/. They are stored in npy format and are readable with the help of Python 3. The Python scripts made available actually use these files. More precisely, each element of the list corresponds to a time step of 30 s.
Python_scripts/ This section presents some Python scripts that are designed to enable the user carry out some initial analysis and basic data treatment with the database. The functions, which are basic toolboxes, can be found in the script Tools_overall_management_RW_Turb_data_paper. py. The main functions are as follows (only a short description is given here; more details, including precise description of the inputs and outputs of the functions, are provided as comments inside each script): -Quicklook_RW_Turb_wind_farm generates a quick look image and the corresponding 30 s and 5 min rain rate time series for a given rainfall event during the measurement campaign on the wind farm. -extracting_one_event_Parsivel reads daily npy files and generates a list containing all the data that can be analysed.
-extracting_one_event_anemometer_100Hz reads daily files at 100 Hz and generates a matrix with the data for a given anemometer and event. -extracting_one_event_anemometer_1Hz reads daily files at 1 Hz and generates a matrix with the data for a given anemometer and event. -extracting_one_event_station_1Hz reads daily files at 1 Hz and generates a matrix with the data for a given station and event. Examples for use of the various functions can be found in the scripts Script_overall_management_parsivel_v1. py, Script_overall_management_station_v1. py, Script_overall_management_anemometer_v1. py, and Script_overall_management_RW_Turb_campaign_v1. py. On a standard laptop, it typically takes few seconds to extract and display all the data for 1 d.
Data availability The data from a 3-month measurement campaign with devices installed on a meteorological mast of Boralex located at the wind farm of Pays d'Othe are presented in this paper. Raw data along with Python-formatted data with corresponding scripts are described. The Hydrology Meteorology and Complexity laboratory of École des Ponts Paris-Tech (HM&Co-ENPC) has made this data set available at https://doi. org/10. 5281/zenodo. 5801900 (Gires et al. , 2021). The following citation should be used for every use of the data: -For this paper, https://doi. org/10. 5194/essd-2021-463. -For the database, Gires, A. , Jose, J. , Tchiguirinskaia, I. , and Schertzer, D. : Data for: "Three months of combined high resolution rainfall and wind data collected on a wind farm", Zenodo [data set], https://doi.
org/10. 5281/zenodo. 5801900, 2021. This data set is available for download free of charge. License terms apply. The campaign is actually still ongoing. Regular updates of its status, along with updates of the database, are to be provided through the lab's website (http: //hmco. enpc. fr, last access: 16 August 2022). The web page https://hmco. enpc. fr/portfolio-archive/rw-turb/ (last access: 16 August 2022) already contains links to the summary calendars for past and ongoing measurement campaigns (daily updates). The database will continue to be organized as presented in this paper, and users interested in a longer series, once available, can contact the corresponding author.
Effective resolution of the data While studying the small-scale space-time fluctuations, it is advantageous to use data at the finest available resolution. However, it is possible that the actual sampling resolution may be different due to quality problems in the series, leaving spurious estimates at finer scales. To understand this, 100 Hz data from anemometers and 1 Hz data from meteorological station were analysed using spectral analysis and the framework of universal multifractals (UMs). Spectral analysis is a commonly used technique in turbulence to estimate scaling behaviour using second order statistics. In the case of scaling behaviour, power spectrum E(k) and frequency are power law related, as follows: E(k) ≈ k -β , ( 3 ) where k is the corresponding frequency, and β is the spectral exponent (slope in log-log plot).
C 1 measures mean intermittency in the field; when C 1 = 0, the field is homogeneous with little variability. α quantifies how much this intermittency changes when moving away from the average behaviour. 0 ≤ α ≤ 2; the higher the value of α, the higher the variability, with α = 0 being a monofractal field where an extreme intermittency is the same as that of mean. A multifractal analysis of collected data is performed to check for the effective resolution of the data, i. e. to assess if measurements are affected or not by instrumental artefacts at small scales. Given the stated purpose, only small scales (i. e. from 16 s down to 0. 01 s) are studied here. Analysis and interpretation of larger-scale regimes will be carried out in further scientific papers. Spectral analysis, which consists of plotting Eq.
Acknowledgements. The authors would like to thank  Boralex , and notably  Ernani Schnorenberger , for facilitating access to the meteorological mast they are operating on the Pays d'Othe wind farm. Financial support. This work has been supported by the  ANR JCJC RW-Turb  project (grant no. ANR-19-CE05-0022 ). Review statement. This paper was edited by  David Carlson  and reviewed by two anonymous referees.
Calcium concentration fluctuations are known to reflect neuronal activity in a very complex way, as ions can flow from many sources that interact non-linearly with each other. There is thus an enhanced need for modeling to resolve what can be seen as an inverse problem: finding from experimental recordings the markers of synaptic activity, and distinguish in the calcium signals the different calcium sources. Here, we present Sinaps, which is an easy-to-use Python library to simulate voltage propagation, ionic electrodiffusion and chemical reactions in neurons.
Statement of need Numerous softwares have been designed to realize simulations of voltage dynamics and ion concentration in neurons  (Bower & Beeman, 1998 ; Carnevale & Hines, 2006). Most of those softwares are using the Cable theory model, and are designed toward neuronal network simulation. Models for ion concentration dynamics in neurons, based on the Nernst-Planck model coupled to specific equations describing the potential, are also available  (Saetra et al. , 2020 ; Solbrå et al. , 2018). Among these models, the Sinaps library is designed to realize the fast computation of voltage propagation and ionic electrodiffusion in complex neuronal geometries, at a fine spatial scale. It is based on the Cable equation for voltage propagation, coupled to a one-dimensional Nernst-Planck equation for ionic electrodiffusion.
Our integrated statistical approach enabled us to analyze -omics and clinical data individually and in conjunction with one another while accounting for a range of potential confounders. We refer to scores between 1-4 as mild and scores between 5-8 as severe disease in further discussion. Sampling time points are represented according to the days after symptom onset for patients. The observation and hospitalization period is marked with a solid line, or a dashed gray line when prolonged. Sample materials included oropharyngeal swabs, plasma, peripheral mononuclear blood cells (PBMCs), urine, stool and tracheobronchial secretions (TBS). The use of antibiotics shortly before or during the sampling period is marked for each participant. All control subjects were antibiotic-free for at least 3 months before and during the sampling period.
ScRNAseq data analysis The 10x Genomics CellRanger pipeline (v4. 0. 0) was used to pre-process the sequencing data. In brief, BCL files from each library were converted to FASTQ reads using bcl2fastq Conversion Software (Illumina) using the respective sample sheet with the 10x barcodes and TotalSeq antibodies utilized. Then, the reads were further aligned to the reference genome provided by 10x Genomics (Human reference dataset refdata-cellranger-GRCh38-3. 0. 0) and a digital gene expression matrix was generated to record the number of UMIs for each gene in each cell. Next, the expression matrix from each library was loaded to R/Seurat packages 61 (v4. 0. 1) for downstream analysis. To control the data quality, we further excluded low-quality cells with >15% mitochondrial reads, < 100 or > 3,000 expressed genes, or < 500 UMI counts.
Acquisition method parameters and UHPLC gradient for LC and FIA mode are shown in Table  S9 -11. All compounds were identified and quantified using isotopically-labeled internal standards and multiple reaction monitoring (MRM) for LC and full MS for FIA as optimized and raw data was computed in MetIDQ TM version Oxygen (Biocrates Life Science AG, Innsbruck, Austria). A script developed inhouse (MetaQUAC) was used for data quality analysis and preprocessing  66. Quality assurance and control were reported using the recommended standards by mQACC (Table  S8 ).
25 µm film thickness. Helium was used as the carrier gas with a flow rate of 1. 2 mL/min. The spectra were recorded in a mass range of 60 to 600 m/z with 10 spectra/second. Each sample was measured twice (technical replicates). The GC-MS chromatograms were processed with the ChromaTOF software (LECO Corporation) including baseline assessment, peak picking and computation of the area and height of peaks without a calibration by using an in-house created reference and library containing the top 3 masses by intensity for 42 metabolites (55 intermediates; Table  S12 ) related to the central carbon metabolism. The data were exported and merged using an in-house written R script. The peak area of each metabolite was calculated by normalization to the internal standard cinnamic acid. Relative quantities were used.
6 min) and 97/3 (from 6. 75 to 10 min). The molecular ion and at least two transitions were monitored for the 15 metabolites that are part of the tryptophan pathway. Data was exported into Skyline (v. 19. 1, 64-bit) to identify and quantify peak intensity and area. Transition settings in the Skyline search were: isotopic peaks included: count; precursor mass analyzer: QIT; acquisition method: targeted; product mass analyzer: QIT. Method match searching tolerance was 0. 6 m/z, and data was manually checked to ensure the correct peaks were selected. Cubic spline drift correction was applied per metabolite and to all sample types using the pooled quality control (QC) samples as references to fit the splines. The first and the last QC samples used to fit the cubic splines are the most critical to the resulting fit.
Hence, metabolites cannot be considered as absolutely but as relatively quantified in this study. In-house R scripts were used for internal standard normalization, calibration, statistics and plotting. Quality assurance and control were reported using the recommended standards be mQACC (Table  S8 ): Taxonomic and functional microbiome profiling Whole genome shotgun sequencing reads were analyzed using the NGLess pipeline (v1. 3. 0). Sequences were quality controlled, trimmed and merged using NGLess defaults and subsequently filtered for human reads. One stool sample and two TBS samples were of insufficient quality and removed from further analysis. Taxonomic assignment was performed using the mOTUs profiler (v2. 6). Functional annotation was performed by mapping rarefied reads to the Global Microbial Gene Catalog (GMGC, v1.
Statistical testing of -omics data and post-hoc confounder analysis with clinical variables All statistical analysis was performed with R (v4. 0. 3) using the targets workflow manager (v0. 12. 1) and renv environment manager (v0. 12. 5) to enhance reproducibility. All figures were generated using ggplot2 (v3. 3. 3) and patchwork (v1. 1. 1). Testing was performed using the metadeconfoundR package (v0. 2. 7) as described in Forslund et al. 69  (especially Extended Data Figure  1  for a graphical overview) and briefly described here. To identify which clinical variables associated with -omics features, standardized, non-parametric effect sizes (Cliff's delta and the Spearman correlation for binary and continuous variables, respectively) were calculated and tested for significance in a first step.
Acknowledgments The authors are grateful to all patients, their relatives, and volunteer controls for consenting to biosampling and data collection. We thank all members of the  Pa-COVID collaborative  study group. We thank  Alina Eisenberger  ( BIH Metabolomics Platform ) for technical assistance. This work was supported by start-up funding by the  Berlin Institute of Health (BIH)  to JAK, SKF, and BO, and by the  Deutsche Forschungsgemeinschaft  ( SFB-TR84 A1/A5 ) to BO. TRL and TS were supported by the  VolkswagenStiftung's initiative  " Niedersächsisches Vorab and Ministry of Lower Saxony  ( MWK , Project  76251-99 ). SKF was supported by the  German Center for Cardiovascular Research , the  German Research Council  (projects  SFB1365 ,  SFB1470  and  KFO339 ) and the  German Ministry of Education and Research.
EW and ML are supported by the Project " Virological and immunological determinants of COVID-19 pathogenesis -lessons to get prepared for future pandemics  ( KA1-Co-02 'COVIPA ')", a grant from the  Helmholtz Association's Initiative and Networking Fund. CT was supported by the  Deutsche Forschungsgemeinschaft  (project number  400667201 ). VC was supported by the  German Ministry of Education and Research  through project  VARIPath  ( 01KI2021 ). VC is a participant in the  BIH-Charité Clinician Scientist Program  funded by  Charité-Universitätsmedizin Berlin  and the  Berlin Institute of Health.
Data and Code Availability Raw sequencing data will be deposited and made publicly available before publication. The metabolomics data are available on MetaboLights 70 with the unique identifier MTBLS6600 (www. ebi. ac. uk/metabolights/MTBLS6600). All supplemental, processed data tables are uploaded separately and the code to perform the confounder and integrated statistical analyses are hosted at https://github. com/sxmorgan/pa-covid-multi-omics. Any further information required to reanalyze the data in this manuscript is available from the lead author upon request.
Background: Bladder cancer survivors and their caregivers face profound practical (eg, use of stoma appliances and care for urinary diversion methods) and psychosocial (eg, depression and anxiety) challenges after surgical treatment with cystectomy. Objective: To improve the health-related quality of life and postsurgical outcomes of both bladder cancer survivors and their caregivers, the team, in collaboration with Sourcetop, Inc (software design) and Dappersmith (graphic design), developed the Cancer Resource and Information Support (CRIS) software. The purpose of this manuscript is to report on the development and usability testing of the CRIS software.
Methods: The development of the CRIS software was guided by the Obesity-Related Behavioral Intervention Trials (ORBIT) model for developing behavioral treatments for chronic diseases. The ORBIT model is unique in that it proposes a flexible and progressive process with prespecific clinically significant milestones for forward movement and returns to earlier stages for refinement, and it facilitates communication among diverse groups by using terminology from the drug development model. This paper focuses on 2 phases of the ORBIT model: phase IA: define and IB: refine. During phase IA, the study team developed solutions for the stated clinical problem-adjustment to life post cystectomy-by reviewing the literature and collecting feedback from clinicians, professional organizations, bladder cancer survivors, and their caregivers.