dataesr/scientific-paragraphs-categorization huggingface dataset:
The README should outline exactly what is required in order to reproduce the analysis, including the requirements and the instructions to reproduce. Typically it is written in the Markdown format, which should be familiar to those using R Markdown or Jupyter notebooks. Hardware requirements such as RAM, CPU architecture, GPU needs, etc need to be outlined. Software requirements, such as operating system, dependencies, container engine, workflow manager, etc, also need to be described. The exact instructions for reproduction should be clearly outlined. This should include what the output objects of the analysis are, and instructions on how to access the results and what should be expected. Literate programming allows for thorough documentation of workflow methods and results which makes it more accessible for reproducers to comprehend the workflow details, which is not possible with standard code comments  [135].
These instructions should be tested whenever changes are made to the codebase. As outlined above, one of the goals is to reduce the complexity of reproduction, in particular by minimizing the number of commands required. This also makes the README documentation much simpler. For example, the pathway analysis workflow we previously developed could be reproduced and inspected with just six commands, starting from a new Ubuntu Linux installation  [136]. By minimizing the complexity of reproduction and writing the instructions clearly in the README, reproducibility is made possible to a larger segment of the computational research community, especially those who are not experts in the languages used. In addition, the README should outline the contents of the repository, how users can contribute to the project and how to report issues, such as code errors and discrepancies.
Continuous validation When these principles are not integrated correctly and checked, problems can readily emerge  [6, 7, 138]. This is why regular code testing after making updates to the code or data is considered best practice  [22, 135, 139, 140]. For bioinformatics software, testing may involve a suite of unit tests of each function, and another integration test whether the functions are working properly together  [141]. For data analysts, the situation is somewhat different as a typical bioinformatics workflow might involve hundreds of lines of code, so conducting tests at each step in the process would be impractical. Literate programming also provides an opportunity to record sanity checks in an output report. In R, commands like dim(), length(), head(), str() are a simple way to show the features of a dataset to validate that data transformation steps are working as expected.
Acknowledgments We are grateful to individuals who provided feedback on an earlier draft of the concept including  Dr Aziz Khan  ( Stanford University ),  Dr Sriganesh Srihari  ( QIMR Berghofer Medical Research Institute ),  Dr Barbara Zdrazil  ( EMBL-EBI ) and  Dr Irene López Santiago  ( EMBL-EBI ). Special thanks to  Dr Altuna Akalin  ( Max Delbrück Center, Germany ) and  Dr Simon Tournier  ( Paris Cité University, France ) for advice on Guix. We appreciate advice on Python notebooks from  Samuel S. Minot  ( Fred Hutchinson Cancer Center ). We are grateful to  Dr Martin O'Hely  ( Deakin University ) for critical comments on the manuscript. This research was supported by use of the  Nectar Research Cloud , a collaborative Australian research platform supported by the  NCRIS- funded  Australian Research Data Commons (ARDC).
2. 3. 5. 235 Avoid commodity cloud storage as these are impermanent and susceptible to link decay [129]. Avoid large supplementary data files accompanying journal articles as these are less findable and accessible [130]. 4. Preferably archive and share raw data and use existing standards for the discipline. Use file formats that are machine-readable and compatible with many different types of software. Some examples include comma-and tabseparated values (CSV/TSV) formats, eXtensible Markup Language (XML), JavaScript Object Notation (JSON), Hierarchical Data Format version 5 (HDF5) and Apache Parquet.
However incorporating such tests at key points in a workflow such as during quality control of input data, after data cleaning, before statistical analysis and a summary of the findings. While if/else statements can be used for testing, it is recommended to use a dedicated testing package such as testthat for R [142] or pytest for Python [143]. In the example in Box 1, testthat raises an error to halt the script if the test fails, while the if/else statement requires an extra line to do this. Box 1: An example of a test in R using an if/else statement and with the testthat package. The test is checking that the iris dataset describes three species. data(iris) numspecies <-length(unique(iris$Species)) # ifelse test if ( numspecies == 3) { print("Iris data describes three species") } else { stop("Iris data doesn't describe three species") } ## [1] "Iris data describes three species" # testthat test library(testthat) data(iris) testthat::expect_equal(numspecies, 3).
Patient Cohort We established a local prospective cohort composed of 38 adult (≥18 years) IDHwt glioblastoma patients included at initial diagnosis between June 2016 and October 2017 at Timone Hospital (Marseille, France). For these patients, the plasma samples were collected before and 48 h after surgical resection. This cohort was completed by 7 patients hospitalized for the resection of a brain aneurysm without glioblastoma. The following data were recorded: age, gender, type of surgery, Karnofsky Performance Status (KPS), oncological treatment, clinical symptom, steroid dose, and magnetic resonance imaging (MRI) characteristics. For plasma collection, peripheral blood was drawn into an EDTA tube. Blood samples were centrifuged within 2 h at 3000× g for 10 min at room temperature, and plasma was stored at -80 °C.
EGFR Amplification Determination by Next Generation Sequencing The nucleic acids were extracted with the Maxwell RSC DNA FFPE kit (Promega, Madison, WI, USA) from formalin-fixed, paraffin-embedded (FFPE) tumor samples. For amplification screening, we used our custom panel Oncomine Solid Tumor and Oncomine Solid Tumor+ (OST/OST+), which includes EGFR. For all clinical samples, we performed sequencing with Ion Torrent S5XL (ThermoFisher Scientifics, Waltham, MA, USA) with a sensitivity of 5% and a minimum coverage of 500X. Then, data were analyzed through two complementary pipelines. The first pipeline was developed by ThermoFisher on the IonTorrent Suite + Ion Reporter. The second pipeline, which was developed in our laboratory, uses open-source software such as BWA-MEM for alignment, SAMtools for mpileup, VarScan2 as the variant caller and VEP Ensemble for annotations.
Sample Analysis by NanoDSF Plasma denaturation profiles were obtained using the Prometheus NT. Plex instrument (Nanotemper), as previously described  [12]. Briefly, plasma samples were loaded to 10 μL capillaries and scanned using Prometheus at 5% of laser power and a 1 °C/min heating rate to obtain PDP in a range of 15 to 95 °C. We carefully mixed preoperation and postoperation patients to avoid any batch effect. Raw data, namely fluorescence at 330 and 350 nm (F330 and F350), scattering at 350 nm (S350) as well as the ratio of fluorescence (F330/F350) were exported into datasets. The first derivatives of these 4 measures were added to the datasets in order to provide their dynamics.
Acknowledgments: We acknowledge the support and expertise of the  NeuroTimone Platforms (PFNT)  of the  INP , in particular the  Interactome Timone Platforms (PINT) , certified by  Aix Marseille University (AMIDEX) , the  CNRS  and  Inserm  and financed in part by the  fonds européen de développement regional (FEDER).
2019). The ASNR is a large repository of empirical contact networks that provides novel opportunities to test the utility of spectral values in predicting spread across a wide variety of, mostly animal, taxa across a spectrum of social systems --from eusocial ants (Arthropoda: Formicidae) to more solitary species such as the desert tortoise (Gopherus agassizii). Farmed domestic animals were not included in our analyses. We combined networks from this resource with other published networks, including badgers (Meles meles)  (Weber et al. 2013) , giraffes (Giraffa camelopardalis)  (VanderWaal et al. 2014)  and chimpanzees (Pan troglodytes)  (Rushmore et al. 2013)  to generate a dataset of over 600 unweighted networks from 51 species. We then simulated pathogen spread using a variety of SIR parameters and harnessed recent advances in multivariate interpretable machine learning models  (MrIML; (Fountain-Jones et al.
Diversity of network structures We identified substantial variation in network structure across animal taxa. The static unweighted animal social networks in our database ranged from nearly completely unconnected (Spectral radius λ 1 ~ 1, Fiedler value ~0, not included in our predictive models) to highly connected (Spectral radius λ 1 ~ 160, Fiedler value ~ 140, Fig. 1 ). Similarly, the networks ranged from homogeneous (i. e. , not modular, Q rel = 0, see the Text box for a definition) to highly modular and subdivided (Q rel > 0. 8,). Our principal component analysis (PCA) identified key axes of structural variation across empirical networks (Fig. 2 ). The first principal component (PC1) distinguished networks that had a large diameter and mean path length and were highly modular (negative values), from networks with a high mean degree and transitivity (positive values, Fig.
Simulations To simulate the spread of infection on each network we used our R package "EpicR" (Epidemics by computers in R; available on GitHub at https://github. com/mcharleston/epicr). The simulations use a standard discretisation of the SIR model, in which time proceeds in "ticks," for example representing days. Initially one individual was chosen at uniform random to be infected (I) and all others were susceptible (S). At each time step, one of two changes of state can happen to each individual (represented by a node), depending on its current state. An 'S' individual will become infected (I) with a probability (1 -(1 -β) k ), where k is the number of currently infected neighbours it has, or otherwise stay as S; an 'I' individual will recover (R) with probability γ or remain as I. Recovered (R) individuals stay as R.
24, 27  METHODOLOGY 2. 1. Automated Optimization of CG Lipid Models. The optimization protocol proposed in SwarmCG  25, 26  allows for the obtainment of CG FF parameters for lipid models by simultaneously exploiting experimental data (top-down: area per lipid and D HH thickness) and AA MD simulations (bottomup: bond and angle distributions), which, respectively, inform on the supra-molecular structure of the systems and on their submolecular dynamics (Figure  1a ,b, Sections S1 and S2, and Tables  S1  and S2 ). CG models are tested iteratively in a set of simulations of small patches of homogeneous lamellar bilayers used to measure the discrepancies observed between the simulated and reference data (128 lipids and 200 ns of equilibrium CG MD simulation, each�Section S3, Table  S3 ). The software allows for the modulation of selected parameters of the CG FF that are iteratively optimized within predefined boundaries using FST-PSO  28  (Fuzzy Self-Tuning Particle Swarm Optimization, one of the most efficient PSO variants to date  29  ) for the minimization of a loss function designed to improve FF accuracy.
Behavioural measurements in laboratory experiments Organism tracking is carried out in the ToxMate monitoring device using inbuilt Viewpoint videotracking software. ToxMate, designed for aquatic organism video-tracking, is used here for the individual measurement of invertebrate avoidance during exposure to micropollutants. The apparatus is made up of a compact cubic chamber unit, with three observation panels composed of four columns of four individual chambers allowing for monitoring in continuous water flow conditions (Fig. 1 ). Observation panels are connected to a water tank by hydraulic pumps, forming a closed 15 L water circuit for experiments. The flowrate is 1. 4 L/min in each of the three panels, meaning organisms are exposed to a flow rate of 0. 35 L/min in the four-column arrangement, tested to be quasi-laminar across the panel.
In the scope of this study, G. fossarum was used to measure behavioural response (avoidance) to chemical exposure (spiking). Each panel contains 16 isolated gammarids that are tracked individually by the software (Fig. 1 ), removing any immobile organisms (<5% across experiments). Infrared panels and cameras are used to enable total darkness. Output data files were calibrated to give the total displacement of each organism for 20 s time steps. To limit biological variability, homogenously sized males (<10 mm) are taken from pairs of gammarids in amplexus, after the second week of acclimation. The selected males are then left for a third week. This process controls biotic factors and ensures moult in the week prior to exposition  (Geffard et al. , 2010).
Potential for real-time micropollutant detection in WWTP effluents The last part of the study questioned the possible transposition of the laboratory results to real field conditions. Hence, Fig. 5  shows the G. fossarum reaction to copper spiking at 100 µgL -1 in two additional contrasting water types carried out during one laboratory experiment and one on-site WWTP deployment of the ToxMate apparatus. We tested avoidance response to copper spiking in water sampled from the natural habitat of test organisms, and in the wastewater effluent at the Saint-Fons WWTP. The industrial ToxMate unit installed at the Saint-Fons WWTP allowed for the analysis and characterisation of the effluent over two years of data collection, with the copper spike shown zoomed on a several-week extract of the monitoring period (Fig.
Acknowledgments This research received financial support from  Région Auvergne-Rhône-Alpes  (projet Pack Ambition Recherche 2020 -ToxPrints),  ANRT  (Cifre PhD Grant of GR), institut  Carnot Eau-Environnement ,  Agence de l'eau Rhône-Méditerranée-Corse , and  European Union  ( H2020-EIC-SMEInst-2018-2020  Grant# 881495 ). The authors thank  SAUR R&D , the  Greater Lyon Authority and Ecostation operating its WWTP of Saint-Fons  for the access to the WWTP monitored during the study. They acknowledge  B. Giroud  ( UMR5280   CNRS ISA-TRACES Villeurbanne-France ),  L. Dherret  and  A. Dabrin  ( INRAE RiverLy-LAMA Villeurbanne-France ) for chemical analyses.
Materials and Methods Clinical data from patients treated with either partial nephrectomy (PN) or radical nephrectomy (RN) for cT1/cT2a RCC from 2000 to 2019, included in the French multi-institutional kidney cancer database UroCCR, were retrospectively analysed. Seven machine-learning algorithms were applied to the cohort after a training/testing split to develop a predictive model for upstaging to pT3a. Survival curves for disease-free survival (DFS) and overall survival (OS) rates were compared between PN and RN after G-computation for pT3a tumours.
Some predictive factors of pT3a upstaging, such as age and tumour size, have been previously reported  [5, 6]  but so far there are no practical tools available to preoperatively assess individual patient risk and guide individual treatment. Artificial intelligence has opened up a whole new era in clinical decision-making  [7]. It has made it possible to process large amounts of medical data within limited time and with greater accuracy. With the capacity to automatically find the best action to achieve a given goal, machine-learning algorithms (MLAs) are powerful tools for statistical analyses  [8]. However, MLAs require large, multi-institutional databases to follow the consecutive stages of training, testing and validating before any findings can be extrapolated to a new population. Few studies so far have explored RCC from a computational viewpoint  [9] [10] [11].
Study Population We conducted a retrospective analysis of patients from the French multi-institutional kidney cancer database UroCCR. All data from patients were prospectively collected in the UroCCR database (NCT03293563). A signed consent was obtained from all the patients after clear information and an ethical board approbation was obtained for this study. The data collection and analysis were authorized by the National Information science and Liberties Commission (CNIL) under number DR-2013-206. The study cohort consisted of patients treated surgically for RCC from 2000 to 2019, either by laparoscopic (pure or robot-assisted) PN, open PN or laparoscopic radical nephrectomy (RN) in all UroCCR centres. All tumours were preoperatively staged as ≤cT2aN0M0 and postoperatively staged as ≤pT3aN0M0.
Variables Demographic and clinical variables were collected, including age at diagnosis, sex, American Society of Anesthesiologists (ASA) score, Eastern Cooperative Oncology Group performance status (ECOG PS) and presence of symptoms at diagnosis. Preoperative tumour variables included the RENAL nephrometry score and details of its items: tumour size, rim location, polar location, exophytic/endophytic nature and hilar location. Data from the pathological examination included: pathological tumour size, histological subtype (clearcell RCC, papillary RCC, chromophobe RCC and other types), tumour stage, Fuhrman grade, presence of sarcomatoid features, necrosis, microvascular invasion, positive surgical margin and type of pT3a invasion.
1; (iii) using a backward stepwise selection procedure based on maximum likelihood ratio test, with P value cutoff at 0. 1. The average treatment effects and the marginal survival curves were then estimated using the prediction of the counterfactual outcomes of each patient under each treatment type. To take into account pathological features that might not be designated as potential confounders, we subsequently performed a non-causal analysis with Cox regression multivariable analyses after adjusting for pre-and postoperative covariates. A sub-analysis was also ruled out to compare surgical approaches between open PN and laparoscopic PN (gathering both pure-laparoscopic PN and robot-assisted PN) in the upstaged cohort in terms of DFS and OS. Causal analyses were performed with R software environment for statistical computing (version 4.
Upstaging Predictive Model The study population was split into train and test cohorts. Sampling was stratified on centres with a 70:30 ratio based on the average pT3a rate. Centres were different from one cohort to the other (17 and eight centres, respectively), in order to perform an external validation. The Mann-Whitney U-test for continuous variables and the chi-squared test for categorical variables were used to measure the association between covariates and pT3a status in the whole dataset and according to the two cohorts. We tested several strategies of feature selection to specify which variables should be included in the statistical learning process to train the MLAs. Our decision to exclude each variable was based on several indicators: the estimated level of bivariate association between each feature and outcome, the rate of missing values, and the consistency of the feature distribution between train and test datasets.
Missing values of the selected variables were imputed with the K-nearest neighbours' algorithm using K = 10 neighbours. Seven supervised MLAs  [14]  were trained: Logistic regression, Binary Decision Tree, Random Forest, eXtreme Gradient Boosting tree (XGBoost), Gradient Boosting Machine (GBM), LightGBM and Support Vector Machine. Hyperparameters of each algorithm were optimized using a 10-fold crossvalidation approach on the training dataset. The predictive abilities of each algorithm were evaluated on both train and test datasets using precision/recall curves, which are preferable in imbalanced datasets  [15] , and receiver-operating characteristic (ROC) curves. The best area under the precision/recall curve (prAUC) was 1 and the baseline was the actual rate of pT3a in the cohort. The most effective algorithm was chosen to build the predictive model.
Upstaging Predictive Model The cohort was split into one training cohort (n = 2636 from 17 centres) and one test cohort (n = 1759 from eight other centres) with an equivalent pT3a rate (15% each). ECOG PS, polar and rim location were not significantly associated with pT3a status, whereas the association between exophytic nature and pT3a status was not   S2 ). These four features did not improve the predictive performances of the multivariable MLA and were excluded from the prediction model. Preoperative features independently associated with pT3a upstaging and included in the final UroCCR-15 predictive model are shown in Table  S3  with their corresponding odds ratios (ORs). Among the seven MLAs tested, logistic regression was the most effective, with a prAUC of 0. 41 on the test dataset and  an area under the ROC of 0.
In these models, T3a tumours are of intermediate or high risk of recurrence, require closer follow-up than T1-T2 tumours and are potential candidates for postoperative treatment. Adjuvant therapies for intermediate-and high-risk RCC are currently being assessed with one positive study (S-TRAC  [17] ) on DFS in high-risk RCC after nephrectomy with tyrosine-kinase inhibitors. More recently, the KEYNOTE-564 phase III study obtained positive results comparing a checkpoint inhibitor (pembrolizumab) vs placebo on DFS (pembrolizumab; HR 0. 68; 95% confidence interval [CI] 0. 53-0. 87; P = 0. 002)  [18]. Whereas, three other trials, two with an adjuvant setting (IMmotion-010  [19]  and CheckMate-914 [NCT03138512]) and one with a peri-operative setting (PROSPER [NCT03055013]), failed to show any improvement of DFS with immunotherapy.
As a collective database, data completion is left to the discretion of each centre, possibly resulting in evaluation or follow-up biases. It has been shown that tumour staging is subject to interobserver variability  [27]. Second, the retrospective design of our study has possibly led to selection and information biases. Some of these were prevented by the prospective collection of data but we must consider that the cohort gathers patients from a large period with a risk of changes in disease management or data acquisition. Third, the amount of missing data should be mentioned, even though it was managed by the machine-learning imputation strategy  [28]. Fourth, the median follow-up in our cohort was relatively short (21 months) and may thus have prevented us from observing most cases of relapse since mean delay of recurrence for RCC has been evaluated at 77.
5 months  [29]. Longer follow-up is warranted to accurately explore DFS. Moreover, the use of MLAs is new in the scope of renal tumours. More studies are needed to ascertain the interpretability and clinical utility of our prediction model; validation using external datasets in particular is necessary. Finally, we did not consider biological factors in our predictive model due to lack of data. For example, it has been recognized that systemic inflammatory response, especially neutrophils, have decisive roles in initiating cancer, promoting metastases and helping tumour growth via different cellular and biochemical pathways  [30]. Inflammatory biomarkers such as neutrophil-tolymphocyte ratio have become important prognostic markers in many types of cancer, including RCC. A recent work showed that inflammatory markers, including neutrophil-to-lymphocyte ratio, could predict pT3a upstaging and other adverse pathological features  [11].
Acknowledgements Additional statistical support was provided by (alphabetical order):  G. Etchepare  and  C. Lopez  of  Sophia Genetics, France. The authors wish to thank  Teresa Sawyers ,  British Medical Writer at the B. E. S. P. I. M. ( Laboratoire de Biostatistique, Epid emiologie clinique, Sant e Publique, Innovation et M ethodologie), Nîmes University Hospital, France , for her help in editing this text.