dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Users Within the marine climate research community, international workshops promote best practices: the JCOMM Workshop on Advances in Marine Climatology and the International Workshop on Advances in the Use of Historical Marine Climate Data. These workshops focus on data management aspects of marine climatology, the development and production of multidecadal to centennial climate products, and sharing improvements in bias adjustment and uncertainty estimation methodologies. Both series include dataset developers and users, but it would be beneficial and is recommended to expand the scope to include operational users of marine data, allowing connections to be made between the need to make observations to support immediate operational needs and also to foster understanding that reusing these data is essential to meet longerterm climate objectives.
Objectives: To investigate the agreement between two data reduction approaches for detecting sedentary breaks from uni-axial accelerometry data collected in human participants. Free-living, uni-axial accelerometer data (n = 318) were examined for sedentary breaks using two different methods (Healy-Matthews; MAH/UFFE). The data were cleaned and reduced using MAH/UFFE Analyzer software and custom Microsoft Excel macro's, such that the average daily sedentary break number were calculated for each data record, for both methods.
Introduction The seminal study of Healy et al. demonstrated the usefulness of objectively-measured indices of sedentary behaviour, namely sedentary time and specifically sedentary breaks (periods of physical activity punctuating sedentary time)  [1]. Subsequent to the Healy et al. study, further evidence has emerged supporting the use of objectively-measured sedentary time and sedentary breaks  [2] [3] [4]. More recently, in addition to sedentary time and breaks, the importance of sedentary bout durations has been highlighted  [5]. The accumulating evidence regarding the importance of sedentary behaviour  [6, 7]  has led to a proliferation of terminology, definitions, measurement techniques and methods  [3, [8] [9] [10]. To date, the extraction of these objectively-measured sedentary-related variables, has utilized software which requires some level of programming skills (R, SAS, Mat-Lab)  [1-3, 5, 9].
More recently, commercial proprietary software such as Actilife  [4]  and MeterPlus ™  [11]  provide researchers with the ability to extract these variables without the need for programming skills. However, this software is costly such that an additional barrier exists for researchers from low resource settings to implement these variables in their analyses. Initial software costs range from approximately USD 695 to USD 1695 for a single-user licence, and an additional USD 200 annual renewal per licence. Freeware software such as MAH/UFFE has been used extensively to clean and score accelerometry data whether describing physical activity (non-sedentary time) (≥ 100 counts) or sedentary time (< 100 counts). As of May 2019, approximately 329 items were retrieved from Google Scholar (excluding citations and patents) using the search term 'MAHUFFE' , and 79 items were retrieved from PubMed Central (4 papers reported the use of MAHUFFE for 2017-2019).
However, it must be stated that a sedentary break is in essence the initiation of a bout of physical activity. Since a cut-point or threshold is employed (≥ 100 counts)  [1] , it is thus possible to specify parameters within the MAH/ UFFE configuration such that sedentary breaks can be easily determined. Importantly, as far as the author is aware, no study has used MAH/UFFE to extract sedentary breaks. Consequently, this analysis evaluates the agreement in indentifying sedentary breaks using the accepted Healy-Matthews algorithm  [1]  and the MAH/ UFFE accelerometer data reduction software, using accelerometer data collected in rural and urban African participants during a physical activity questionnaire validation study  [12, 13]  and a free-living, cross-sectional survey  [14]. Previous analyses of this data did not include sedentary breaks as a sedentary index.
Data collection and initial data reduction The initial data reduction methodology is described in detail elsewhere  [14]. In short, participants were asked to wear uni-axial accelerometers for six to seven complete days. The CSA model 7164 (Rural and Urban sample) and MTI model AM-7164-2. 2 (DHDSS sample) are both products of Actigraph, LLC, Pensacola, FL, USA (formerly Computer Science Applications, Inc. Shalimar, FL and MTI Health Services, Fort Walton Beach, FL). The minute-by-minute data were downloaded from the accelerometers onto an IBM-compatible personal computer via an interface unit, for further analysis using specialized software (MAH/UFFE Analyzer version 1. 9. 0. 3; http:// www. mrc-epid. cam. ac. uk/physi cal-activ ity-downl oads/). Unlike an earlier analysis  [14] , the MAH/UFFE configuration file was modified such that sedentary breaks of ≥ 1 min could be detected and summarized (see Additional file 1).
In this case, the MAH/UFFE settings detect ≥ 1 min bouts of ≥ 100 acceleration counts. In other words, the number of sedentary breaks and total break time are determined. The resulting summary Microsoft Excel file was imported into a statistical package for further analysis. From the total number of sedentary breaks and total break time, a daily average was calculated for each participant by dividing the totals by the number of valid days.
The resulting summary Microsoft Excel file was imported into a statistical package for further analysis. From the total number of sedentary breaks and total break time, a daily average was calculated for each participant by dividing the totals by the number of valid days. The Microsoft Visual Basic macros used in this analysis can be obtained from the author by request. The SAS syntax for the Healy-Matthews algorithm  [1]  was obtained from the authors (Dr Genevieve N Healy, personal communication) and implemented within a Microsoft Excel spreadsheet (Fig. 1a, b ). Unlike the Healy-Matthews algorithm, the MAH/UFFE software does not require the deletion of excluded values (nonwear time, counts = 0 for 60 consecutive minutes) when detecting bouts, but rather codes the excluded values as "TRUE" (Fig.
In this example there are 11 min of sedentary behaviour (counts ≤ 99) distributed as 5 continuous sedentary bouts, and 5 corresponding sedentary breaks, within the first 26 min. Descriptive statistics comprised means and one standard deviation (SD). One-way Analysis of Variance was used to compare variables across groups (samples and algorithms). Post hoc multiple comparison analyses (Sidak's t-test) assessed group differences. Linear regression was used to examine the relationship between output variables from the Healy-Matthews and MAH/UFFE algorithms. Bland-Altman plots explored the agreement between the outputs of the Healy-Matthews and MAH/ UFFE algorithms. Data were analysed using appropriate statistical software (Stata/SE for Windows: Release 15. 1.
Results Considering the sedentary breaks per day and the minutes per break, across the three samples, and the Healy-Matthews and MAH/UFFE algorithms, there was no statistical or practical difference between the algorithms (Table  1 ). Moreover, there were no significant differences across the algorithms for the combined data (n = 318) for both average breaks per day or average total break time (p ≥ 0. 911); the values were virtually identical across algorithms (average total break time: 429 ± 90 min day -1 , average break number: 72 ± 14 to 73 ± 14 breaks day -1 ). Both the agreement analysis (Fig. 2a ) and linear regression (Fig. 2b ), demonstrated a slight bias toward detecting approximately 1 sedentary break per day more with the MAH/UFFE software. Only 2. 2% of the data points fell outside the 95% limits of agreement; the maximum difference was < 2.
Discussion This analysis is novel in that, as far as the author is aware, this is the first time that it has been demonstrated that the MAH/UFFE data reduction software is capable of identifying sedentary breaks in high agreement with the accepted Healy-Matthews algorithm. In addition, since MAH/UFFE uses Microsoft Excel in its underlying architecture, it is not surprising that the Microsoft Excel Array functions agreed well with the MAH/UFFE output. Second, this analysis provides novel sedentary data. As far as the author is aware, this is the first sedentary break data from a rural African setting. The number of breaks per day reported from more urbanized settings  [2] [3] [4] [5]  tends to be higher than the values reported for the two rural groups in this study, but similar to the urban sample.
There are numerous variations around those methods (for reviews, see  Kalnay, 2003; Evensen, 2009). Both types of algorithms are already commonplace in meteorology and oceanography, but have only been recently introduced in geomagnetism (for details, see  Fournier et al. , 2010; Gillet, 2019). In this article, we present a Python package called pygeodyn devoted to geomagnetic DA based on a statistical method, namely an augmented state Kalman Filter (see  Evensen, 2003). It uses a reduced numerical model of the core surface dynamics to alleviate the computation time inherent to DA algorithms. The reduced model is based on stochastic Auto-Regressive processes of order 1 (AR-1 processes). These are anchored to cross-covariances derived from three-dimensional numerical geodynamo simulations. We provide examples involving the coupled-earth  (Aubert et al.
, 2013)  and midpath  (Aubert et al. , 2017)  dynamos. The aim of pygeodyn is to provide the community with a tool that can easily be used or built upon. It is made to ease the updating of data and the integration of new numerical models, for instance to test them against geophysical data. This way, it can be compared with other existing DA algorithms (e. g. Bärenzung et al. , 2018; Sanchez et al. , 2019). The manuscript is organized as follows: Section §2 presents the principles under which the pygeodyn package was developed. Section §3 is a technical description of the version 1. 1. 0 of the package  (Huder et al. , 2019a)  that also gives the basic necessary scientific background (for details, see  Barrois et al. , 2017 Barrois et al. , , 2018;; Gillet et al. , 2019). In §4 we give examples of the visualization interface webgeodyn to which is coupled the core surface DA tool pygeodyn.
Efficient: -Direct integration of parallelisation is possible using Message Passing Interface (MPI) -Lengthy computations (such as Legendre polynomial evaluations) are performed by Fortran routines wrapped in Python. Easy to post-process: -Output files are generated in HDF5 binary file format that is highly versatile (close to NumPy syntax) and more time-and size-efficient. -The output format is directly supported by the visualization package webgeodyn for efficient exploration of the computed data (see Section §4).
User profiles The package was designed for several user types: Standard user: the user will use the supplied DA algorithms with the supplied data. The algorithms can be modified through the configuration files so this requires almost no programming skill. 1 https://hypothesis. works/ 3 Advanced user: the user will use the supplied DA algorithms but wants to run their own data. In this case, the user needs to follow the documentation to implement the reading methods of the data 2. This requires a few Python programming skills and a basic knowledge of object-type structures. Developer user: the user wants to design their own algorithm using the low-level functions implemented in the package. The how-to is also documented but it requires some experience in Python programming and object-type structures.
The documentation (available online 3  ) was written with these categories of users in mind. First, installation instructions and a brief scientific overview of the algorithm are provided. Then, it explains how launch computations with a supplied script that takes care of looping through DA steps, logging, parallelisation and saving files. For more advanced uses, the documentation includes tutorials on how to set up new data types as input and how to use low-level DA steps on CoreState objects. The developer documentation, gathering all the documentation of the functions/objects implemented in pygeodyn, is also available online. 3 pygeodyn 1. 1. 0 content 3. 1 Model state DA algorithms are to be supplied in the forms of subpackages for pygeodyn. The intention is to have interchangeable algorithms and be able to easily expand the existing algorithms.
Beyond the supplied data For advanced users, pygeodyn provides the possibility to define custom prior and observations types by supplying new data reading methods in the dedicated pygeodyn modules. Defining a custom prior type allows to use custom geodynamo simulation data to compute covariance matrices that will be used in the forecasts and analyses steps. Similarly, a new observation type can be supplied with custom observation data that will be used to estimate the core state in the analysis step. In other words, an advanced user can completely control the input data of the algorithm to test new magnetic observations and/or new numerical models, and derive new predictions from them.
Note that the computations remain tractable whatever the number of cores. A basic sequential computation (n = 1) for a re-analysis using 50 realizations over 100 yrs is performed in less than 6 hours, while using 32 cores will reduce it to half an hour. The source code of webgeodyn is hosted at its own Git repository 7. Being available on the Python package index, it can also be installed through the Python package installer pip. webgeodyn implements a web application with several modes of representation to explore, display and diagnose the products of the re-analyses  (Barrois et al. , 2018). It is deployed at http://geodyn. univ-grenoble-alpes. fr but can also be used locally on any pygeodyn data, once installed. We illustrate here several possibilities offered by the version 0. 6. 0 of this tool  (Huder et al.
Comparison with ground-based and virtual observatory data Computed data can be easily compared with the geomagnetic observations used for the analysis in the Observatory data tab. It allows to display the spatial components (radial, azimuthal and ortho-radial) of the magnetic field and its SV recorded by observatories. These can be either GO or VO data. Data can be displayed by clicking on a location on the globe and be compared with spectral model data predictions evaluated at the observatory location. Figure  4  shows an example of the SV at a ground-based site in South America. One can compare how predictions from a re-analysis, together with its associated uncertainties, follow geophysical data (black dots) -here the model by  Barrois et al. (2019) , which uses a diagonal AR-1 model from GO and VO series.
It can also be used to compare predictions from several magnetic field models -here COV-OBS. x1, which is constrained by magnetic data only up to 2014. 5. On top of the three examples illustrated above, the package webgeodyn also gives the possibility to display and export Lowes-Mauersberger spatial spectra, or cross-sections at the core surface as a function of time and longitude (respectively latitude) for a given latitude (respectively longitude).
Conclusions We presented the Python toolkit pygeodyn that allows to: calculate models of the flow at the core surface from SV Gauss coefficient data 270 13 calculate models of the flow and the magnetic field at the core surface from measurements of the magnetic field and its SV above the Earth's surface represent and analyse the results via the web interface webgeodyn. The underlying algorithm relies on AR-1 stochastic processes to advect the model in time. It is anchored to statistics (in space and optionally in time) from free runs of geodynamo models. It furthermore accounts for errors of representativeness due to the finite resolution of the magnetic and velocity fields. This Python tool has been designed with several purposes in mind, among which : test of the Earth-likeness of geodynamo models comparison with alternative geomagnetic DA algorithms production of magnetic models under some constraints from the core dynamics education of students on issues linked to core dynamics and geomagnetic inverse problem.
Acknowledgements. We thank  Julien Aubert  for supplying the geodynamo series used as priors, and  Christopher Finlay  for providing the go_vo data. We are grateful to  Nathanaël Schaeffer  for helpful comments on the manuscript. This work is partially supported by the  French Centre National d'Etudes Spatiales (CNES)  in the context of the Swarm mission of  ESA. We thank the national institutes that support ground magnetic observatories and INTERMAGNET for promoting high standards of practice and making the data available.
Code and data availability. The version 1. 1. 0 of pygeodyn is archived on Zenodo (Huder et al. , 2019a). Other versions (with or without data) are available on the Git repository located at https://gricad-gitlab. univ-grenoble-alpes. fr/Geodynamo/pygeodyn. The version 0. 6. 0 of webgeodyn used for the plots is archived at https://doi. org/10. 5281/zenodo. 2645025. This version (and others) can be installed from the Git repository (https://gricad-gitlab. univ-grenoble-alpes. fr/Geodynamo/webgeodyn) or as a Python package with pip. Data sets can be downloaded on http://geodyn. univ-grenoble-alpes. fr or on the git repository https://gricad-gitlab. univ-grenoble-alpes. fr/Geodynamo/pygeodyn_data Author contributions. The scientific development of the presented package was done by NG and LH. The technical development (including tests and packaging) was done by LH and FT.
Mapping, genotyping and SEX-DETector analysis The SEX-DETector analysis requires mapping the reads of the individuals onto a reference transcriptome and performing a SNP-calling to genotype all individuals for all expressed genes. We extracted the 30,074 transcripts from the annotation of the 2011 complete genome  (van Bakel et al. 2011). The initial mapping analyses were done using BWA, allowing for 5 mismatches per read (version 0. 7. 15-r1140, bwa aln -n 5 and bwa samse, see  (Li and Durbin 2010) ) and Bowtie2 (version 2. 1. 0, bowtie2-build and bowtie2 -x, see  (Langmead and Salzberg 2012) ). We used Samtools (version 1. 3. 1, samtools view -t output. fa -F 4 -h and samtools sort -m 2G, see  (Li et al. 2009 )) to convert files from the mapping. bam files into. sam files and to remove unmapped reads.
The genotyping was performed using reads2snp (version 2. 0. 64, reads2snp -aeb -min 3 -par 0, see  (Gayral et al. 2013 )), as recommended  (Muyle et al. 2016 ) (i. e. by accounting for allelic expression biases and without filtering for paralogous SNPs). Only SNPs supported by at least three reads were conserved for subsequent analysis (except in Table  S2 ). We ran SEX-DETector ( -system xy/zw/no_sex_chr -seq -detail -detail-sex-linked -L -SEMthr 0. 8, see  (Muyle et al. 2016 )) on genotyping data of the 12 individuals. SEX-DETector uses a maximum likelihood approach to estimate the parameters of its model, which include several genotyping error parameters. The posterior probability of being autosomal (P_A), XY (P_XY) or X-hemizygous (P_Xh) is then computed for each SNP and then computed for each transcript (combining the posterior probabilities of all SNPs, see  (Muyle et al.
2016)). A transcript was inferred as sex-linked if its posterior probability of being either XY or Xhemizygous was ≥ 0. 8 (i. e. P_XY + P_Xh ≥ 0. 8) and if at least one sex-linked SNP had no genotyping error; autosomal segregation was inferred similarly (P_A ≥ 0. 8 and at least one autosomal SNP without genotyping error; see  (Muyle et al. 2016) ). The remaining transcripts were considered undetermined and were not used for further analysis. Among the sex-linked transcripts, those with an expressed Y-allele were considered XY, the others X-hemizygous. SEX-DETector runs on the first mapping with BWA and Bowtie2 yielded high Y genotyping error (YGE) parameter values, which could be the result of mapping errors of Y-linked reads  (Muyle et al. 2016). The reference transcriptome used for mapping was derived from the genome of a female plant  (van Bakel et al.
ACKNOWLEDGMENTS We thank the  BGI  for free sequencing thanks to their call for RNA-seq for medicinal plants. We thank  Aline Muyle  for advice with SEX-DETector and discussions. We thank  Dr Tatyana Sukhorada , P. P. Lukyanenko Krasnodar Research and Development Institute of Agriculture  for providing seeds of C. sativa cultivar 'Zenitsa'. This project was supported through ANR  grant  ANR-14-CE19-0021-01  to G. A. B. M.
Datasets Metabolomics data were derived predominantly from untargeted metabolomics platforms (55%), with 27% derived from combining untargeted and targeted platforms and 18% from targeted only. The analysis goals were largely for biomarker discovery (91%) and/or to investigate disease etiology (82%), methodology development (31%), and other purposes (18%; e. g. , metabolome-wide association study) (Figure  1a ). Different study designs were used to generate metabolomics data, including: 17 cohorts, 15 nested case-control studies, 8 case-control studies, 8 case-cohorts, 5 randomized trials, and 4 cross-sectional analyses within prospective studies, with an average of 3302 participants (standard deviation 3972) (Figure  1b ). Reported outcomes being analyzed included cancer (39%), cardiovascular disease (CVD) (30%), diabetes (21%), and pregnancy outcomes (6%).
Almost half of the respondents (48%) assessed the performance of biomarker classification using area under the receiver operator characteristic curve (AUC) (n = 16), net reclassification improvement (n = 2), sensitivity/specificity/positive predictive value/ negative predictive value (n = 1) and PLS-DA (n = 1). Approximately 40% of respondents measured metabolite intercorrelations. The most common method for assessing metabolite intercorrelations was partial correlation (n = 11). Gaussian graphical modeling (GGM; n = 2) and WGCNA (n = 1) were also used for this purpose. A quarter of respondents conducted network analyses. WGCNA (n = 3) and unspecified methods incorporated into programs within the analytic resource MetaboAnalyst (n = 2) were the most common approaches followed by GGM with linkage to biological pathway, BayesNet, Gene-Set Enrichment Analysis (GSEA), over representation analysis (ORA), Metscape, and yED graphical networking software (n = 1 each).
Cross Validation and External Validation Seven respondents (21%) performed cross-validation (CV) analysis. Six respondents used k-fold CV and one reported simulation/permutation of data. The proportion of training data varied (60% to Approximately 40% of respondents measured metabolite intercorrelations. The most common method for assessing metabolite intercorrelations was partial correlation (n = 11). Gaussian graphical modeling (GGM; n = 2) and WGCNA (n = 1) were also used for this purpose. A quarter of respondents conducted network analyses. WGCNA (n = 3) and unspecified methods incorporated into programs within the analytic resource MetaboAnalyst (n = 2) were the most common approaches followed by GGM with linkage to biological pathway, BayesNet, Gene-Set Enrichment Analysis (GSEA), over representation analysis (ORA), Metscape, and yED graphical networking software (n = 1 each).
Meta-Data In total, 9 of 33 respondents (27%) appended metabolite meta-data for publication (Figure  6 ). Unique identifiers such as those from publicly available metabolomics databases such as Human Metabolome Database (HMDB) and PUBCHEM were most often appended to metabolomics study results (n = 5). The addition of pathway information and mass-to-charge ratio (m/z ratio) were also reported (n = 4) along with retention time (n = 3) and internal database compound tracking number in the platform's chemical library (COMP ID) (n = 1).