dataesr/scientific-paragraphs-categorization huggingface dataset:
Robustness of workflow execution Robustness of workflow execution measures the likelihood that the workflow execution fails due to errors in the components or in the interactions of the software architecture. Errors coming from the infrastructure (e. g. , unavailable data or terminated tasks) or workflows (e. g. , wrong user input or application errors) are not covered, since they do not stem directly from the software architecture. Robustness is measured here as a consequence of global complexity, since complex architectures tend to be more prone to failure, in particular in distributed systems where interactions between components involve wide-area network communications between machines that may be operated by different people. More precisely, robustness is determined by the number of software components and interactions represented in red in the architecture diagrams in Fig.
For example, interaction g (workflow language conversion) is clearly more complex than interaction d (service invocation). However, without entering into the details of a particular system, quantification of the robustness or the amount of development associated with each interaction and software component will hardly be precise. If a particular system is evaluated, the public spreadsheet can be used to adjust the weights given to specific components and interactions based on the technical context. The particular case of interaction g (workflow language conversion) requires particular attention when implementing a real system since this interaction may not be easily generalized to any workflow language. For instance, converting FSL, PSOM or Nipype pipelines to any other workflow language is problematic because these engines rely on general-purpose programming languages such as Bash, Octave/Matlab and Python, which are much richer than scientific workflow languages.
Irving Ludmer Family Foundation and the Ludmer Centre for Neuroinformatics and Mental Health. The integration between PSOM and CBRAIN was supported by a Brain Canada Platform Support Grant, as well as the Canadian Consortium on Neurodegeneration in Aging (CCNA), through a grant from the Canadian Institute of Health Research and funding from several partners. This work is in the scope of the LABEX PRIMES (ANR-11-LABX-0063) of Université de Lyon, within the program ''Investissements d'Avenir'' (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). This work also falls into the scope of the scientific topics of the French National Grid Institute (IdG). The VIP team thanks the site administrators of the European Grid Initiative and the GGUS support for their help related to the VIP platform.
The CBRAIN team is grateful for the computing cycles, storage, and support obtained from Compute Canada (https://computecanada. ca) and platform development program from CANARIE (http://www. canarie. ca/). We also acknowledge the Dutch national e-Infrastructure with the support of SURF Cooperative, the Dutch national program COMMIT/ and the High Performance Computing and Networking (HPCN) Fund of the University of Amsterdam for their support to the science gateway activities at the AMC. We are also grateful to the financial support provided by FP7 E-INFRASTRUCTURE program for financial support to SCI-BUS, SHIWA and ER-flow projects.
Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e. g. , in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces.
[9]  have identified good practices that would contribute to ensuring research quality. Among these rules, several apply to clinical bioinformatics: keeping track of how the results were produced, avoiding manual data manipulation, archiving the versions of all the programs, storing raw data and intermediate results, linking textual statements to raw results. We have developed our system with these recommendations in mind. A new technology has appeared in recent years that can be of great help in a clinical bioinformatics setting. Docker  [10]  is a Linux-based open-source technology, used on a daily basis by thousands if not millions of users in the world. Docker is a container technology that isolates tools from the operating system of the host server. Among other features, it allows users to deploy tools in a quick and reproducible manner.
Different containers can run different versions of the same tools or library without any mutual interference. We designed an approach based on Docker and Galaxy  [11] , the popular bioinformatics analysis support open-source software, that (1) is easy to manage and transparent to the user, (2) enables traceability of the result, (3) enables reproducible analysis, (4) can be deployed in a clinical setting with limited bioinformatics expertise and resources, (5) reduces the burden of manual analysis and data manipulation for molecular biologists, and (6) shortens the delay between the sample processing and the production of a clinically relevant result.
Workflow management systems The development of high-throughput methods in molecular biology has considerably increased the volume of molecular data produced daily by biologists. Many analytical scripts and software have been developed to assist biologists and clinicians in their tasks. Commercial and open-source solutions have emerged, allowing the user to combine analytical tools and build pipelines using graphical interfaces. In addition, workflow management systems (such as Taverna  [12] , Galaxy  [11] , SnakeMake  [13] , NextFlow  [14] ) also ensure the traceability and reproducibility of the analytical process. The efficient use of a workflow management system remains limited to trained bioinformaticians.
Analysis manager The AnalysisManager (AM) aims at simplifying the task of the biologists and reducing the burden of routine analysis. The AM enables the launch of common analytical processes in a single click (see Fig. 3 ). Like most of the components of our architecture, the AM is based on Docker. We developed a Django web application (in Python) using Bootstrap  [33]  framework. The AM handles 3 aspects of data treatment: (1) retrieving and backing-up the raw data from the sequencer using userdefined sample names (in addition to the sequencer barcodes); we leveraged the sequencer APIs to ensure compatibility across update releases; (2) running a user-selected Galaxy workflow, and adding the workflow to a Galaxy user's history; we used the BioBlend API (BioBlend Library, RRID:SCR 014557)  [34]  to connect our application to Galaxy; (3) finally, the AM backs up processed output files and sends an automated email to alert the user that their analysis is ready.
The AM uses a PostgresSQL database to store the user operations. Asynchronous Tasks jobs are performed with Celery and Redis  [35, 36]. ReGaTE  [37]  aims at automating the publication of Galaxy tools in the ELIXIR bio-tools registry by generating EDAM ontologies annotations  [29]. The EDAM ontology goal is to describe and characterize bioinformatics operations such as data types and format. We choose to Dockerize ReGaTE and to include it in our infrastructure. We leverage ReGaTE to produce metadata associated with every tool from the Galaxy instance, used by the reproducibility logs.
Bioinformatics from the perspective of medical informatics Bioinformatics analyses have become clinical tools comparable to any other laboratory tests. Consequently, in addition to preanalytical quality insurance, the NGS analytics pipelines must be standardized and respond to high-level quality requirements. As they are part of the patient care process, the results should be produced by explicitly traceable pipelines and made available without undue delay. Moreover, the overall technical aspects of the pipelines should be transparent for the user. Dock-erized tools and pipelines can be archived, shared, restored, and reused easily. In our case, we store the different versions of the tools and of reference datasets (reference genome, etc. ) to ensure the reproducibility of results within their original context in the future.
Tables 1-3 show traceability and reproducibility logs from the application. For each tool located on our Galaxy instance, we registered the tool name, the version, and the URI of the Edam ontology, which describe the input and output data of a specific tool. len"", u'option set": u'{" current case ": 1, "min bq": "10," "read depth": "242," "drop ambiguous": "False," "min het": "37," "block length": "13," "option sets": "advanced," "ignore chimeras": "False"}", u'dbkey": u'"?"'} u'[{"to posix lines": "Yes," "NAME": "None," "file data": "/tmp/nginx upload store/ 0000000001," "space to tab": null, "url paste": "", " index ": 0, "ftp files": "", "uuid": "None"}]", u'paramfile": u"'/export/Galaxycentral/database/files/ tmpkDBRmt"", u'file type": u"'auto"", u'files metadata": u'{"file type": "auto", " current case ": 39}", u'async datasets": u"'None"", u'dbkey": u"'?"'}.
Implementation at HEGP The molecular biologists use the Galaxy platform to analyze results from NGS, including the identification of variants in the tumor tissue, identification of variants in circulating tumor DNA  [48] , and copy number analysis  [47]. Our platform is deployed on a production server (running on Linux, with 20 cores, and 50 GB of RAM). In our experience, the minimal requirement for development purposes is a machine with 4 cores and 6 GB of RAM. Our AM instance is connected to an Ion-Proton R sequencer.
, living cells  [39] ). The fluorescent markers were added to the algal samples by adding a small volume of concentrated markers (erythrosine: 20 μL mL -1 culture at 1 g L -1 sea water filtered at 0. 2 µm; FDA: 3 μL mL -1 culture at 10 mg mL -1 acetone ; marker concentrations were optimized to ensure that viable and non-viable cells could be clearly identified; data not shown). For measurements with erythrosine, samples were exposed for 60 min to the marker in the dark. For measurements with FDA, samples were kept for 20 min at room temperature (20 °C) and in the ambient light (measured within 20-50 μmol m -2 s -1 ) to stimulate enzymatic cell activity of living cells. Cells fluorescence was measured 1 and 6 h after heat exposure by cytometry on three different fluorometry channels. Chlorophyll a fluorescence was measured by using the "FL3" channel (Excitation/Emission: 488/>670 nm) and enabled distinguishing algae from bacteria and/or other non-photosynthetic particles.
Model calibration and statistical analysis Each kinetic study (i. e. , the evolution of photosynthetic activity and viability during heating) was used to fit the Weibull model by least-square regression (Matlab function lsqcurvefit). Viability and photosynthetic activity were measured in duplicates for each time of exposure. Confidence intervals on measured algal viabilities and activities were based on a statistical analysis of the differences between duplicate values. The uncertainty on model parameters was then estimated through Monte Carlo simulations based on these confidence intervals, as detailed in Additional file 1: S3.
72 to ~5. 25 Å. We obtained neutron diffraction patterns of the sample ilm13 at the high-flux powder diffractometer D20 at the ILL. The HOPG(002) monochromator with a take-off angle of 42° and the detector, covering 153. 6° in 2q, provided us with neutrons of a wavelength of 2. 4 Å and a d-spacing range of 1. 3-13. 1 Å with a resolution on the order of Dd/d = 10 -2. While the resolution of D20 is inferior to that of OSIRIS, the high flux at D20 enabled us to measure a diffraction pattern in a few minutes. It was therefore possible to measure a temperature series of closely spaced data points (in steps of about 5 K) from room temperature to far above the Néel temperature of the sample (≈800 K) within one day.
Refinement of the atomic occupation factors produced unrealistically small values compared to the nominal compositions. Instead an initial refinement of room-temperature diffraction data was performed for each sample using the nominal composition. The compositions (and thus the atomic occupation factors for Fe and Ti) were then determined from the refined unit-cell volume using the formula  (Harrison et al. 2010 ) V = 1. 685 x 2 + 10. 823 x + 301. 740 (x < 0. 5) (1) where V is the volume of the hexagonal unit cell in Å 3. The occupancies were then fixed at the values obtained for the respective sample in each refinement. In the D20 experiment we measured a large number of diffraction patterns of ilm13 above T N. This high-temperature data was refined to the high-symmetry hexagonal unit cell and with zero magnetic moment.
aCknowLEdGmEnts The neutron experiments were performed at  ISIS, Oxfordshire, U. K. and Institut Max von Laue Paul Langevin, Grenoble, France. This work was supported by the  Danish Agency for Science, Technology and Innovation through Dan Scatt. The research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-.
36 While the OC potential measured by the potentiostat corresponds effectively to the back contact and not to the surface, it is assumed that any change in one would trigger a similar change in the other. Therefore, we consider the 'E vs. t' measurements (Fig. 1 , inset) as a technique to monitor in situ the effect of the PC treatment on the E F,n of BiVO 4. Our data show that under PC conditions facilitating the highest enhancement in the photocurrent (pH 10), the OC potential strongly shifts towards the water reduction potential (0 V RHE ) during the PC time (Fig. 1 , inset, black curve). Therefore, we can conclude that a successful PC treatment leads to a shift of the BiVO 4 E F,n towards the conduction band edge. The observed shift in the OC potential can be related to pinning of the electron Fermi level by the surface capacitive layer.
1 M K-P i ). BiVO samples were illuminated from the back-side using a Xe lamp equipped with an optical fiber. The XANES spectra, three scans per sample, were energycalibrated, averaged and further analysed using the FDMNES package program using multiple scattering theory based on the muffin-tin approximation on the potential shape. 64, 65 The muffin-tin radii were tuned to have a 10% overlap between the different spherical potentials. The Hedin-Lundqvist exchange potential was used. The XANES spectrum of a BiVO 4 crystal was calculated, considering in the calculation all the atoms surrounding the absorber V within a 7 Å radius sphere. The approximation of non-excited absorbing atoms was used, which better reproduces the experimental data. A similar calculation with the FEFF8 code gave similar results.
Acknowledgements We acknowledge the financial support of the  Foundation for Fundamental Research on Matter  ( FOM TNW 10. 327 ). This work has been done under the agenda of the  BioSolarCells Consortium  supported by the  Dutch Ministry of Economic Affairs, Agriculture and Innovation. We acknowledge The  Netherlands Organisation for Scientific Research (NWO)  for financial support of the measurements at  ESRF. We also thank financial support from the  University Jaume I  through the project  P1Á1B2011-50.
Acknowledgements LJM:  University of Florida (UF) ,  ACS Petroleum Research Fund  ( ACS-PRF 52704-DNI3 ),  National Science Foundation  ( CHE-1464876 ,  CHE-1048604 ),  J. Goodsell  and  Prof. A. Angerhofer  for help collecting EPR data. RGS:  Labex ARCANE  ( ANR-11-LABX-0003-01 ). KAA:  National Science Foundation  ( CHE-0821346 ) and  UF  for funding of the purchase of the X-ray equipment. KJA:  UF University Scholars Program.
Powder X-Ray diffraction analysis (XRD) on composite samples The mineralogical assemblage of each composite sample was assessed by XRD, using a Siemens D5000 diffractometer with cobalt X-ray tube (CoKα radiation). The powdered composite samples were scanned with steps of 0. 03°2θ and a counting time of 10s per step, in the 4-84°2θ angular range. Mineral identification and semi-quantitative determination of relative abundances of crystalline phases was performed using the software DIFFRAC plus EVA. XRD patterns of composite samples were subjected to quantitative mineralogical analysis using the SIROQUANT TM software. The quantification is based on the principles of Rietveld refinement  (Rietveld, 1969). In addition to crystalline phases, SIROQUANT TM was used to indirectly determine the amorphous phase content by spiking the samples with corundum internal standard.
EXAFS spectra were deglitched if necessary. Radial structure functions were obtained by Fourier transform of the EXAFS spectra, using a Kaiser-Bessel apodization function with a smoothing parameter of 3. 0. Data reduction was done using the Iffefit software package  (Ravel and Newville, 2005). The Fe-bearing minerals building up the composite samples were determined by linear combination fit of the EXAFS spectra. The minimum number of components needed to reproduce the EXAFS spectra was determined by principal components analysis  (Manceau et al. , 2002)  using the entire set of composite samples spectra. The minimum of the indicator function (0. 017) shows that two components are needed to reproduce the whole set of spectra (Table  2 ). Linear combinations were done between 3 and 11 Å -1 in the EXAFS region.
During the simulation, the following commonly used parameter was minimized: Where n is the number of data point, data i is the data point number i, and fit i is the fit value at point i. The sum of the wt. % of each compound obtained after least square fitting calculations was also used as a goodness-of-fit parameter, since it should reach values close to 100 wt. % in a reliable reconstitution. For Zn, XANES spectra were recorded in fluorescence mode (multi-element Canberra Ge detector), in the range 9511-10361 eV. Each sample was scanned 12 times and resulting spectra were averaged.
Evolution of the Zn status along the Technosol profile and assessment of Zn mobility The evolution of Zn speciation was first assessed using Zn-K edge XANES analysis of composite samples, as it provides an integrating view. Data are presented as a function of depth for two sets of replicate samples (Figure  8 ). The XANES spectra are similar for each replicate samples, indicating that Zn speciation is similar at any location at the same depth. Contrastingly, changes with depth are observed. The contribution at 9668. 5 eV is higher in the deepest samples (F1 and F3) whereas the peak at 9665. 5 eV is more pronounced in the surface samples (A, C and E). In Zn-ferrihydrite reference sample, the main contribution is located at 9665. 5 eV. According to  Cismasu et al. (2013)  and  Waychunas et al.
Acknowledgments The authors would like to acknowledge the  French Environmental Agency (Ademe; Hélène Roussel)  for their financial support ( REITERRE research program ) and advices through this work. S. G. acknowledges partial funding by the  ANR  ( NACRE-ANR-14-CE01-0006 ). The XAFS experiments were performed at the  Advanced Photon Source  (sector 20). Sector 20 facilities at the  Advanced Photon Source , and research at these facilities, are supported by the  US Department of Energy -Basic Energy Sciences , the  Canadian Light Source  and its funding partners, the  University of Washington , and the  Advanced Photon Source. Use of the Advanced Photon Source , an  Office of Science User Facility  operated for the  U. S. Department of Energy (DOE) Office of Science by Argonne National Laboratory , was supported by the  U.
ACKNOWLEDGEMENT The authors would like to thank a number of people that contributed significantly to the generation of the huge amount of data that served as a basis for the analysis provided in this paper:  Elvira Fehrenbacher ,  Michael Schachtner ,  Alexander Wekkeli  from  Fraunhofer ISE ,  Bernd Lehmann ,  Stephane Roure ,  Carl Hall ,  Bob Witteveen ,  Natascha Klein  from  ESA ,  Gerald Jüngst ,  Ainhoa Martínez de Olcoz ,  Alberto Vegas  at  INTA  and  Jean-Michel Siguier  from  ONERA. Furthermore ,  C. Baur  likes to thank his colleagues from  TEC-EPG :  Emilio Fernandez ,  Paul Hodgetts ,  Antonio Caon ,  José Ramón González  and  Gianfelice D'Accolti  for fruitful discussions on testing and interpretation of test results. Last but not least,  C. Baur  likes to thank the  JUICE  project team  Sam Verstaen ,  Robert Furnell  and  Fabio Panin  from  ESA  and  Thomas Schirmann  and  Didier Loche  from  Airbus DS Toulouse  for the good collaboration and support in the execution of the TDAs.
The estimated values are then optimised by minimising the error between simulated and experimentally measured fluid temperatures. Parameter estimation methods for evaluating thermal response tests are based on both analytical as well as numerical models. Several parameter estimation methods have been implemented in high-level programming languages and are available as standalone computer programs. These include, among others, Geothermal Properties Measurement (GPM)  [119] , Vertical Borehole Analysis and Parameter Estimation Program  [120] , and TRT Evaluation Program (TEP)  [121]. Software tools GPM and Vertical Borehole Analysis and Parameter Estimation Program are, respectively, based on one-dimensional finite-difference and two-dimensional finite-volume models, whereas software tool TEP is based on the analytical method proposed by Reference  [122].
Upscaling from Field to Regional Scale It is desirable to have a general knowledge of the spatial distribution of the geothermal characteristics of an area before determining in detail the design parameters for a shallow geothermal facility  [181]. Consequently, the question of upscaling local data, whether collected in situ through TRT or via laboratory studies on samples, to regional information has become important. The use of such datasets in a geographical information system (GIS) interactive online platform is becoming common. An example is the Thermomap project, which mapped the very shallow geothermal.
Upscaling from Field to Regional Scale It is desirable to have a general knowledge of the spatial distribution of the geothermal characteristics of an area before determining in detail the design parameters for a shallow geothermal facility  [181]. Consequently, the question of upscaling local data, whether collected in situ through TRT or via laboratory studies on samples, to regional information has become important. The use of such datasets in a geographical information system (GIS) interactive online platform is becoming common. An example is the Thermomap project, which mapped the very shallow geothermal potential (up to a depth of 10 m) across Europe  [182]. However, reliable large-scale geothermal mapping can only be based on a significant number of individual in situ or laboratory measurements of thermal conductivity  [183].