dataesr/scientific-paragraphs-categorization huggingface dataset:
Raw images were initially thresholded against a user-defined intensity (selection via a graphical user interface) to provide a binary image, approximately highlighting bacteria against the background. High frequency noise was reduced through application of the built-in MATLAB medfilt2 median filter. Bacterial poles were identified as corresponding to the 2 pixels in a continuous region with the largest separation. Intensity along the selected bacteria was measured 11 times between the bacterial end-points, each at a uniformly spaced offset from the bacterial long axis. The mean profile was calculated and normalised to the range 0-100 for comparison between bacteria. A total of 30 cells were analysed this way per condition, in triplicate. All data were plotted in GraphPad software (Prism V).
Acknowledgements We thank  Nick Housden  ( University of Oxford ) for many helpful discussions,  David Sherratt  ( University of Oxford ) for E. coli strains expressing TolQ-mCherry and  FtsZ-GFP ,  Pete de Boer  ( Case Western University, USA ) for GFP-TolA expressing plasmid and  Dan Walker  ( University of Glasgow ) for anti-TolA antibody. We also thank  Laura S. Griffin  for preliminary FRAP microscopy of GFP-TolA in wild-type E. coli and the tolA deletion strain. Thanks also to the staff of  Micron , the  Oxford Advanced Bioimaging Unit , for help with microscopy experiments. This work was funded by the  Biotechnology and Biological Sciences Research Council UK  ( BB/G020671/2 ) to C. K. and C. G. B. and by a  European Research Council   Advanced grant  ( 742555 -OMPorg ) to C. K.
Long-wave upward radiation (LWU): The thermal emission from the planet's surface. Apart from the measurements mentioned above, many stations also offer additional information such as spectral ultraviolet irradiance, synoptic weather observations, radiosonde data (vertical profiles of temperature, humidity, wind and ozone) and data on the ozone column thickness. An up-todate overview of all available measurements can be accessed here: https://dataportals. pangaea. de/bsrn/ 3 ; an explanation of the content of each logical record (LR) can be found in Table 1. These additional measurements are not required for BSRN stations (though they are recommended) and in this paper we focus on the core measurements of BSRN, the downward-and upward-directed long-and shortwave radiation fluxes from 1992 to 2017.
Quality control The methods for the initial stage of quality control are left up to the individual station scientists (some scientists are responsible for more than one station) and thus are not implemented uniformly across BSRN stations. However, BSRN does maintain recommendations  (Long and Dutton, 2002; Long and Shi, 2008)  as well as software tools  (Schmithüsen et al. , 2012). After this initial quality control, the data generated at BSRN stations are submitted in monthly granularity in the station-to-archive file format by the station scientist to the WRMC data curator. From 1992 to 2007, when the archive was maintained by the ETH in Zurich, the data were checked for physically possible limits within the WRMC and suspicious values were flagged, but not removed. Since being operated at the AWI, additional procedures have been enacted for data submitted to the WRMC.
The data are now not only quality checked (see below), but suspicious data are reported back to the station scientist with an appeal to remove them and resubmit the file. The files that were archived using the old system (pre-2007) were adopted by AWI without further checks and without quality flags. However, many of the old files have since been exchanged with updated versions. Old files can be distinguished from new ones quite easily: if the data are from pre-2007 and the version number is 1 in the station to archive file (line 1, last number) or if it has 2007 as publication year in PANGAEA, it is an old file. Details on the quality checks performed at the AWI are given below. Station scientists and users have the option of processing BSRN data using software developed for the BSRN community and WRMC called the "BSRN Toolbox"  (Schmithüsen et al.
, 2012). The software provides a format check for the station-to-archive files (and for PANGAEA download files, see below) and can perform quality checks of the data as outlined in the BSRN Global Network recommended QC tests, V2. 0  (Long and Dutton, 2002). The types of tests include those that identify data outside physically possible limits, as well as extremely rare limits. Additionally, a set of comparison metrics are available that flag data as suspect based on empirically established relationships relative BSRN stations and the years where short-and long-wave radiation data are available for each station. Short name 1992 Short name 1993 Short name 1994 Short name 1995 Short name 1996 Short name 1997 Short name 1998 Short name 1999 Short name 2000 Short name 2001 Short name 2002 Short name 2003 Short name 2004 Short name 2005 Short name 2006 Short name 2007 Short name 2008 Short name 2009 Short name 2010 Short name 2011 Short name 2012 Short name 2013 Short name 2014 Short name 2015 Short name 2016   to other variables (comparisons check, e.
Station 2017 Alert ALE 1 X X X X X X X X X X X Alice Springs ASP X X X X X X X X X X X X X X X X X X X X X X Barrow BAR 1 X X X X Since the BSRN Toolbox is subject to the GNU General Public License, it is freely available to everyone. With the help of the toolbox, BSRN station scientists as well as data users can easily download and convert station-to-archive files to user-friendly formats by clicking the option "Data", which splits station-to-archive files up into logical records containing parameter names and units. In addition, quality checks can be applied on BSRN files (downloaded from both ftp and PANGAEA) using the option "Quality check". All data can be visualized with the free software PanPlot2  (Sieger and Grobe, 2013). Some quality issues cannot be easily solved. One prominent example is the case of zero-offsets: Thermophile radiometers are known to suffer a negative bias due to infrared loss to the sky  (Dutton et al.
, 2001). Consequently, though physically impossible, negative radiation fluxes and values are often present in nighttime measurements. To reduce these so-called IR loss offsets, BSRN sensors are ventilated  (Michalsky et al. , 2017)  and/or heated artificially. However, ventilation sometimes fails or is insufficient to compensate for large temperature variations. Depending on the location and instrument used, nighttime values more negative than -4 W m -2 can just be removed, or in other cases corrections can be made using algorithms that make use of colocated pyrgeometer data that are part of the BSRN instrument suite  (Dutton et al. , 2001; Long et al. , 2001; Younkin and Long, 2004). However, if the negative bias also occursbut is not evident -during the day, the BSRN recommends keeping the nighttime negatives in the archived files.
Each individual BSRN customer can then decide how to treat the data depending on the scope and aim of their work. Ventilation is also used to help prevent the build-up of ice (e. g. , rime or frost) on the sensor windows, though experience within the BSRN community indicates that ice remains an important vulnerability to measurement quality and data retention rates for stations in cold climates  (BSRN, 2012). BSRN is working to formulate recommendations for the network. A more thorough description of BSRN methods and quality control issues can be found in  Lanconelli et al. (2011)  and  Matsui et al. (2012) , where issues especially related to polar BSRN sites are elaborated on. Other interesting papers on the quality and possible biases of radiation measurements were published by  Vuilleumier et al.
Data availability In https://doi. org/10. 1594/PANGAEA. 880000 the downward and upward short-and long-wave data of all BSRN stations for the years 1992 to 2017 are archived. More than 10 300 monthly files (> 850 station years) of high-quality radiation data are thus available free of charge. The files are organized in zip files with one file containing all monthly data files for 1 year of downward and (if available) upward radiation data (LR0100 + 0300 or LR0100 and LR3010; see Table  1 ) from one station. The format of the files is tab delimited txt, and the structure is the same as if downloaded directly from PANGAEA (see Fig. 4 ). At the top of the files the metaheader gives details on the citation of the dataset; a link to the respective station-to-archive file on the ftp server; and the location of the station, parameters, units, PI, methods etc.
, followed by the actual data table with abbreviated parameter names and units. To be able to download the files, users should contact the WRMC staff (http://bsrn. awi. de/contact-persons/ 6  ) to request a BSRN account. By accepting this account, the data user agrees to comply with the guidelines defined at http: //bsrn. awi. de/data/conditions-of-data-release/ 7. The reason for requiring that users register with BSRN is that the organization is a nonprofit voluntary network and it is important that station managers who commit resources to providing the data are able to receive appropriate acknowledgement, the correct citation of their data files in publications and are able to monitor the level of use of the station data. Therefore, the acknowledgement of the data release guidelines is an essential step in the data download process.
There are plans to simplify the procedure by allowing the acceptance of the data release guidelines directly during the download of the data, but this feature is not operative yet. From 1 January to 31 December 2017, 140 requests from 35 countries were received and answered promptly (within 1 day). On average 800 visitors per month visit the BSRN website. Up to now, Web of Science reports a total of 100 publications related to BSRN, which have been cited more than 2500 times (accessed on 10 January 2018). Several hundred to several thousand BSRN files are downloaded each month. Unfortunately, to the best knowledge of the authors there is no data citation report yet available to check for BSRN dataset citations.
Some remarks for the best usage of BSRN data The zip files which are publicly available at https://doi. org/10. 1594/PANGAEA. 880000 contain all downward and (where available) upward radiation fluxes of one station and 1 year in monthly granularity. The ". tab" files can be opened in any text editor or spreadsheet program (e. g. , Excel). They can be visualized individually (in the provided monthly granularity), for example, in PanPlot2 (see Sect. 3. 2) by dragging the file onto the program icon. To obtain annual files one can, for example, drag all 12 months on the BSRN Toolbox and go to "Tools" ⇒ "Concatenate files". One can then insert how many lines the program should skip (metadata header) and press OK. Be aware that the resulting annual file will be quite large. PANGAEA itself offers comfortable ways to extract long periods of individual variables via the so-called data warehouse (http://bsrn.
awi. de/data/ data-retrieval-via-pangaea/data-warehouse/ 8 ). Go to https://dataportals. pangaea. de/bsrn/ 9 , choose the station, logical record and time period and click on the respective value or "X" (for all datasets from that year and station), which will then lead to the respective search page on www. pangaea. de. Before the user can retrieve the selected files he/she has to log in at the very top right corner of this page (log in can be obtained from the WRMC director; 8 last access: 28 July 2018 9 last access: 28 July 2018 see http://bsrn. awi. de/contact-persons/ 10  ). After logging in, a green button labeled "Data Warehouse" appears on the top right corner of the page. A click on this button leads into the data warehouse, where parameter(s) can be chosen individually. Arithmetic averages can be calculated via "Method" but the results should be considered with care or should best not be used at all in cases where significant data gaps exist.
In these cases the user should refer to more sophisticated averaging procedures as published in  Roesch et al. (2011)  or using a macro excel spreadsheet for hourly averages provided by  Schild (2016). After clicking on "Start Data Warehouse Query" a window will pop up to download the output file. Depending on the amount of years, stations or parameters chosen the files can get quite large (easily > 100 MB). A list of all radiation instruments used in BSRN is available at https://www. pangaea. de/ddi?request=bsrn/ BSRNMethods&format=html&title=BSRN+Methods 11. To get all data from one instrument just copy-paste the instrument specifications into https://www. pangaea. de/ and press search.
Summary and outlook In the 25 years of its existence, the Baseline Surface Radiation Network (BSRN) and its central archive have become an important component of global climate monitoring and climate research. Beginning as a contribution to the Global Energy and Water Cycle Experiment (GEWEX), the BSRN was later made part of the Global Climate Observing System (GCOS). Also, a formal cooperation agreement was established with the Network for the Detection of Atmospheric Composition Change (NDACC). Validation of models  (Wild, 2008; Müller Schmied et al. , 2016)  and satellite-based estimates of surface radiation  (Pinker et al. , 2005)  remain the focus of users. However, the data have also been used for trend analyses  (Long et al. , 2009)  and aerosol studies  (Xia et al. , 2007; Kim and Ramanathan, 2008).
Over the past decade, BSRN data have increasingly been used by customers in the field of solar energy, who especially welcome the high temporal resolution of the data (e. g. ,  Moshövel et al. , 2015; Hofmann and Seckmeyer, 2017). Probably the first study that made full use of the BSRN network to estimate trends and constituted the solar brightening was  Wild et al. (2005). More reviewed scientific papers referring to BSRN can be found at http://bsrn. awi. de/other/publications/ 12. Neither the BSRN stations nor the archive receive direct financial grants for their involvement in the BSRN. Despite this fact it has succeeded in developing a network that is setting standards for ground-level radiation measurements through volunteer efforts by a global community of scientists and technicians who have agreed to define and maintain a common set of standards.
A total of 12 of the 59 stations have unfortunately closed, are no longer maintained, are inactive or seek funding. Thanks to the BSRN, the data collected by these stations remain available. However, BSRN is now in the process of adding nine new stations into the network (four from India, two from Taiwan, one from Russia, one from the Azores and one from Australia). One of the challenges the BSRN faces will therefore be the tremendous effort to continue its collection of high-quality, high-temporal-resolution measurements with as many representative stations as possible. As a fundamental environmental variable, the importance of permanently archived, freely accessible and highquality radiation data cannot be valued highly enough.
Langlo et al. (2013)for a detailed explanation of the content of each logical record and Table2in the present paper for an overview of available data. All stationto-archive files are stored on, and are accessible via, a public ftp server. To increase usability and visibility of the files, the WRMC at the AWI also publishes the data within the Data Publisher for Earth & Environmental Science, PAN- GAEA (http://bsrn. awi. de/data/data-retrieval-via-pangaea/ 4 ) in a much more straightforward and intuitive format. BSRN datasets in PANGAEA contain a metadata header followed by the data table with parameter names and units; see, for ex- The actual data within station-toarchive files are divided into so-called logical records, which do not contain table headers (no parameter names and units); see Königample, https://doi.
Computational setup In this work, we used a homemade high-order FE code  4  , which takes advantage of an efficient assembly technique, as described in  [28]. Indeed, quite often with high-order techniques, the computational bottleneck is found to be the assembly of the discrete operator itself. It is worth mentioning that the discrete high-order FE spaces used are those proposed in  [22]. Regarding the eigenvalue solver, the developed tool relies on the SLEPc library 5   [26, 27]. When an LU decomposition has to be carried out (see section 3. 5), the MUMPS 6   [29, 30]  library is called, and is configured for parallel analysis with the ParMETIS 7   [31]  reordering. Among the eigenvalues in the 51. 1 GHz region of the spectrum, we expect to find one with a high ratio between the real and imaginary parts.
Geometry Let us now discuss the geometry considered for the simulations. In order to reduce the number of unknowns, only one quarter of a single mirror is modeled, as shown in figure  3. Furthermore, the air surrounding the quarter mirror is represented by a rectangular parallelepiped, as shown in figure  4 (a). As discussed in section 2, a PML is added to model the surrounding space, as depicted in figure 4(b). As a first guess, the PML thickness is taken as1λ (where l  5. 9 mm is the wavelength at 51. 1 GHz), and its distance from the mirror is also set to 1λ. More details on the PML can be found in sections 2 and 3. 4. The final geometry is depicted in figure 4(b), and was generated by the OpenCASCADE 8 engine. This library was driven using the python 9 interface provided by Gmsh 10 [33].
Eigenvalue problem Our objective is to find the eigenvalues of the photon cavity. In other words, we need to find every possible value of the angular frequency ω, such that holds, where c 0 is the speed of light in vacuum, e the electric field, and where Ω is the computational domain (i. e. the quarter mirror surrounded by the PML). To clarify the presentation, the treatment of the boundary conditions is discussed in the next subsection. Available at: http://opencascade. com 9 Available at: http://python. org 10 Gmsh is a three-dimensional FE mesh generator with built-in pre-and post-processing facilities; available at: http://gmsh. info From a FE point of view, this eigenvalue problem writes  [35] : ò ò m e w W - W = " Î W W - W ⎧ ⎨ ⎪ ⎩ ⎪ ⎛ ⎝ ⎜ ⎞ ⎠ ⎟ ( )•( ) • ( ) e e e e e c V curl curl curl d d 0 , , boundary conditions of section 3.
Acknowledgments This work has been supported by the  Belgian Science Policy Office (BELSPO)  under Grant  IAP P7/02  and the  ANR   RESONANCE  project, grant  ANR-16-CE24-0013  of the  French Agence Nationale de la Recherche. The computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant  2. 5020. 11. Furthermore, this work has benefited from computational resources made available on the Tier-1 supercomputer of the  Fédération Wallonie-Bruxelles , infrastructure funded by the  Walloon Region  under the grant agreement  1117545. The work of  N Marsic  has been supported by the  Belgian Research Training Fund for Industry and Agriculture (FRIA). Finally, the authors would like to express their gratitude to the referees for their constructive comments, and to  Michel Brune  ( Laboratoire Kastler Brossel ,  CNRS ,  ENS ,  UPMC ,  Collège de France ) for the picture of the mirrors, shown in figure 1, and his valuable feedback on the manuscript.
In addition to flexibility with regards to how much effort goes into populating a framework with evidence, it is flexible in terms of the relative importance attached to the included criteria, since this depends on the nature of the decision being made. Moreover, the EtD framework can be adapted by organisations, for example, by modifying the key criteria that are included  [23]. The interactive EtD tool  [54]  and GRADE's official software GRADEpro GDT  [55]  include 16 templates for different types of recommendations and decisions for either two or multiple options. The templates include clinical recommendations from different perspectives, coverage decisions, recommendations about tests, and the four templates for health systems and public health recommendations and decisions shown in Box 1.
Acknowledgements Members of the GRADE Working Group who contributed to writing this paper include  Jenny Moberg ,  Andrew Oxman ,  Pablo Alonso Coello ,  Holger Schünemann ,  Gordon Guyatt ,  Sarah Rosenbaum ,  Angela Morelli ,  Elie Akl ,  Claire Glenton ,  Metin Gulmezoglu ,  Signe Flottorp ,  Simon Lewin ,  Reem A. Mustafa ,  Gabriel Rada ,  Jasvinder Singh ,  Erik von Elm ,  Josh Vogel  and  Joseph Watine. We would like to acknowledge  Tess Lawrie  and  Ӧzge Tuncalp  for their comments, particularly on the EtD framework;  Philippe Duclos ,  Daria O'Reilly ,  Melanie Marti ,  Jean-Eric Tarride  and  Shaun Treweek  for their comments and suggestions; and  Andrea Cervera Alepuz  for her help with submission of the manuscript.
CryoEM sample preparation and data acquisition Gel filtration fractions of Rea1 and Rea1_DAAA2L-H2a were diluted to 1 mg/ml using buffer B (without glycerol) and ADP or AMPPNP were added to a final concentration of 3 mM. 3 ml of sample were applied to glow-discharged holey carbon grids (Quantifoil Cu R2/2, 300-square-mesh) that were subsequently blotted for 7-8 s and flash frozen in liquid ethane using a manual plunger. All cry-oEM grid preparations were carried out at 4 ˚C. All data were collected on an FEI Titan Krios equipped with a Cs corrector and a Gatan K2-Summit detector (300 kV, 35-38 frames, 7-8 s exposure, 1. 09 A ˚/pixel,~45-50 e "/A ˚2) using a slit width of 20 eV on a GIF-quantum energy filter (Gatan). All images were recorded in super-resolution counting mode using the automated data collection software Serial EM  (Mastronarde, 2005)  with a defocus range of 1.
Image processing Motion correction and dose-weighting were performed using MotionCor2  (Zheng et al. , 2017). CTF parameters were estimated using Gctf  (Zhang, 2016). Subsequent processing was done using Relion-2. 0 (Kimanius et al. , 2016)  unless stated otherwise. Micrographs were first manually examined to remove images with significant uncorrected drift, large amount of contamination, a large astigmatism, extreme defocus values (<1. 2 mm or >5 mm), or abnormal Fourier patterns. Micrograph quality evaluation in the case of the Rea1_DAAA2L-H2a AMPPNP phase plate data set was done according to von  Loeffelholz et al. (2018). For the Rea1 ADP dataset a small set of particles was manually picked and subjected to reference-free 2D classification. A selection of 2D class averages representing different views of Rea1 was selected, centred using the Relion shift_com function, and used as references for autopicking by Gautomatch (K.
Negative stain sample preparation and data acquisition Rea1 or the Rea1_DAAA2L-H2a deletion mutant in buffer B without glycerol were diluted to a final concentration of 45 nM. AMPPNP, ATP, or ADP were added to reach a final concentration of 3 mM. Negative-stain electron microscopy was performed on plasma-cleaned carbon film on 400-squaremesh copper grids (Electron Microscopy Sciences). 3 ml sample was applied to the grids that were subsequently stained with 2% (w/v) uranyl acetate. Data collection was done on an FEI tecnai G2 operated at 200kV and equipped with a Gatan Ultrascan 2K*2K CCD camera. Data were collected at ~1 mm underfocus, with a pixel size of 3. 629 A ˚and an estimated dose of 25 electrons / A ˚2 during 1 s exposures. SerialEM was used for semiautomatic data acquisition. Around 8000 particles per data set were manually picked and processed with Relion using standard procedures.