dataesr/scientific-paragraphs-categorization huggingface dataset:
org/), and reconstructed using Octave and PyHST (https://software. pan-data. eu/ software/74/pyhst2). Reconstructed tomograms of the same specimen were stitched using an implementation of NRStitcher (https://github. com/arttumiettinen/pi2). SBEM datasets were acquired on a variable pressure Sigma SEM 3View2 using SBEMimage software (https://github. com/SBEMimage) and on a high vacuum Merlin SEM 3View2 using ZEISS SmartSEM (version 5. 07) and Gatan DigitalMicrograph (version 3. 32. 2403. 0). All datasets were converted into a pyramidal file format using Voxelytics and explored and annotated through webKnossos (https://github. com/scalableminds/webknossos). Data analysis was performed using Python (version 3. 8. 3), Fiji (ImageJ 1. 53c), suite2p, MATLAB (2019a) and Amira (6. 7. 0). The toolbox for warping skeletons as described in "Methods" section "Warping" is based on BigWarp (https://github.
com/saalfeldlab/ bigwarp) and its MATLAB implementation derived from https://gitlab. mpcdf. mpg. de/ connectomics/L4dense. This implementation is accessible for public use alongside the warping landmarks, datasets and annotations described in this study. submission. This work was carried out with the support of Diamond Light Source, beamline I13-2 (proposal 20274), the TOMCAT beamline of the Swiss Light Source at the Paul Scherrer Institut (proposal 20190417) and the ID16A and ID19 beamlines of the European Synchrotron Radiation Facility (proposal ih-ls-3253). This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001153 to A. T. S. ; FC001999 to L. M. C. ), the UK Medical Research Council (FC001153 to A. T. S. , FC001999 to L. M.
Acknowledgements The authors are grateful to the advanced light microscopy, biological research, electron microscopy and scientific computing science technology platforms of the  Francis Crick Institute. We thank  Ede Rancz ,  John Bogovic ,  Kevin Briggman  and  Sina Tootoonian  for discussion,  Marta Majkut  and  Alexander Rack  for support and imaging experiments at ID19, and  Peter Cloetens  and  Pierre Paleo  for discussion and support with data analysis and synchrotron imaging. This research was funded in whole, or in part, by the  Wellcome Trust  ( FC001153  and  110174/Z/15/Z  to A. T. S. ;  FC001999  to L. M. C. ;  090843/F/09/ Z  to T. W. M. ). For the purpose of  Open Access , the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this.
Acknowledgments I would like to thank  Helen Solterer  and  Ellen Raimond  for sharing their insights on the " In Transit" project and installation , and  Taha Yasin Arslan ,  Vincent Joos ,  James Amelang ,  Saskia Ziolkowski , and the anonymous readers from  Manchester University Press  for their comments on earlier versions of this chapter. 27 This quote appears in many places without further bibliographic details. I have quoted it from the The Tribe of Dina:  A Jewish Women 's Anthology (Klepfisz, 1990:  160 ).
Acknowledgments I am indebted to  Mimmo Paladino ,  Arabella Pio , and  Jem Perucchini  for generously discussing their work with me and for providing image permissions for this chapter, as did photographer Franco Guardascione. I am also grateful to  Helen Solterer  and  Vincent Joos  for their editorial stewardship of this volume. Research and writing of this chapter was facilitated by the support of a scholar-in-residence position at  Magazzino Italian Art Foundation  ( Cold Spring, NY ), and by  Florida State University. My sincere thanks to  Nancy Olnick ,  Giorgio Spanu , and  Vittorio Calabrese at Magazzino. Additional thanks to the  Galerie Christian Stein ,  Silvia Valisa ,  Kristin Dowell ,  Victoria DeBlassie , and especially  Stephanie Hom  for conversations about this work. Notes Acknowledgments Valuable assistance was provided by  Lexi Bateman ,  Diali Cissokho & Kaira Ba ,  Katy Clune ,  Michael Faber ,  Philip Moss ,  Mark Olson ,  Gabriel Pelli ,  Victor Ribet ,  Austin Powers ,  Philipp Sadowski ,  Yuchen Zhao , and  Robert Zimmerman.
In this paper, we report on the first implementation of atmospheric chemistry and aerosol as part of the European Centre for Medium-Range Weather Forecasts (ECMWF) OpenIFS model. For this, modules and input data for model cycle CY43R3, which have been developed as part of the Copernicus Atmosphere Monitoring Service (CAMS), have been ported to OpenIFS: the modified CB05 tropospheric chemistry scheme, the AER bulk-bin tropospheric aerosol module, as well as the option to use BASCOE-based stratospheric ozone chemistry. We give an overview of the model, and describe the datasets used for emissions and dry deposition, which are similar to those used in the model configuration applied to create the CAMS Reanalysis. We evaluate two reference model configurations with and without the stratospheric chemistry extension against standard observational datasets for tropospheric ozone, surface carbon monoxide (CO), tropospheric nitrogen dioxide (NO2) and aerosol optical depth.
In the framework of the Copernicus Atmosphere Monitoring Service (CAMS, https://atmosphere. copernicus. eu/) the tropospheric chemistry module originating from TM5 based on a modified version of the Carbon Bond 2005 (CB05) scheme  (Yarwood et al. , 2005)  has been implemented into ECMWF's Integrated Forecasting System (IFS)  (Flemming et al. , 2015). This module has been extended and updated over time  (Huijnen et al. , 2017; Williams et al. , 2021). Alternative chemistry modules are also supported  (Huijnen et al. , 2017) , particularly a module for stratospheric chemistry based on BASCOE chemistry, which can be switched on in combination with the CB05-type chemistry  (Huijnen et al. , 2016). Likewise a module describing the fate of aerosol has been developed over time  (Morcrette et al. , 2009; Rémy et al.
, 2017 Rémy et al. , , 2021a)). The composition aspects have also been integrated in ECMWF's data assimilation system  (Inness et al. , 2015)  allowing the assimilation of satellite observations of trace gases and aerosol. This enables running the model in an operational context to provide global analyses, reanalyses and forecasts of atmospheric composition, and may in general be referred to as IFS-Composition (or IFS-CB05-AER, IFS-CB05-BASCOE), etc. , to specify a particular configuration). The OpenIFS model (https://confluence. ecmwf. int/display/OIFS) is a portable version of the ECMWF global IFS numerical weather prediction model that is available to research institutes and universities. It enables the scientific community to use selected cycles of the ECMWF operational IFS model on local computers at their own institutes.
OpenIFS has the same forecasting capability as IFS, it supports the same grid resolutions up to the operational resolution, and it can be used for ensemble forecasting  (Ollinaho et al. , 2021). The model codes differ however as the IFS code for data assimilation and observation processing have been removed from OpenIFS. OpenIFS is used in professional training and university teaching (e. g. ,  Szépszó et al. , 2019) , as well as a research tool to investigate atmospheric processes on a wide range of topics from NWP to climatologically relevant time scales (https://confluence. ecmwf. int/display/OIFS/OpenIFS+publications). In the realm of climate applications, OpenIFS will form the atmospheric component of the next generation (version 4) of the European community Earth system model. A recent, more detailed description of OpenIFS 40r1 is available in  Sparrow et al.
(2021). The current version, OpenIFS 43r3 (version 2), that is used in this work is based on IFS cycle CY43R3 which is described in detail both at the scientific  (ECMWF, 2017a (ECMWF, , 2017b) )  and technical levels  (ECMWF, 2017c (ECMWF, , 2017d)). Until now OpenIFS did not contain support for atmospheric composition modeling as has been developed in CAMS. Here we present a first version of OpenIFS that contains modules for atmospheric composition, based on the CB05, CB05-BASCOE and AER, which we refer to as OpenIFS/AC. This version relies on model code that has been developed as part of CAMS, although alternative schemes will be introduced in future versions of the model. In particular, a modal aerosol https://doi. org/10. 5194/gmd-2022-80 Preprint. Discussion started: 12 April 2022 c Author(s) 2022.
In the following subsections we provide an overview of the parameterizations and the corresponding input datasets, required to perform model simulations with OpenIFS/AC. Furthermore we provide a first, global evaluation of the model performance in terms of reactive trace gases and aerosol. The purpose of this evaluation is primarily to present the technical feasibility of the system, and provide a benchmark for future developments. The version of the model as presented here is made available to the community under the OpenIFS license. In Sect. 8 we provide information that describes the access to the model code, along with its elemental input data and pre-and post-processing scripts that are necessary to run the model as described here.
However, various constraints can be provided to the model as indicated in Table  1  through specifying the initial conditions and composition surface fluxes at the start of the run, and through different ways to constrain meteorology and surface fluxes throughout the run. This allows use of the model for different application types. Here we present the use of OpenIFS/AC primarily in the 'nudging' configuration. In this configuration selected meteorological quantities (e. g. winds and surface pressure) are nudged towards prescribed input data with specified relaxation time constants. In this configuration the emissions and deposition velocities are standard updated daily from a prepared dataset. This allows running the model efficiently for longer periods (typically a year), while keeping the meteorology and emissions constrained towards external datasets on a high temporal and spatial resolution.
Atmospheric chemistry and aerosol In this section we describe key aspects concerning the atmospheric chemistry and aerosol modules, and the main options available in this version of OpenIFS/AC. For the troposphere this concerns the modified CB05 mechanism (see Sect. 3. 1), while for the stratosphere one may choose between the linear ozone model or the BASCOE-based module for stratospheric ozone chemistry (Sect. 3. 2). A description of the AER bulk-bin tropospheric aerosol module is given in Sect. 3. 3. The specification of reference emissions, and dry and wet deposition is given in Sect. 3. 4 and 3. 5, respectively. Some comments on tracer advection aspects in OpenIFS are given in Sec 3. 6.
Tropospheric chemistry The modified CB05 module for tropospheric chemistry in our version of OpenIFS/AC is based on the CB05 scheme from  Yarwood et al. (2005). It uses a lumping approach for organic species depending on their functional groups. In its application in the TM5 and CAMS models, modifications and extensions have been developed which include an explicit treatment of C1 to C3 species  (Williams et al. , 2013) , as well as parameterization of SO2, dimethyl sulfide (DMS), methane sulfonic acid (MSA) and ammonia (NH3) chemistry (see also  Huijnen et al. , 2010   Metzger et al. , 2002). The modified band approach (MBA) is adopted for the online computation of photolysis rates in the troposphere  (Williams et al. , 2012)  and uses seven absorption bands across the spectral range 202-695 nm, accounting for cloud and aerosol optical properties.
Stratospheric chemistry Above the tropopause stratospheric ozone is either governed by a linear ozone chemistry scheme  (Cariolle and Teyssèdre, 2007) , or by explicit modeling of stratospheric composition. For this last option, the chemical scheme and the parameterisation for polar stratospheric clouds (PSCs) from the BASCOE system version "sb14a" have been implemented in the IFS in the framework of CAMS  (Huijnen et al. , 2016) , and here made available for OpenIFS/AC. Photolysis rates were computed offline by the TUV package  (Madronich and Flocke, 1999) , and are provided as lookup tables as a function of log-pressure altitude, ozone overhead column and solar zenith angle. Photolysis rates for reactions occurring in both the troposphere and stratosphere are merged at the interface in order to ensure a smooth transition between the two schemes.
Tropospheric aerosol The standard aerosol module as available in OpenIFS is based on the AER module as developed in CAMS  (Morcrette et al. , 2009 Rémy et al. , 2019) , as of the status of CY43R3. It consists of a bulk-bin scheme, originally derived from the LOA/LMDZ model  (Boucher et al. , 2002; Reddy et al. , 2005) , with aerosol species characteristics as shown in Table  2. The prognostic species are sea salt, desert dust, organic matter (OM), black carbon (BC), and sulfate. Optionally, the OpenIFS/AC aerosol module can be run in stand-alone mode (without interaction with chemistry), in combination with one gas-phase precursor: Both sea salt and desert dust are represented with three bins. As described in  Reddy et al. (2005) , sea salt emissions as well as sea salt particle radii are expressed at 80 % relative humidity (RH).
Input and output data, and its handling In this section we describe some specifics regarding input and output data for OpenIFS/AC, to the extent this is different compared to a standard OpenIFS configuration. Like the IFS, OpenIFS produces GRIB-fields containing 2D and 3D output fields with essentially user-specific quantities. Also the use of the XIOS infrastructure (Yepes-Arbós et al. , 2022) to allow NetCDF output is supported. Pre-and post-processing scripts have been developed to handle the model input and output, of which a basic selection is available in the distribution package.
Input data The following sets of atmospheric composition input data are required to run OpenIFS/AC. 1. Trace gas initial conditions (IC): 3D fields that are integral part of the ICMGG${expid}INIUA input datafile in GRIB format. In our experiments these fields are taken from existing IFS-Composition experiments as run in the CAMS configuration, but they may equally be provided through other sources. These tracer fields are defined in OpenIFS/AC by their namelist entries YCHEM_NL(1:NCHEM), where the tracer specifics (GRIB number, some physical parameters, together with transport, deposition and mass fixer settings) are configured. We note that for various tracers (particularly those required to run stratospheric chemistry) the official GRIB number in table  217  is not yet supported in this cycle of OpenIFS.
Here we use entries from the free, experimental GRIB table 216 instead, and rename the GRIB numbers when using a more recent CAMS experiment to provide input data. 2. Aerosol IC: similar to trace gas IC these are provided in ICMGG${expid}INIUA datafile, with tracer fields defined in the namelist entries YAERO_NL(1:NACTAERO). Table  files  define the list of chemistry and aerosol tracers, together with their main tracer specifics. A script is used to generate the corresponding namelist entries. 3. Trace gas and aerosol surface emissions and biomass burning injection heights. For trace gases all surface emissions are combined and provided as a single surface flux to the OpenIFS, with a GRIB number to identify the respective fields. For aerosol emissions these are split out between two components, which allows the introduction of a diurnal cycle to only a subset of the total emissions.
Furthermore aerosol biomass burning emissions are treated separately, also allowing the application of an injection height specifically for these emissions. 4. 3D emissions of aircraft NOx emissions are provided as a separate dataset. Currently they are provided as a monthly mean dataset available on various resolutions. 5. For trace gases to which dry deposition is applied, the deposition velocities are ingested in the model similar to the emissions treatment. If OpenIFS/AC is run in a nudging configuration then the daily (or monthly) varying surface boundary conditions (emissions and deposition) require to be provided as part of the nudging input dataset. A standard, CAMS-based input dataset of GRIB files, as used for the model runs presented here, is provided in the distribution package along with the model code, to provide a starting point for further modification.
Output data In analogy to OpenIFS, various types of output fields associated with composition-related quantities are supported by OpenIFS/AC. 1. GRIB data files containing model and/or interpolated pressure level output of user-specific tracer fields, with an output frequency of typically 3 hours. This also includes support for output of total aerosol optical depth at various wavelengths. 2. NetCDF data files containing model level output of user-specific tracer fields relying on XIOS infrastructure is available. Output settings can be configured by specific XML files. 3. Global mass and tendency diagnostics are available in the source code, allowing to analyze the evolution of tropospheric, and stratospheric burden, as well as the global, accumulated tendencies due to emission, dry and wet deposition, chemistry, as well as negative fixing and mass fixing due to non-conservation of the SL advection scheme.
Pre-, runtime-and post-processing scripts Whereas the (Fortran-based) model code for atmospheric composition modules could essentially be taken over from the developments done as part of the CAMS activities  (Flemming et al. , 2015; Huijnen et al. , 2016; Rémy et al. , 2019) , the shell scripts to be able to conveniently set up the experiments on any computing infrastructure, to run the model, and to digest model output for analysis and archiving had to be largely developed. Here we describe the main functionalities of these three aspects. Further details can be found in the README_atmo-composition text file provided in the code release. A config. h file is used to specify the key characteristics (configuration) of the model experiment, such as the period to cover, the mode (free run / nudging), and model settings.
Pre-processing scripts The pre-processing scripts prepare all the data that is needed for the requested model configuration, in particular the initial conditions and climatology. It consists of a main-level script named prep-ic-icmcl-compo. sh, which calls a selection of low-level scripts situated in the /scripts/ directory. If OpenIFS is run on ECMWF computers it may access MARS archives to find necessary datasets. The pre-processing script may be run in parallel to prepare data for multiple start dates of the full OpenIFS/AC experiment. It may be set to look for already prepared datafiles instead of creating them.
Runtime scripts The script used to actually execute an OpenIFS/AC experiment is called oifs-run. sh. This script reads the experiment setup from config. h, continues to set up a rundir (if not already done) and launches the OpenIFS executable to start or continue the experiment. A wrapper around this script is used to define the number of threads and processors, depending on user preferences. After the experiment is finished the output data is moved over to specific directories containing the log in ASCII format, and restart files and model output files in GRIB format.
Post-processing scripts Many downstream applications, including validation activities, require NetCDF-based model data for a selection of quantities. Also This output data needs to be archived for later use. Such NetCDF output can be configured using the XIOS infrastructure. Another option is to select and convert the standard GRIB output from OpenIFS into the requested format using the CDO package. For this purpose a basic post-processing script is provided along with the package. This script currently supports handling of GRIB output from OpenIFS/AC, and stores this data in a user-defined location, e. g. the ECMWF's File Storage system ECFS. However, for XIOS-based NetCDF output the command structure will be very similar. 6 Model evaluation.
Configurations Here we describe the model configurations as evaluated in subsequent sections. The current configurations are: 1. OpenIFS/AC-CB05-AER: standard configuration, using EBI solver in troposphere; OpenIFS/AC-CB05 in short. 2. OpenIFS/AC-CB05-BASCOE-AER: configuration including stratospheric chemistry, and using the KPP solver both in the troposphere and the stratosphere; OpenIFS/AC-CBA in short. Both OpenIFS/AC experiments have been performed on a TL 255 spectral horizontal resolution, with 91 model levels in the vertical. The OpenIFS/AC-CB05 configuration uses chemistry table-file 'tm5ver15', which contains 56 tracers. The OpenIFS/AC-CB05-BASCOE configuration uses chemistry table-file bascoetm5ver2d. txt, and uses 100 tracers. Together with 11 tracers for the AER aerosol module, the total number of tracers in these configurations add up to 67 and 111, respectively.
Conclusions We have presented a first version of OpenIFS containing atmospheric composition modules, based on OpenIFS 43r3. For this, the corresponding chemistry and aerosol modules as developed in CAMS have been introduced, along with standard input data such as emissions and dry deposition velocities. This release includes basic scripts to allow running the system in various modes, other than the nudging mode presented here. The composition model version provided is essentially the one as used for the CAMS Reanalysis, although we emphasise that OpenIFS does not include the data-assimilation capabilities used to produce reanalysis products in CAMS. Specifically, the model contains the modified CB05 tropospheric chemistry, which can optionally be coupled to the BASCOE-based module for stratospheric chemistry, and the AER bulk-bin aerosol module.
Along with a description of the model code and input data we have provided a basic evaluation of key quantities for a oneyear simulation for the year 2010, using relaxation of winds and surface pressure towards ERA-Interim data. An overall good performance was found against various dataset for key trace gases and aerosol, e. g. showing realistic spatial variations and/or seasonal cycle in tropospheric ozone, stratospheric composition, carbon monoxide, nitrogen dioxide and aerosol optical depth. This release makes for the first time the atmospheric composition modules integrated in ECMWF's Integrated Forecast System, as developed as part of CAMS, freely available for use by other research institutes. Also this allows the integration of alternative chemistry and aerosol schemes as the ones provided here, and allows the use of inline chemistry as part of Earth System Modeling activities as, for instance, in EC-Earth.
Nevertheless, many of the limitations in the model configuration as presented here are known, and have already been addressed in more recent cycles of the IFS, such as for instance documented in  Williams et al. (2021)  and  Rémy et al. (2021). With new cycles of OpenIFS coming up in future, we intend to make updated versions of the atmospheric composition module available as well. Likewise other, more up-to-date emission inventories can be expected to help address some of the biases seen here. The standard configuration of OpenIFS 43r3 does not contain most of the add-ons described here  (modules, scripts, input data)  which are required to model trace gases and aerosol. These can be obtained upon request as additional software to the standard model under the same licence agreement as OpenIFS itself.