dataesr/scientific-paragraphs-categorization huggingface dataset:
The verification of the model was carried out by comparison of the numerical outputs with solubility data recently published by  Gaboreau et al. (2020)  and archived in the datasets selected by these authors from previous literature. ClayTherm estimations are in good agreement with data from the literature, calorimetry data included, except for chlorite, which was not found to reach equilibrium in solubility experiments. Some discrepancies between estimated and measured data were observed for vermiculite, possibly because of the presence of organic carbon in the sample measured by  Gailhanou et al. (2013). Directly derived from ClayTherm, the second estimation tool, ISTherm, is specifically designed to investigate the thermodynamic properties of interstratified illite/smectite minerals. In particular, it allows for the calculation of the thermodynamic properties of a series of illite/smectite (I/S) minerals from the composition of the smectitic and illitic end-members, and from a single I/S composition.
ACKNOWLEDGEMENTS Financial support from the  Thermochimie projects TemparSiamK  (n°  20071653 CGK ) and  Domhyne  ( 20081010 COX ) and from the  Stab-Inter BRGM/IGG  project are gratefully acknowledged. Exchanges and contributions from  Arnault Lassin ,  Mathieu Debure  and  Francis Claret  at  BRGM  and  Claire Fialips  from Total were particularly helpful to us and welcomed. The whole project could not be completed without the contributions of colleagues from  Marseille  (IM2NP:  Jacques Rogez  and  Georges Mikaelian ,  MADIREL :  Renaud Denoyel  and  Emily Bloch ) and from Tokyo ( TOKYOTECH :  Hitoshi Kawaji ), we are very grateful to them. This article is especially dedicated to the late  Eric Giffaut , whose talent and constant efforts were decisive in achieving this 17-year suite of scientific programs.
For quality, interpretation, reproducibility and sharing value, microscopy images should be accompanied by detailed descriptions of the conditions that were used to produce them. Micro-Meta App is an intuitive, highly interoperable, open-source software tool that was developed in the context of the 4D Nucleome (4DN) consortium and is designed to facilitate the extraction and collection of relevant microscopy metadata as specified by the recent 4DN-BINA-OME tiered-system of Microscopy Metadata specifications. In addition to substantially lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training purposes. For microscopy images to be appropriately interpreted and reproduced, and to satisfying Findable Accessible Interoperable and Reusable (FAIR) principles 1 , they should be accompanied by detailed descriptions of microscope hardware, image acquisition settings, image pixel and dimensional structure and instrument performance.
Currently, documentation of imaging experiments is seriously impaired by the lack of easy-to-use software tools that facilitate the extraction and collection of relevant microscopy metadata. Micro-Meta App is an intuitive open-source software designed to tackle these issues that has been developed in the context of the 4D Nucleome (4DN) consortium 2,3 , and of nascent global bioimaging community organizations, including BioImaging North America (BINA) 4,5 and Quality Assessment and Reproducibility in light microscopy (QUAREP-LiMi) 6-8 , whose goal is to improve reproducibility, data quality and sharing value for imaging experiments. The App provides a visual interface for building comprehensive descriptions of the conditions used to produce microscopy datasets as specified by the 4DN-BINA-OME tiered-system of Microscopy Metadata specifications  [9] [10] [11] [12] [13].
NAturE MEthODs and software manufacturers about what information should be written automatically in the headers of image files during image acquisition to ensure scientific rigor. Finally, machine-actionable versions of the same information  30  could be uploaded alongside image-datasets on the growing number of public image data resources  22, [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43]  that allow the deposition of raw image data associated with scientific manuscripts, emulating for light microscopy the successful path that led to genomics community standards  [44] [45] [46] [47] [48]. To promote the development of shared standards, the NIHfunded 4DN  2, 3  and the CZI-funded BINA  4, 5  have recently proposed the 4DN-BINA-OME (NBO) Microscopy Metadata specifications  [9] [10] [11] [12] [13].
To render metadata specifications and QC standards actionable and easy to adopt, experimental scientists require software tools (or, even better, automated pipelines) to easily extract all available metadata from microscope configuration and image files and produce well-documented, high-quality, reproducible and reusable datasets. Despite some advances  26, 56, 57  , current tools offer limited functionalities, and are not integrated with community standards. Here, we present a suite of three interoperable software tools (Extended Data Fig. 1 ) that were developed to provide complementary, intuitive approaches for the bench-side collection of experimental and microscopy metadata  10, 11, 13. In two related manuscripts, we describe: (1) OMERO. mde, which emphasizes the development of flexible, nascent specifications for experimental metadata  16, 17, 58  and (2) the ImageJ/Fiji MethodsJ2 plugin  14, 15  , which automatically generates Methods for scientific publications.
Micro-Meta App: intuitive microscopy experiment documentation. In the absence of tools that expedite image data documentation and QC, the metadata provided by manufacturers often does not align with existing minimal information criteria (that is, OME and BioFormats)  50, 52  and, as a consequence, is extremely variable, interfering with quality assessment, reproducibility and meaningful reuse of third-party datasets (Extended Data Figs. 2  and 3 ). Micro-Meta App, developed to address these unmet needs, consists of an interactive open-source and interoperable software tool to facilitate and (where possible) automate the annotation of light microscopy datasets. It provides a visual approach for documenting imaging experiments based on available OME-compatible community-sanctioned tiered systems (details in Supplementary Information) of specifications, such as 4DN-BINA-OME  [11] [12] [13] 53.
Thu , Micro-Meta App can adapt to varying levels of imaging complexity and to evolving data-models emerging from the community. To this aim, Micro-Meta App uses two parallel dataflows (Fig. 1 ): 1. In 'Manage Instrument' (hardware specifications; Figs. 1b  and  2a and Extended Data Fig. 4 ) microscope users and custodians create accurate graphical depictions of microscope configurations by dragging-and-dropping icons representing hardware components onto the workspace while collecting relevant information scaling with microscope modality, experimental design, instrument-complexity and image analysis needs according to the 4DN-BINA-OME tier-level system  [11] [12] [13] 53   61. These  iles can be stored as described in (1) and associated with related Microscope. JSON files and image-datasets to ensure proper imaging experiment documentation.
Detailed descriptions of the functionality, implementation and documentation material of Micro-Meta App are available in Supplementary Information, Methods and ref. 63. Case studies: use at core facilities. To demonstrate feasibility and test usability, Micro-Meta App was employed, with minimal initial training, at 16 partnering core facilities (Extended Data Fig. 6 ) to document both example microscope instrumentation and the settings used for the acquisition of exemplar image-datasets (Fig. 2 , Extended Data Figs. 7 8 9 and Supplementary Figs. 3 4 5 6 7 8 9 10 11 12 13 14 15 )  [64] [65] [66] [67] [68] [69] [70] [71] [72] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83]. The most striking result (detailed in Supplementary Information) was that, in comparison with the baseline represented by BioFormats alone (Extended Data Figs.
2  and 3 ), the use of Micro-Meta App considerably increased the uniformity of reported metadata fields, facilitating comparison of image data within and across different microscopes and imaging experiments. In addition, since the App's data model is defined dynamically on the basis of shared and evolving community specifications, the use of this method maximizes reproducibility, quality and value, while minimizing effort on the part of individual scientists. Example Microscope. JSON, Settings. JSON and image files produced for the use case in Extended Data Fig. 9  are publicly available on Zenodo as illustrated in the Data Availability section and in Supplementary Information  61. Case studies: integration to 4DN-Data-Portal. An initial impetus for the development of Micro-Meta App was the need to expedite and, where possible, automate the rigorous reporting of imaging experiments and QC procedures for the purpose of integrating 4DN imaging and omics experiments 2.
NAturE MEthODs exchange-sites for microscopy metadata JSON files, similar to ImageJ/Fiji plugin repositories. Furthermore, integration with the Research Resource ID (RRID) effort  93  could promote the recognition of microscope configurations as a quantifiable scientific output, providing credit to the work of imaging scientists. 3. Further integration with MethodsJ2: Micro-Meta App will be extended to automatically generate text for scientific publications and MethodsJ2 (refs. 14, 15 ) will be adapted to use Micro-Meta App Settings. JSON files as sources of image acquisition settings and QC metadata. 4. Further OMERO and OMERO. mde integration: a pilot Micro-Meta App OMERO plugin is available  94. Future development will include extracting experimental metadata developed using OMERO. mde  16, 17 and saving 4DN-BINA-OME metadata  [11] [12] [13]  as collections of key-value pairs associated with individual OMERO image-datasets.
Conclusions Easily accessible and facile tools such as Micro-Meta App, MethodsJ2 and OMERO. mde are essential for microscope custodians and users to see image data documentation and QC as routine tasks in their imaging workflow, therefore promoting better quality, reproducibility and value for imaging data. In addition, reaching this goal will entail partnering with manufacturers to promote the automated interpretation of metadata stored in image file headers, the development of community-wide repositories for microscopy hardware metadata specifications and the automated annotation of datasets to be uploaded in imaging data repositories  30. Thus, key support from funding agencies and institutions will ultimately lead to automating all aspects of the process used by members of the community to annotate and upload metadata-rich imaging datasets to both local and public repositories  38, 40, 51.
Methods Software implementation. Micro-Meta App is available in two JavaScript (JS) implementations. The first was designed to facilitate integration of the software into third-party web portals, such as the 4DN-Data Portal) 2,59,60,62,97 and OMERO  94  , and was developed using the JS React library, which is used widely to build web-based user interfaces. Starting from this version, a stand-alone version of the App was developed by wrapping the React implementation using the JS Electron library, with the specific purpose of lowering the barrier of adoption of the tool by laboratories that do not have access to, or prefer not to use, imaging databases. Dependencies. For the Micro-Meta App to work, the following elements are generated in advance as described in the following sections and made available via GitHub: 1.
JSON schema: a JSON file or a series of files that define the underlying schema, which are used to construct the graphical user interface (GUI) of the application on the basis of the 4DN-BINA-OME Microscopy Metadata specifications  [11] [12] [13]. 2. Dimensions and coordinates: a JSON file that codifies the dimensions of the canvases used by the Manage Instrument GUI of the Micro-Meta App as well as the position on the canvas occupied by icons representing each microscope hardware component. 3. Icons: a series of SVG files, each containing an icon representing the individual hardware components. In addition, the stand-alone version of Micro-Meta App depends on the 4DN Microscopy Metadata Reader  98  , which implements the BioFormats library  52  to allow the user to import known OME metadata directly from the file header of an image data file of interest.
To maximize flexibility, these elements can be customized to meet the needs of individual users. XSD to JSON schema converter. The main function of this Java-encoded component  99  is to transform the XML schema definition (XSD) implementation of the 4DN-BINA-OME data model  [11] [12] [13] 100  into a JSON-based schema, which is subsequently ingested by Micro-Meta App to automatically generate the software GUI and the associated data insertion forms. The XSD to JSON schema converter middleware uses the Xerces2 Java XML Parser 101 and W3C Java XML bindings libraries  102  to navigate the XSD schema, and produces two kinds of version-aware JSON files: 1. A comprehensive JSON file containing an array of schemas for all necessary individual components that constitute the 4DN-BINA-OME data model (for example, Objective, Filter or Detector).
This comprehensive JSON file is made available on GitHub and is designed specifically to facilitate the remote loading of the schema by web portal embedded React implementations of the Micro-Meta App. This schema is available as an individual file on GitHub (https://github. com/WU-BIMAC/4DNMetadataSchemaXSD2JSONConvert er/blob/master/latest/fullSchema. json). 2. A series of JSON files, each containing the schema of individual components, which were designed to be employed by the Electron implementation of the App. These individual schema files are available within a subdirectory of the main repository on GitHub (https://github. com/WU-BIMAC/4DNMetadataS chemaXSD2JSONConverter/tree/master/latest/schemas). The middleware was designed specifically to maximize flexibility and extensibility. As such, the software allows the introduction of implementationspecific modifications of the resulting JSON schema so that it can be adapted for special purposes.
For example, the introduction of a 'Version' field allows validation of whether the data being saved is compatible with the specific version of the schema being employed. As a further example, the introduction of the 'Category' field allows the organization of different components in specific submenus across the sidebar. To facilitate the evolution of the model while ensuring back-compatibility, the GitHub repository supports versioning by storing all revisions of the output JSON schema. TXT to JSON dimensions converter. This Java-encoded component  99  is used to process an input text file containing the dimensions of the Manage Instrument canvas of Micro-Meta App alongside the desired x,y positions where each icon has to be placed. As a result, the software produces a Dimensions and Coordinate JSON file that is made available for remote loading from GitHub (https://github.
com/WU-BIMAC/4DNMetadataSchemaXSD2JSONConverter/tree/master/ latest/dimensions)  99  and is ready to be used to implement the 'snap-in-place' functionality of the software. ICON. Scalable vector graphic (SVG) files containing icons representing the different microscope hardware components were generated specifically for this project and are made available in a version-aware manner for remote loading from GitHub (https://github. com/WU-BIMAC/4DNMetadataSchemaXSD2JSONConv erter/tree/master/latest/images)  99. Custom-made icons can be similarly generated by developer users and manufacturers for representation of their hardware components within the application. Microscopy metadata reader. This software is written in JAVA to fully take advantage of the BioFormats library and the OME-XML metadata structure  98.
Using these two dependencies, this software accesses all the OME-compatible metadata present in user-selected images and maps it to the 4DN-BINA-OME microscopy metadata specifications  [11] [12] [13]  that extend the OME Data Model  50, 52  to produce a temporary, Micro-Meta App-compatible JSON object that can be read by Micro-Meta App. The object is then passed on to the Micro-Meta App and read by the Manage Settings section of the App to prepopulate the corresponding metadata fields. JS React implementation of Micro-Meta App. This is the core implementation of the Micro-Meta App, and it is the starting point for embedding it into a third-party web portal, and for wrapping it into Electron for local execution  97. This component ingests JSON schema files, JSON Dimension and Coordinate files and Icon images produced as described above and uses a series of custom React classes to produce a set of individual windows composing the GUI.
JS Electron implementation of Micro-Meta App. This implementation of the Micro-Meta App is encoded in JS  103. It uses the Electron library to wrap the React implementation of Micro-Meta App with all necessary schemas, icon images and position/dimension to ensure its correct functionality. This discrete executable file can interact directly with the underlying operating system and launch the software from the local file system. Beta testing. Micro-Meta App was developed in the context of community efforts organized around the 4DN Consortium need for imaging data dissemination and integration with omics datasets 2,3 and the BINA 5 effort to improve rigor, QC and reproducibility in light microscopy. As part of this effort, several core facilities were identified to serve as reference beta-testing sites for the Micro-Meta App (a subset of the facilities listed in Extended Data Fig.
Acknowledgements We would like to thank  L. Lifshitz  at the  Biomedical Imaging Group of the Program in Molecular Medicine at the University of Massachusetts Medical School  for invaluable intellectual input and countless fruitful discussions and for his friendship, advice and steadfast support throughout the development of this project. We acknowledge  M. Luban  for critically reading and editing the manuscript. This project could never have been carried out without the leadership, insightful discussions, support and friendship of all OME consortium members, with particular reference to  J. Swedlow ,  J. Moore ,  C. Allan ,  J. M. Burel  and  W. Moore. We are massively indebted to the  RIKEN community  for their fantastic work to bring open science into biology. We would like to particularly acknowledge  N.
S. -D. -C. by the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation , as part of their  Imaging Scientist Program. Major funding also came from: (1)  NIH  grant  U01CA200059  to P. J. P. , C. S. -D. -C and D. G. ; (2)  NIH  grants  1U01EB021238  and  8U01DA047733-05 , and  NSF  grant  1917206  to D. G. C. M. B. was funded in part by grant  2020-225398  from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation. R. A. C. was funded by  NIH  grant  5R01GM126045-04. T. G. and O. F. are members of the  RTmfm network , and the  MRI  and IMAG'IC core facilities are supported by the  National Infrastructure France BioImaging  (grant  ANR-10-INBS-04 ) and IBISA consortium. M. S. I. was supported by grant number  2019-198107 , from the  Chan Zuckerberg Initiative DAF , an advised fund of  Silicon Valley Community Foundation.
Data availability Data associated with this manuscript is available as follows:  (1)  Example Microscopy Metadata JSON files and associated image data file related to the use case presented in Fig. 2  and Extended Data Fig. 9  are publicly available on Zenodo at: https://doi. org/10. 5281/zenodo. 4891883. These files were produced using Micro-Meta App at UMass Medical School to document the acquisition of the FSWT-6hVirus-10minFIX-stk_4-EPI. tif. ome. tif example image file using the TIRF Epifluorescence Structured Illumination Microscope (TESM)  79  custom built by the Biomedical Imaging Group. These files demonstrate the usability of Micro-Meta App to document microscopy experiments. (2) Example datasets associated with Extended Data Figs. 7-8 and Supplementary Figs. 3-15 and used at 16 different imaging core facilities to evaluate the functionality and test the usability of Micro-Meta App, can be made available upon request from the corresponding author.
These exemplar image data files were not produced to test hypotheses or reach conclusions that are part of this study. Rather, they were used successfully as case studies to test the feasibility of the Micro-Meta App approach. (3) Data associated with Extended Data Fig. 10 are available publicly on the 4DN-Data Portal as follows: panel 10a https://data. 4dnucleome. org/files-microscopy/4DNFI7 639BEB/; 10b https://omero. hms. harvard. edu/pathviewer/vanilla-viewer/975042/; 10c https://data. 4dnucleome. org/microscope-configurations/28f1c0f2-d903-4761-93c6-dd3994db3462/. (4) Supplementary Video 1 is also available publicly at: https://vimeo. com/manage/videos/604291798.
Code availability This manuscript describes the Micro-Meta App software tool whose function is to facilitate the collection of Microscopy Metadata that can be used to document the hardware specifications, image acquisition settings and QC metrics used to produce high-quality, reproducible and valuable light microscopy image data. Micro-Meta App is available in two JS implementations that are described in detail above. To promote the adoption and reuse of Micro-Meta App, the executables and source code for both JS-React and -Electron implementations of Micro-Meta App are available on GitHub  97, 103. In addition, a website describing Micro-Meta App  104  was developed alongside complete documentation and tutorials  105. More specifically, as listed in the Reporting Summary document associated with this publication, and on the README file on GitHub (https:// github.
com/WU-BIMAC/MicroMetaApp-Electron), the software is available as follows: (1) Project name: Micro-Meta App. (  2 ) Project home page: https://github. com/WU-BIMAC/MicroMetaApp. github. io. (3) Documentation (including video tutorials): https://micrometaapp-docs. readthedocs. io/en/latest/index. html. Note: if you intend to use the Micro-Meta App on MacOS you might encounter difficulties unzipping and launching the MacOS Zip. To address these issues please follow the special instructions specified in this VIDEO: https://vimeo. com/529609242. (4) Executable: https://github. com/WU-BIMAC/MicroMetaApp-Electron/releases/ latest (https://doi. org/10. 5281/zenodo. 4750765). (5) Source code: (i) https:// github. com/WU-BIMAC/MicroMetaApp-Electron (https://doi. org/10. 5281/ zenodo. 4750765); (ii) https://github.
com/WU-BIMAC/MicroMetaApp-React (https://doi. org/10. 5281/zenodo. 4889259). (6) Operating system(s): (i) Windows ×32; (ii) Windows ×64 (tested on Win 7 Pro v. 6. 1. 760; Win 8. 1 v. 6. 3. 9600; Win 10 Home v. 10. 0. 19040-10. 0. 19041-10. 0. 19042; Win 10 Pro v. 10. 0. 19041-10. 0. 19042; Win 10 Enterprise v. 10. 0. 18362, 10. 0. 19041 and 10. 0. 19042); (iii) MacOS (tested on High Sierra v. 10. 13. 6; Mojave v. 10. 14. 6; Catalina v. 10. 15. 7; Big Sur v. 11. 0-11. 1-11. 2-11. 3-11. 3. 1-11. 4-11. 5 Beta). (7) Programming language: JS and Java. (8) Other requirements: Java v. 1. 8. 0. In addition, see dependencies listed in Methods and the Supplementary Information. (9) License: GNU GPL v. 3 (https://www. gnu. org/licenses/gpl-3. 0. html).