dataesr/scientific-paragraphs-categorization huggingface dataset:
Procedure When the code first opens a datafile, it compares the columns within to a selection of pre-made (and user-creatable) 'configuration' files for each type of input file (e. g. , CDF, HDF5). Each describes a file in terms of the column names within it, and provides metadata for use in the tool -units and display names, and scaling factors that can be applied to change data stored in one unit into data viewed in another. If the data are in an unspecified format, the code will prompt the user to create a configuration file (see Section 2. 2. 3). Alternatively, if their data fit multiple configuration files, they will be prompted to select which they want to use (see Section 2. 2. 2). Once a configuration has been determined, the code parses the observations and may re-bin the time into a coarser resolution in order to improve performance, taking the average of measurements in the new larger bins.
Input formats The code is designed to cope with input files in a variety of file formats and column formats by use of configuration files, several of which are pre-provided. HDF5 input files require at least three datasets, corresponding to observation time (floats, in MJD), frequency range (floats, in any arbitrary unit) and at least one measurement, stored with frequency as the rows and observation as the columns. The names and units for each measurement (in LaTeX form) must be provided in a configuration file, in easilyeditable JSON format. The appropriate configuration files are automatically-selected by the code from those available -making it easy to work with HDF5 files from a variety of collaborators with arbitrary naming schemes. CDF files in NASA format are more structured, and can be read in either singly or as a collection, combining all files in the directory matching the naming scheme [.
]_YYYMMDD_  [. ]. cdf into a single pre-processed data file. As with the HDF5 files, CDF files must contain a frequency attribute (floats, in any arbitrary unit) and a time attribute (either in TT_2000 or CDF_EPOCH format, which is parsed using Astropy, Price-  Whelan et al. , 2018)  and at least one measurement, stored with frequency as the rows and observation as the columns. The code can easily be expanded to ingest other file formats (see Section 2. 3. 2).
'Model-View-Presenter' architecture The code is designed using a standard object-oriented 'Model-View-Presenter' architecture, with strong separation between the data input and management, and the visualisation. This allows for easy development of both new input file-types (see Section 2. 3. 2) and pre-processing options, and alternative GUI front-ends and settings (see Section 5 for suggested development building on top of this flexibility). A generic 'Presenter' controls the logic of the program, and feeds data from the data models to the selected GUI view, and requests from the GUI for changes to the data models. Either the 'View' or 'Model' can be easily interchanged as long as they conform to the API expected by the 'Presenter'. Full development documentation is available on the GitHub repository of the code  (Louis et al.
History of the code The first version of the labelling code was developed in IDL by P. Zarka. It allowed users to read data from an IDL saveset (sav format), draw polygons around features of interest and label them. However, this IDL version had to be adapted to each new dataset. This code has been used to build many catalogues based on different observers (such as the Nançay Decameter Array (NDA) ground-based radio telescope  (Marques et al. , 2017) , or the Cassini  (Zarka et al. , 2021)  or Juno  (Louis et al. , 2021c,b) spacecraft). The second version of the labelling tool was written in Python and was the first to be officially released  (Empey et al. , 2021). This version allowed to automatically read any dataset in sav or cdf format, based on the information requested from users from the terminal.
The other main improvements compared to the previous version were the number of vertices in the polygons (unlimited) and the possibility to modify the vertices position during the polygon drawing (using the Matplotlib's Polygon Selector widget), as well as the production of the catalogue directly in TFCat format. The current version  (Louis et al. , 2022)  brings a large number of improvements, both in terms of architecture, usability and ergonomics, which are described in the previous sections. The SPACE tool has also joined the MASER service  (Cecconi et al. , 2020). 10. 3389/fspas. 2022. 1001166 Software  (Faden et al. , 2010). For an example, the reader is invited to visit the web page https://doi. org/10. 25935/nhb2-wy29 where an autoplot template file is given to display the Juno data  (Louis et al.
Limitations and future work The code is ready for distribution and use, but has some technical limitations. Potential works to address those limitations, and avenues of future development, are: • Performance: The MatPlotLib-based front-end can struggle when provided with especially high resolutions of data, or over large time windows. Rebinning features exist to mitigate this, but an alternative rendering framework could be employed (e. g. , datashader 4  ), and/or the performance of the interactive front-end components could be improved if re-implemented in a more performant framework [e. g. , Plotly Inc. (  2015 )]. • Scalability: The code loads all the data provided into memory at launch, limiting its applicability for large datasets. Whilst this can be mitigated by the feature to allow appending to TFCat files created by data files sharing filename formats, a 'deferred load' approach would be better.
Configurations: The code depends heavily on pre-written configuration files, and can prompt users to create missing ones -but does not yet contain a 'wizard' or automatic walkthrough to aid users in creating them. • Platform: Run the SPACE labelling tool in a notebook (launch on Binder). • Catalogue integration: The modular format of the code would make it possible to create 'dataset' types that access and download data directly from online catalogues, maintaining local caches. Southampton Research Software Group was funded by a grant via the Alan Turing Institute. KS's work at the Dublin Institute for Advanced Studies was funded by a 2022 SCOSTEP/PRESTO Grant. The Europlanet 2024 Research Infrastructure project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871149.
Data availability statement The code of the SPACE labelling Tool is open-source and freely available on github (Louis et al. , 2022). The Cassini/RPWS dataset displayed in Figure  1A , produced by  Lamy et al. (2008)  is available at https: //doi. org/10. 25935/ZKXB-6C84 (Lamy et al. , 2009). The Juno/Waves dataset displayed in Figure  1B , produced by Louis et al. (2021c), is accessible at https://doi. org/ 10. 25935/6jg4-mk86 (Louis et al. , 2021a), and the catalogue can be download at https://doi. org/10. 25935/ nhb2-wy29  (Louis et al. , 2021b). The Polar/PWI dataset displayed in Figure  1C  is accessible through the CDAWeb at https://cdaweb. gsfc. nasa. gov/ pub/data/polar/pwi/.
• Configurations: The code depends heavily on pre-written configuration files, and can prompt users to create missing ones -but does not yet contain a 'wizard' or automatic walkthrough to aid users in creating them. • Platform: Run the SPACE labelling tool in a notebook (launch on Binder). • Catalogue integration: The modular format of the code would make it possible to create 'dataset' types that access and download data directly from online catalogues, maintaining local caches. Southampton Research Software Group was funded by a grant via the Alan Turing Institute. KS's work at the Dublin Institute for Advanced Studies was funded by a 2022 SCOSTEP/PRESTO Grant. The Europlanet 2024 Research Infrastructure project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871149.
| Flux and environmental measurements During the growing season of 2016-2018, flux measurements of CH 4 and CO 2 were conducted on a bi-weekly basis at both sites, from early June to the end of August with an additional campaign in early October 2017 (a total of 20 field campaigns), utilizing a closed chamber technique on pre-installed soil collars. Three groups of three collars were installed at each site within the area covered by the vegetation surveys, and gas flux data were averaged for each group (n = 3 per site). Fluxes of CH 4 and CO 2 fluxes were measured using an ultraportable greenhouse gas analyzer (Los Gatos Research) operating at 1 Hz, with a precision of <2 ppm and <300 ppm (respectively) and an operational range of 0-500 and 0-20,000 ppm, respectively. For net exchanges of CH 4 and CO 2 , the analyzer was connected to a transparent chamber (r = 9.
5 cm, h = 20 cm), while ecosystem respiration was measured in darkness by covering the   S2 ) established from the over 20 years of overlapping datasets between Abisko and Latnjajaure Field Station (LFS). A polynomial function was imposed on each dataset as described by  Callaghan et al. (2010). The shaded area "A" indicates the time-window when permafrost was last observed (1993) and confirmed absent (2001) in Latnjajaure  (Beylich, 2003; Beylich et al. , 2004; Molau, 2010)  Both net fluxes and respiration rates were measured three times each from every collar, with a 5 min closure time and one-minute aeration of the system in between measurements. The change in chamber concentration over time was then calculated using linear regression of the dry concentration of each gas (provided by the instrument), disregarding the first 30s of all measurements and regression with R 2 <.
8 for the ecosystem respiration. No threshold cutoff was made for the net exchange of CO 2 and CH 4 since these can be both positive, negative or zero (giving low R 2 values). Furthermore, soil temperature (-5 cm) and soil moisture were measured four times around each collar using handheld soil thermometers and a Delta ML2x Theta probe (Delta-T Devices Ltd), respectively. Meteorological parameters such as air temperature, snow depth, precipitation, and pressure were recorded using a U30 HOBO automatic weather station (Onset Computer Corporation, Bourne, Massachusetts, U. S. ) at each site, and soil temperatures were logged at 2, 10 and 30 cm depths at two locations in each site (TinyTag, Gemini Data Loggers). Measurements from the local weather station at Latnjajaure Field Station  (Scharn,, Brachmann, et al.
| Soil sampling Soil samples for microbial and chemical analysis were collected <0. 6 m from each collar used for gas flux measurements. The top organic layers (0-5 cm) and deep mineral layers (15-30 cm) of the tussock soils were sampled using either an electric drill (r = 1 cm), during the initial frozen conditions, or with a soil auger (r = 1. 5 cm). Soil samples were pooled to form one homogenized sample per soil depth for each group of three collars, allowing for comparisons with the flux data (n = 3 per site and soil layer). Microbial samples were transferred to sampling tubes with silica gel (both sterilized), samples for chemical analysis were collected in Ziploc bags, and both were stored cold during fieldwork and frozen within one week of sampling. Microbial samples were shipped frozen to the University of Lyon, France, while biochemical samples were shipped to the University of Gothenburg, Sweden.
| Sequence processing Forward and reverse read files for all sequenced genes of each sample were merged using the iu-merge-pairs command from the Illumina-Utils v2. 6 libraries using the-enfore-Q30-check flag to ensure quality filtering over the entire read length before merging  (Eren et al. , 2013). Successfully merged amplicons of the V4 region of the 16S rRNA gene were filtered for chimeric sequences and further annotated with the RDP classifier  (Edgar et al. , 2011; Wang et al. , 2007). Taxonomic annotation of the sequenced marker genes was performed by placing sequenced amplicons in a reference tree using GraftM (version v0. 13. 1; https://github. com/geron imp/graftMreleased under GNU General Public License v3+)  (Boyd et al. , 2018). The graftM package including reference sequences for mcrA gene was downloaded from the repository at GitHub.
For pmoA annotation, a multiple sequence alignment calculated with MAFFT (version v7. 475)  (Katoh et al. , 2002)  of 7809 sequences downloaded from the pmoA gene reference database at the GFZ Potsdam  (Yang et al. , 2016)  was used to create a graftM package. All further processing and analysis on annotation tables were carried out using the Rpackage phyloseq (McMurdie & Holmes, 2013; Core R Team, 2019). Significances of differences of means between sample groups were estimated by the ggpubr package. Differential abundance analysis on the 16S rRNA gene dataset was performed using the DESeq2 package. Diversity estimates were calculated with the R-packages vegan and betadisper.
S1 ), the Observatoire Volcanologique de Goma (OVG), and several local institutions. Goals were to build a database of fractures and gas/thermal anomalies within the urban areas of Goma and Gisenyi, and allow a team of national and international scientists to carry out gas and temperature measurements at these fractures. The area is well-known for CO 2 -rich gas pockets, locally called "mazuku", which are considered one of the most persistent risks for the inhabitants of the area. CO 2 is a colourless and odourless asphyxiant gas, denser than air, and which at concentrations higher than 10% will lead to coma and death in few minutes  13. It was therefore essential to identify if, within Goma and Gisenyi, CO 2 was escaping from the cracks. Insights from the participatory tracking and monitoring system of the fractures.
A total of 458 fractures (Supplementary Data S1) were identified by the population involved in the participatory tracking and monitoring system of the fractures. The map revealed two main parallel networks of fractures from North to South (Fig. 2 ). These fractures networks extended parallel to the rift axis  14  and were reminiscent of the evolution of segmented continental rift basins  15. The first network of fractures crossed the city of Goma ("GoF" stands for "Goma main network of Fractures") with a severity index rarely exceeding 3 out of 5 (Fig. 2a ) on the scale defined by the local representants and collaborators of the United Nations (see "Methods"). Several thermal and gas anomalies were reported by the population on the northern part of the "GoF" (Fig. 2b ). The second network of fractures extended from the northern districts of Goma (namely "Munigi") to Gisenyi ("GiF" stands for "Gisenyi main network of Fractures") and was marked by a higher index of severity (> 4).
Many of the people in whose houses the measurements were performed, or to whom the preventive actions were communicated, expressed their satisfaction with the presence of the observatory's staff in the field. The strategy for geochemical measurements was then based on these maps derived from citizen participation and permitting a better response to the 2021 volcano-tectonic event. Isotopic data of gaseous emissions released by fractures in the two urban areas were consistent with increased crustal degassing through the newly opened fractures rather than new magma degassing. This finding minimizes, for this specific case study, the probability of occurrence of a new eruptive event within the urban areas as a consequence of dyke intrusion. Content of CO 2 in the air inside houses targeted during the geochemical survey rarely exceeded 1000 ppm (less than 10% of the dataset indicates CO 2 content above 1000 ppm with a maximum value of 5100 ppm, slightly above the maximum recommended occupational exposure limit).
A flyer describing the procedure and written in English, French, and Swahili (Supplementary Fig. S1 ) was disseminated to the citizens by both personal acquaintances and key actors, such as local businesses, the Stabilization Support Unit (SSU) of the MONUSCO, and religious organizations which are locally very much The end-members used in the mixing model are defined in the "Methods". Credits Attribution: Esri, NASA, NGA, USGS; RMLUA, OpenStreetMap, HERE, Garmin, METI/NASA. listened to and respected. Then, the flyer spread by word of mouth. The data were then continuously collected by a team of 6 people from UNDP including the manager A. Colacicco, each taking turns every 4 h. On May 29, 2021, the first map of the fractures was produced and communicated to the volcanologists. The toll-free number triggered a constant flow of information which allowed daily updates of the database and of the map in the following days.
On June 1, 2021, the UNDP and the SSU managed to put together and train a team of 44 volunteers to carry out a mobile-based data collection at Goma (June 3-5, 2021) in order to cross-check the information received via WhatsApp, complete the mapping of the whole city, and define a severity index of the fractures. This index (from 1 to 5) was defined on the basis of the length, thickness and continuity of the fractures (i. e. , spatial interconnectivity between them). In parallel, geologists from the RMB were deployed to Gisenyi to map the fractures. The 458 fractures presented in this study were identified by the population involved in the participatory tracking and monitoring system and cross-checked by the team of volunteers. All "questionable" data, such as the appearance of fractures before the eruption, were double-checked by the volunteer in charge of the zone.
This volunteer was instructed to verify the date of appearance of the fractures in presence of a minimum of 2 witnesses. No data about the human participants were collected for the following three reasons. (1) The study on fractures is based on observation of hard facts, therefore those should not be influenced by the social status of the observer. (2) A participatory system should be accessible, quick an easy. We cannot take too much time from citizens; therefore, only straight forward information, which are strictly related to the focus of the research, have to be asked. (3) The survey was carried out in a time of emergency while the cities of Goma and Sake were shaken by continuous earthquakes. In order to guarantee the safety of the people who are sharing data while standing right next to potentially dangerous spots it is crucial to minimize their time in the zone of danger as much as possible.
Therefore, the survey needed to be straight to the point. Field measurements. Soil CO 2 flux measurements were performed following the Accumulation Chamber Method  29  by the way of a West System device composed by an accumulation chamber (type A) connected to a temperature-stabilized LI-COR LI-830 infrared sensor (0-20,000 ppm CO 2 ) compensated in pressure and temperature. The sensor has a detection limit of 1. 5 ppm on CO 2 content and a 3% accuracy of reading. The flux (g m -2 day -1 ) of each measurement was elaborated from the related regression line (ppm s -1 ) by using the FluxRevision free software, knowing the air temperature, barometric pressure and volume of the accumulation chamber. Barometric pressure and air temperature were recorded for each point of measurement by the mean of a Kestrel 5500 Weather Meter with an accuracy of 1.
20‰ on δ  13  C. Abundance and isotopes compositions of He were determined by the mean of a Helix SFT-GVI split flight tube mass spectrometer at the INGV Palermo. The analytical error on He isotopes analyses was less than 0. 4%. The purification procedure for helium and the full procedure is the same as described in Boudoire et al. 30. Data elaboration. The 3 He/ 4 He (R) is expressed as R/Ra (Ra being the He isotopes ratio of air equal to 1. 39 × 10 -6 ). δ 13 C of CO 2 refers to the ratio of stable isotopes 13 C/ 12 C (reported in part per thousands) and normalized to Pee Dee Belemnite (PDB) reference standard. Mixing curves on Fig. 4b  were defined by using (i) an averaged δ 13 C value of -3. 7‰ from Nyiragongo fumaroles (at 100% CO 2 ) for the magmatic end-member 20 , (ii) a biogenic δ  13  C value of -24.
7‰ from charcoal (at 100% CO 2 ) for the biogenic end-member  31  , and (iii) an atmospheric value (at 398 ppm CO 2 measured by gas chromatography from a glass vial collected at Goma) of -8. 4‰ in accordance with recent measurements performed in the atmosphere  32. Individual measurements of δ 13 C of CO 2 were corrected from air contamination (aerated fractures) by looking for the intercept of the linear regression between 1/CO 2 and δ  13  C of the sampled gas and the air. The intercept reflects the δ  13  C value at 100% of CO 2 (i. e. , without air contamination). Maps were created using ArcGIS ® software (ArcGIS Pro 2. 8) by Esri in the Projected and Geographic Coordinate System WGS 1984 Web Mercator Auxiliary Sphere and WGS 1984 UTM Zone 35N (Landsat). ArcGIS ® and ArcMap™ are the intellectual property of Esri and are used herein under license.
Copyright © Esri. All rights reserved. For more information about Esri ® software, please visit www. esri. com. Interferograms were generated from Sentinel-1 (ESA Copernicus) products to visualize the large-scale deformation related to the dike intrusion. Sentinel products were freely downloaded from the Copernicus Open Access Hub (https:// scihub. coper nicus. eu/), and processed by the volcano monitoring system MOUNTS  33. The interferograms are computed by DInSAR (Differential Synthetic Aperture Radar Interferometry), from two products acquired on 2021-04-21 16:20 UTC and 2021-05-01 16:20 UTC, and two other products acquired on 2021-05-19 16:20 UTC and 2021-05-31 16:20 UTC (ascending orbit, relative orbit number 174). Interferograms are displayed with a wrapped interferometric phase, whereby each fringe (full phase cycle ranging between 0 and 2π) corresponds to 2.
Acknowledgements We are grateful to the OVG and the RMB for their invitations and logistical help. The MONUSCO is warmly thanked for providing crucial support in the field deployment. The  UNDP  and the  NGO Concern Worldwide  are thanked for their assistance in the field and preliminary work before the arrival of the scientific team. We thank the  INGV  staff for their assistance during gas analysis in laboratory. We thank  A. Harris  ( LMV ) for his informal review of the manuscript. We are grateful to the  Editor of Nature Scientific Reports (A. Billi) , to  M. Lopez-Saavedra  and two anonymous reviewers who significantly improved the manuscript through their comments. Finally, we are in debt with the population at  Goma and Gisenyi  for their help, support and welcome in the field that strongly contributed and motivated this study.
Data availability All data are available in the main text or in the Supplementary Material 1, and Supplementary Tables  1  and 2. In Figs. 1a,  2, 3 and 4a the Digital Elevation Model was processed from the STRM data of the USGS EROS Data Center and the shapefile for lava flows comes from UNITAR-UNOSAT products based on a Sentinel-1 image acquired on May 25, 2021. The background maps on Figs. 2, 3 and 4a are extracted from the ESRI World Terrain Base (ArcGIS) and the Landsat 8 Operational Lan Imager (OLI) and Thermal Infrared data of the USGS EROS Data Center (LC08_L1TP_173061_20200817_20200822_01_T1). In Fig. 1a-f, pictures are from A. Colacicco, P. Sordini, and G. Boudoire. In Fig. 3d, the interferogram is generated from Sentinel-1 (ESA Copernicus) products. Sentinel products were freely download from the Copernicus Open Access Hub (https:// scihub.