dataesr/scientific-paragraphs-categorization huggingface dataset:
Resistome and virulome ABRicate (https://github. com/tseemann/abricate) with NCBI AMRFinderPlus  (165) , Res nder (166), ARG-ANNOT  (167) , CARD  (168) , EcOH  (169)  and VFDB  (170)  databases was used to search antibiotic resistance and virulence genes in genome sequences. The softwares antiSMASH (accessible via MicroScope platform)  (114)  and BAGEL4 (URL: http://bagel4. molgenrug. nl/) were used to search bacteriocins and Ribosomally synthesized and Post translationally modi ed Peptides (RiPPs) clusters sequences in S. thermophilus genome sequences. Resulting heatmap was drawn with Python's matplotlib library  (171).
More experimental details can be found in the work of  Polikarpov et al. (2018)  and  Zhang et al. (2019). To eliminate the glitches caused by the monochromator as well as to compensate the refills of the synchrotron ring with electrons, the transmitted intensity was normalized by the incident intensity (I1/I0) and then by its smoothed version (to compensate for the change in absorption by the sample with X-ray energy as well as different efficiency of the beamline and the ion chambers). The processing of the experimental spectra is automated and described in detail by  Klimova et al. (2020) ,  Klimova, Yefanov et al. (2021)  and the programs are deposited at GitHub (https://github. com/XrayViz/Glitches).
Theory of glitch formation in transmission geometry During the energy scan the direction of the beam, incident on the sample, is kept constant, while its energy (wavelength) changes. This means that in the reciprocal space the length of the incident wavevector K 0i is changing, while its direction (unit vector e 0 ) is constant  [Fig. 3(a) ]. An animation demonstrating the process of glitch formation simulated using the program XVis  (Yefanov et al. , 2008)  can be found at GitHub (https://github. com/XrayViz/Glitches). A glitch in the spectrum at energy E i (or wavelength i ) is formed when the Ewald sphere with radius K i ¼ 1 ¼ E i hc ' E i ðkeVÞ 12:39842 ð1Þ and centred at the beginning of the current vector K 0i intersects some reciprocal-lattice point (RLP) characterized by the reciprocal-lettice vector H i.
Experimental data processing As described earlier, the measured spectrum is normalized (Fig. 2 ) and then the actual energy of each glitch has to be determined. We extract the energy of glitches using the automatic approach described in detail by  Klimova, Yefanov et al. (2021)  -the corresponding program is deposited at GitHub (https://github. com/XrayViz/Glitches). The energies of the glitches are processed in two steps: first by fitting the approximate orientation and UC parameter to index the measured glitches and then refining the parameters by solving the system of equations (  11 ). The sources of the programs for this part of data processing are also deposited at GitHub (https://github. com/XrayViz/Glitches). We should note here that in our previous work  (Klimova, Yefanov et al. , 2021)  we have used a different approach for the indexing: we have probed all energies for each orientation to find the RLPs that are close to the Ewald sphere for each energy.
Unfortunately, the angular error Á can lead to the wrong indexing of the measured glitches -wrong Miller indices can be assigned to some of the glitches. Therefore, it is better to refine the angular offset Á at the indexing stage -this would make the indexing process longer, but the whole procedure will be more reliable. The corresponding program can also be found at GitHub (https://github. com/XrayViz/Glitches). Alternatively, the approximate angular correction can be determined using equation (  14 ), then the experimental spectrum is recalculated and the indexing-refinement procedure repeated. The procedure of the monochromator pitch angle correction led to the following corrections in our measurements: Á was À0. 0164 and À0. 0045 for the two cases shown in Fig. 5(c ) which corresponded to an error in energy determination of 44 eV and 12 eV, respectively, at 10 keV range.
The energy range of the measured spectrum at hard X-rays (>10 keV) can be rather small -200 eV should be sufficient. For lower energies a larger scan could be needed -the programs to simulate the glitches for a crystal with a diamond cubic lattice (silicon, germanium, diamond) can be found at GitHub (https://github. com/XrayViz/ Glitches). From the measured spectrum, using the approach described in this paper, one can calculate the pitch offset Á and the monochromator cell parameter a mono. There is one more way to perform such a calibration. If the beamline is equipped with a precise goniometer, but the energy scans are not possible, one can measure intensity drops of the transmitted beam while rotating the calibration crystal. Knowing the relative angles between the glitches measured in such a way, one can also calibrate both pitch angle and cell parameter of the monochromator for any energy.
Acknowledgements We thank the experimental team:  Maxim Polikarpov  from the  Molecular Biology Laboratory (Hamburg, Germany ),  Qiuyuan Zhang  and  Helge B. Larsen  from the  Department of Mathematics and Physics, University of Stavanger (Stavanger, Norway) ,  Ragnvald Mathiesen  from the  Department of Physics, Norwegian University of Science and Technology, (Trondheim, Norway) ,  Hermann Emerich  from the  European Synchrotron Radiation Facility  ( ESRF ) for their contribution. OY acknowledges support from  H. N. Chapman. Funding Statement: Open Access funding enabled and organized by Projekt DEAL.
The most proper color code can be interactively selected by the user (e. g. , a red-to-green code can be selected to emphasize that some labels are "preferred" to others). Figure  6  shows an example of visualization (obtained through the web-based interface for our prototype implementation) after the first step of progressive refinement. The two benchmarks proposed are of parent and sibling type, respectively; the default comparison and labeling functions used are shown in angular brackets.
EXPERIMENTS The source code for the prototype implementation we used for the tests is available at https://github. com/big-unibo/ assess; the web-based interface can be accessed at http: //big. csr. unibo. it/projects/assess/. The prototype uses the simple multidimensional engine described by  [16] , which in turn relies on the Oracle 11g DBMS to execute queries on a star schema based on multidimensional metadata (in principle, the prototype could work on top of any other multidimensional engine). The mining models are imported from the Scikit-Learn Python library.
Description The relevant figures for the two cubes used in the experiments are summarized in Table  4. In Exp1, 5 volunteers were involved. All of them were data journalists or data enthusiasts with little or no skill in databases and OLAP but a good knowledge of the business domain (electric consumption in France). In Exp2, 10 PhD students and postdocs with good database and OLAP skills and some generic 4. https://data. enedis. fr/explore/dataset/ consommation-electrique-par-secteur-dactivite-iris/export/ 5. https://www. ecdc. europa. eu/en/publications-data/ data-national-14-day-notification-rate-covid-19 knowledge of the business domain (COVID-19 infections) were involved. In both cases, the users were first shown a 10 minutes tutorial video explaining the meaning of assessment, the operator syntax, and the test goal 6 ; then they were assigned two tasks: Task 1: Assess a specific cube measure for a specific slice starting from a given partial statement.
Specifically, among contrast operators, some variants of a DIFF operator have been introduced either to (i) return the set of tuples that most successfully describe the difference of values between two given cube cells  [22] ; (ii) group and highlight commonalities among data points  [23] ; or (iii) pinpoint differences between two datasets that share the same discrete attributes  [24]. The EKISO algorithm  [25]  returns the best insights derived from aggregating a cube; insights are scored with respect to how much the returned value impacts on the aggregate and how unexpected the insight is. The RELAX operator verifies whether a pattern observed at a certain level of detail is present at a coarser level of detail too  [26]. In the same direction of RELAX, in  [27] , the authors evaluate and confirm the accuracy of user insights on a given query result.
Many studies focus on learning users' preferences to suggest personalized search and give recommendations  [30] ; this is somehow complementary to our approach, since assess could be one of the potential patterns to be learned and recommended to the users. Similarly, in  [31] , the authors profile the explorations of a user and use the maximum entropy principle to recommend parts of the cube can be the most surprising in a subsequent query. NextiaJD  [32]  aims at finding interesting and accurate joins (in other terms, at suggesting benchmarks) to bring together datasets with heterogeneous schemata. Other approaches, that can be profitably used for interactive data exploration, aim at creating data descriptions, i. e. , explanations that make large dataset more understandable at a glance by a user.
For instance, in  [33]  this description is generated in the form of predicates that apply to the target dataset. As to visualization, query result comparison is achieved by showing multiple visualizations juxtaposed and highlighting the difference between them  [4]. In  [5] , the authors return visualization sequences by grouping subsets of visualizations with shared properties (e. g. , a common measure or time period) so as to minimize the cognitive cost (or perceived amount of difference) across adjacent views. Similarly, Voyager2  [34]  allows users to specify multiple charts in parallel by authoring partial specifications; Voy-ager2 returns a chart gallery, showing all charts that satisfy the specified constraints, and uses the global minimum and maximum values of a data field to aid comparison across charts.
In this article, the potential of raw material analysis with ambient ionization MS for the exploration of lichen metabolomes is exploited. The comparison of ASAP-MS and DART-MS raw material analyses with direct infusion (DI)-electrospray ionization (ESI)-MS experiments on extract material and an in-house database of around 2,000 lichen compounds (LDB-Lit)  21  is proposed to give a deeper analytical insight. Through examples of ionization profiles-ranging from raw mass spectra to processed data-and multivariate analysis, the respective interests of ASAP-MS and DART-MS for lichen metabolomics will be shown.
More detailed parameters are given in Supplementary text 1. Data were acquired in single batch analysis with a thermal desorption to noise level between every sample, and controls showed no loss of sensitivity. For DART-MS analyses, lichen thalli were maintained with a clamp directly in front of the ionization source. Samples were analyzed in the on-axis mode using helium (He Alphagaz 1, purity 99. 999%, AirLiquide) as the desorption-ionization gas. Thus, with regard to the geometry of the ionization source and the gas pressure, ground lichens were not analyzed in order to avoid damaging the instrument. More detailed parameters are given in Supplementary.
| Bioinformatic data processing Data were processed according to a classification approach based on the VK diagram. 18 It was used as described with the scripts provided at https://github. com/siollivier/directacquisitionproject. Briefly, the scripts generate the best molecular formulae from HRMS data, apply an adduct correction, and then classify the features depending on elemental ratios. [M + NH 4 ] + adducts were additionally taken into account when generating the molecular formulae for DART-MS. The processed dataset, including the DI-ESI-MS analyses, was added to our OSF project for Ambient Ionization MS at https://osf. io/6pyuq/ (dataset_pre-extract_comp_publi2. tsv).
| Multivariate data analysis Both adduct-corrected and uncorrected peak lists were exported to *. csv files with R-script and analyzed with the online platform Meta-boAnalyst 5. 0. 23 The adduct correction allowed direct comparison of the different techniques and of both polarities. The data were binned with a tolerance of ± 5. 10 À4 u (0. 5 mu) and missing values were replaced by a small value. Data were also filtered with an interquartile range and then normalized by applying quantile normalization, log transformation, and Pareto scaling to limit the influence of peak intensity, as the data were acquired on two different instruments. When our VK diagram-based protocol was applied for profiling purposes, the data of all lichens were merged. When showing spectra or statistical analyses, examples on a single lichen are provided to retain significance.
Furthermore, in negative ion mode, DART-MS allows an intense detection of compounds over m/z 550. Some differences appear in the low m/z range between ASAP and DART; this is mainly due to the strategies chosen to optimize detection across the whole m/z range. DART-MS data were obtained with a Q-OT, on which this optimization was done through the S-lens RF level, whereas for ASAP-MS, an ion-cooler RF voltage was applied in the Q-TOF instrument. This ion-cooler RF still allows the transmission of the ions, but lowers their intensity, making them less apparent on the spectra. For the comparison of both techniques, the distribution of compounds across the entire m/z range was also studied: raw spectra are biased by intensity, whereas a density plot of all detected masses gives a more global insight (Figure  2 ).
24 The data are acquired on two different spectrometers with respectively time of flight and orbitrap as last MS analyzer. A comparison study between both spectrometers with ASAP source mounted could be of interest. These data also show that electrospray cannot be neglected because it highly detects discriminants-it is, after all, the golden standard with or without chromatography-so in the future it could be interesting to consider other raw material analysis methods based on ESI, such as desorption ESI. 25  The raw material analyses by ambient MS will not allow the detection of all the compounds found after extraction, but this process still appears to allow a good overview of the general composition of the lichen material. However, it is also worth noting that the extraction process sometimes induces chemical reactions and therefore artifact compounds.
The presented spectra-obtained from Parmelia saxatilis-are averaged over the whole temperature ramps. All associated data and annotations are available at https://osf. io/6pyuq [Colour figure can be viewed at wileyonlinelibrary. com] 3. 2 | Direct ASAP-MS and DART-MS profiling of the lichen metabolome Overall, the combination of ASAP and DART appeared interesting because they could potentially provide information for a wide range of compounds. So, the metabolome of four lichen species was profiled-E. prunastri, L. pygmaea, P. saxatilis, and R. fuciformis-to gain additional insight on the types of compounds ionized and a comparison was performed with DI-ESI-MS experiments on extracts and an in-house database of lichen compounds to prove the entirety of the analysis process and content (i. e.
, surface and internal metabolites and general knowledge). In the present study, these features are similarly detected from MS analyses performed directly on the lichens. In positive ion mode, the ionization profile is similar for both techniques, and DART-MS detects more compounds (average number of ions 2,754/run vs. 1,211/run for ASAP-MS). In negative mode DART also complements ASAP-MS: it appears to alleviate a lack of detection of polyphenols and derivatives. DI-ESI-MS on the extracts represents the entirety in terms of detection capacity for surface and internal metabolites in ASAP-MS and DART-MS; the in-house database represents a milestone of lichen characterization knowledge. There are some specificities like the ionization of terpenes and unsaturated hydrocarbons in DART-MS(+) or ASAP-MS(+), of polyphenols and derivatives in DART-MS(À), or of condensed aromatic compounds in ASAP-MS(À), but all families are represented (Figure 3a and 3b, the numeric values are summarized in Table.
Evaluation of the chemical diversity of the raw lichen material compared with an in-house database (LDB-lit) in positive (A) and negative ionizations (B). Both the relative frequency within each technique (histogram) and the absolute number of detected ions (heatmap) are presented. (C) Extracted Van Krevelen diagram for benzenoids (or atypical terpenoids) in ASAP-MS and DART-MS. The size of the circle is proportional to the number of occurrences of the (O/C, H/C) coordinates [Colour figure can be viewed at wileyonlinelibrary. com] F I G U R E 2 Distribution of the calculated exact masses obtained through the analysis of lichens by DI-ESI-MS after extraction (orange solid line), the combination of ASAP and DART-MS on raw lichen (purple solid line), ASAP-MS (red dotted line), and DART-MS (blue dotted line) as standalone means of analysis, and the distribution of an in-house database (LDB-lit) (green solid line) [Colour figure can be viewed at wileyonlinelibrary.
F I G U R E 3 3. 3 | 333 Evaluation of the chemical diversity of the raw lichen material compared with an in-house database (LDB-lit) in positive (A) and negative ionizations (B). Both the relative frequency within each technique (histogram) and the absolute number of detected ions (heatmap) are presented. (C) Extracted Van Krevelen diagram for benzenoids (or atypical terpenoids) in ASAP-MS and DART-MS. The size of the circle is proportional to the number of occurrences of the (O/C, H/C) coordinates [Colour figure can be viewed at wileyonlinelibrary. com] F I G U R E 2 Distribution of the calculated exact masses obtained through the analysis of lichens by DI-ESI-MS after extraction (orange solid line), the combination of ASAP and DART-MS on raw lichen (purple solid line), ASAP-MS (red dotted line), and DART-MS (blue dotted line) as standalone means of analysis, and the distribution of an in-house database (LDB-lit) (green solid line) [Colour figure can be viewed at wileyonlinelibrary.
Therefore, this signature of changes in surface conditions on subsurface temperatures can be estimated by subtracting the quasi-equilibrium profile to the measured log, obtaining an anomaly profile. Ground heat flux histories retrieved in this analysis are based on inverting the anomaly profile of each individual subsurface temperature profile. We invert subsurface temperature profiles from the Xibalbá dataset  (Cuesta-Valero et al. , 2021a,b)  to estimate the global long-term ground heat flux history. The Xibalbá dataset consists of 1079 subsurface temperature profiles measured around the world, with a larger number of profiles in the mid latitudes of the northern hemisphere. Xibalbá logs have been harmonised to include temperature records from 15 m to 300 m of depth. Ensuring that all logs are truncated at the same depth is crucial to obtain temperature and heat flux estimates relative to the same temporal reference, approximately the period 1300-1700 in this analysis.
, 2009). Required input data are extracted from multiple global datasets such as soil sand and clay fractions  (Masson et al. , 2003; Faroux et al. , 2013) , the soil organic carbon content  (Hugelius et al. , 2013) , and the soil thickness  (Pelletier et al. , 2016). Uncertainties in soil stratigraphies affecting latent heat storage are primarily determined by the amount and distribution of ground ice. Such uncertainties are accounted for by ensemble parameter simulations (N=100) using a Monte Carlo approach that randomly varies soil thickness as well as the thickness of soil layers with different ice saturation  (Langer et al. , 2022; Nitzbon et al. , 2022). Additional data on excess soil ice are included by increasing the soil ice content for 50% heat of fusion of water (334×10 6 kJ m -3 ) yields the average annual latent heat uptake per square metre which is multiplied with the land area (excluding the surface water area) contained within each model grid cell.
, 2014)  driven by the same ESMs. In total, the ensemble contains 16 global lake simulations, providing lake temperature profiles (Table  3 ) for the period 1900 to 2021 on a 0. 5° by 0. 5° grid. These simulations are combined with global gridded lake depths from the Global Lake Database v. 3 (GLDB;  Choulga et al. , 2019)  rasterized global lake and reservoir area from HydroLAKES and GRanD databases  (Messager et al. , 2016; Lehner et al. , 2011) , as described in  Vanderkelen et al. (2020). Different from the cylindrical lake assumption of  Vanderkelen et al. (2020) , in which the grid cell lake volume is calculated by multiplying lake area and depth, we determine lake and reservoir volumes by estimating lake morphometry with the volume development parameter (Vd), which is a well-established geometric approach  (Johansson et al.
, 2007; Håkanson & Hakanson, 1977). The Vd parameter represents the extent to which the lake volume deviates from the volume of a cylinder, thereby indicating whether the lake morphometry is concave or convex. We employ a global constant Vd value of 1. 19, which is the median Vd derived from the 1 427 688 lake polygons in the GLOBathy dataset  (Khazaei et al, 2022)  using their mean and maximum depths (Vd = 3 dmean/dmax). Per grid cell, the lake volume is calculated as a 'reversed wedding cake' by multiplying the thickness of every discrete lake layer of the lake model with the average area at the layer depth A(z), calculated following  Johansson et al. , (2007)  as A ( z )= A max [ ( 1 -d rel )( 1+d rel sin (√ d rel )) ] f (Vd ) (2) and f (V d )=1. 7V d -1 +2. 5 -2. 4 V d +0. 23 V d 3 (3) with Amax (m²) the surface lake area calculated based on the gridded HydroLAKES distribution, drel (m) the relative lake layer depth ( drel = z/zmax), where zmax (m) is given by the GLDB lake depth, and finally Vd (-) the volume development parameter, taken constant at 1.
Ideally, an international organisation should gather a copy of all available measured subsurface temperature profiles to ensure the maintenance and accessibility of these valuable records in future decades. Such a safe copy of all logs should lead to less fragmented datasets, harmonising the archiving practices and metadata requirements for all records, in contrast with current practices in which individual researchers are responsible for measuring, curating, storing, and distributing the data. Several limitations are also present in our estimate of permafrost heat storage. The primary source of uncertainty in this analysis is the lack of accurate information about the amount and distribution of ground ice in permafrost regions. Additionally, the Tibetan Plateau, the Alpine regions and the southern hemisphere are not included in the analysis, thus the heat storage by melting of ground ice is probably slightly larger than the values presented here.
Expanding the number of estimates of ground heat flux has a high priority, as the ground heat storage is the largest term of the continental heat storage. New measurements of subsurface temperature profiles in areas not well represented in the current global database, regions such as northern and central Africa, South America, the Middle East, and southeastern Asia, are important to improve the spatial coverage of the current subsurface temperature dataset. Furthermore, strengthening the global network of subsurface profiles by repeating measurements at previously measured sites, would reduce uncertainties for the warming of the continental subsurface in the last decades. Flux estimates from other datasets can be also considered, such as from FluxNet towers and from satellite remote sensing data.