dataesr/scientific-paragraphs-categorization huggingface dataset:
The transparency of algorithms, native in the open-source community, is a recent need both of the industry and of the public institutions which request a certain level of ability to explain automated decision-making. Structuring the open data offer goes through investment in digital cultural heritage, which is strongly linked to open source, open content, openness and free licenses that encourage reusing and dissemination by a more involved public. These recent observations, repeated in the NEMO's recommendations for increasing the success of museums during and after a major crisis, yield a diversification of the services provided. This goes through an international collaboration through open and linked data. In a global context, such a transition, already adopted before the storm hit by some museums and governments, needs to adopt open data and free licenses in order to allow the public to be part of preserving the (digital) cultural heritage, while disseminating it as their own.
For our purposes here, we can say that a genospecies is a discrete cluster in the sequence space of core genes  [29]. There have been some broad-scale initiatives to establish taxonomic databases for all bacteria and archaea based on genome sequences. The Type Strain Genome Server (TYGS) includes only type strains, but provides tools to compare any query genome with the most relevant type strains using a whole-genome similarity metric called dDDH, which is designed to correlate well with the laboratory measurements of DDH that were used in the past  [30]. On the other hand, the Genome Taxonomy Database (GTDB) uses a 95% ANI threshold to define species and aims to include and classify all available genomes  [20] , including many that do not fit into species that have been formally described.
The authors called these gsF-1 and gsF-2, and the second of these included strain FB206T, the type strain of R. laguerreae, a species that was described in 2014  [51]. This description was based on six isolates that were distinct from USDA 2370 T , the type strain of R. leguminosarum, in housekeeping gene sequences and by DNA-DNA hybridization, even though their 16S rRNA sequences were all identical to that of USDA 2370 T. As measured by ANI, strains of gsB and gsC are just as divergent from USDA 2370 T as are those of R. laguerreae  [50] , so it seems likely that these genospecies could also be described formally as distinct species using conventional taxonomic criteria. The type strain USDA 2370 T is in gsE, so this genospecies remains R. leguminosarum in the narrow sense. Recently, several published  [21, 50, 52]  and unpublished  [53]  studies have augmented the public databases with a large number of new genome sequences assigned to, or related to, R.
The Set of Genome Sequences All genome sequences for the genus Rhizobium (NCBI:txid379) were downloaded in FASTA format from the NCBI database (www. ncbi. nlm. nih. gov) using the script gen-bank_get_genomes_by_taxon. py that is distributed as part of the pyani package (http: //widdowquinn. github. io/pyani/). Initial analysis was based on the 834 genomes available on 25 July 2020 (Table  S2 ). All later analyses were confined to the genomes that belonged to the Rlc or to its sister taxon R. anhuiense, using the genomes that were available on 28 August 2020. After eliminating duplicate sequences of the same strain, there were 429 genomes in the Rlc and 11 in R. anhuiense (440 genomes altogether). These are listed in Table  S1 , together with additional relevant information. Files were renamed to include the strain name as well as the accession name, and also the known genospecies in the case of the strains previously described by Cavassim et al.
0. 1  [70]  on the CIPRES Science Gateway at https://www. phylo. org  [71] , after selecting the optimal model (GTR + FC + I + G4 m) using ModelTest-NG 0. 1. 3 [72]. Nonparametric bootstrapping (allowing up to 1000 replicates) converged after 300 replicates. The trees were displayed using Dendroscope 3. 7. 2 [73]  for initial exploration, FigTree 1. 4. 3 (https://github. com/rambaut/figtree/releases) for Figure  1 , and iTOL  [74]  for other figures. Python scripts are available at https://github. com/jpwyoung/Rlc and the iTOL trees at https://itol. embl. de/shared/rhizobium.
Average Nucleotide Identity (ANI) Pairwise ANI was calculated using fastANI 1. 31  [75]  with the default settings (kmer = 16, fragment length = 3000, minimum shared fraction = 0. 2), and displayed using the Seaborn library with custom Python scripts available at https://github. com/jpwyoung/Rlc. Within the Rlc plus R. anhuiense, the number of sequence fragments per genome ranged from 2189 to 3829, and the number matched in pairwise comparisons ranged from 1640 to 3825.
The Genus Rhizobium and Related Genera All genomes identified as the genus Rhizobium and available at NCBI were downloaded on 25 July 2020 (834 genomes, Table  S2 ). The sequences of 120 core genes were extracted from each genome assembly. The full set of core genes was found in most assemblies, but 37 were incomplete (Table  S3 ). A phylogeny (not shown) based on all assemblies that were complete enough to have at least 100 of the genes showed that GCF_003001755. 1, annotated as R. tropici NFR14, was an outlier. Analysis of this assembly by the TYGS server (https://tygs. dsmz. de/) indicated that it actually belonged to the genus Bradyrhizobium, so this was used as an outgroup to root the tree, together with R. sp. FKL33, a distant member of the Rhizobiaceae according to the Genome Taxonomy Database (GTDB, https://gtdb.
ecogenomic. org). The tree for genomes that had a complete set of 120 core genes is shown in Figure  1. Bradyrhizobium, so this was used as an outgroup to root the tree, together with R. sp. FKL33, a distant member of the Rhizobiaceae according to the Genome Taxonomy Database (GTDB, https://gtdb. ecogenomic. org). The tree for genomes that had a complete set of 120 core genes is shown in Figure  1. Phylogeny, based on 120 core genes, of genomes assigned to the genus Rhizobium by NCBI (NCBI:txid379). Only the 797 genomes that had all 120 genes were included. Genomes that are not currently included in the genus Rhizobium are indicated in the basal clades, collapsed by genus: Pararhizobium, Mycoplana, Agrobacterium, GTDB g__Rhizobium_A  [20] , Pseudorhizobium, Neorhizobium, and Allorhizobium (top to bottom).
The tree was rooted using strain FKL33 (not shown). Many strains in the basal clades of this phylogeny have featured in previous studies that classified them in genera related to, but distinct from, Rhizobium, and this allowed us to identify the main clades as Allorhizobium  [84, 85] , Neorhizobium  [85, 86] , Pseudorhizobium  [87, 88] , Agrobacterium  [89] , Mycoplana  [90] , Pararhizobium  [85] , and an unnamed genus-level clade identified in GTDB as g__Rhizobium_A, which includes the type strains of Rhizobium rhizoryzae and Allorhizobium pseudoryzae. It should be emphasized that the only strains of these genera that are shown in Figure  1  are those that are misclassified as Rhizobium in the NCBI database. The database also includes a much larger number of related strains that are correctly classified.
The Genospecies Are Consistent with Genomic Taxonomy Databases When the genomes of the 18 strains representing the proposed genospecies were submitted to TYGS  [30] , those representing gsC, gsE, gsG, gsI and gsR were correctly identified as R. ruizarguesonis, R. leguminosarum, R. sophorae, "R. indicum" and R. laguerreae, respectively, while the remaining 13 genomes were all described as 'potential new species' (Table  S7 ). Furthermore, the similarity score dDDH(d 4 ), which should be above 70% for conspecific strains, was no greater than 66. 1% for any of the pairwise comparisons among the 18 representative strains, supporting our proposition that they represent 18 distinct genospecies. All of the seven unique strains that did not fall within these genospecies had dDDN(d 4 ) values below 70% with the representative strains of their closest genospecies (Table  S8 ).
Most were significantly below (65. 8% or less), but CCBAU10279 scored 69. 8% (C. I. 66. 8-72. 6%) against FB206, the type strain of R. laguerreae (gsR). This borderline value is consistent with the ANI value of 96. 54 and our decision to leave this strain outside gsR despite this. Overall, then, we can conclude that our proposal to split the Rlc into 18 genospecies and 7 unique strains is completely supported by the dDDH-based species definition used by TYGS. The Genome Taxonomy Database (GTDB) is another resource for genome-based taxonomy that provides a comprehensive genome-based taxonomy of all prokaryotes  [20]. The database includes 287 of the 329 Rlc strains that we have studied, and they are assigned to ten species plus two single isolates. These species designations are consistent with the genospecies we have defined, but with some 'lumping' (Table  1 ), as can be expected because GTDB uses a fixed 95% ANI threshold.
However, a search of GenBank would have revealed that there were already well over a hundred available genomes of the same species (gsC), greatly expanding the known diversity and geographic range of the species. Until recently, taxonomists have tended to consider only a small number of strains when describing a new species, limited by the practicality of obtaining strains and characterizing them in the laboratory. The increasing availability of large numbers of genome sequences gives access to a much richer picture of the extent of variability within each species, potentially leading to more robust and useful species definitions. There have been recent initiatives to harness the power of genome sequences in the service of bacterial taxonomy. Two are particularly noteworthy for their ambitious scope, covering all bacteria and archaea, and for their contrasting philosophies.
The Type Strain Genome Server (TYGS) aims to identify any submitted genome by comparison to the genomes of all published type strains  [30]. Strains that do not fit within the species that have been formally named are simply returned as 'potential new species'. By contrast, the developers of the Genome Taxonomy Database (GTDB) take a more inclusive approach, using a 95% ANI threshold to place all available genomes into species-level groups  [20]. The result is a parallel nomenclature that sometimes diverges from the formal taxonomy but is arguably a better and more consistent reflection of biological reality. Almost twothirds of the species-level taxa in GTDB have no formal name yet. These include, of course, some of the genospecies we have identified in the Rlc. Our analyses suggest that, within the Rlc, the natural boundaries are mostly rather higher than 95% ANI, centered at approximately 96%, so GTDB does not recognize some of the splits we have discussed.
Funding: Funding for genome sequencing and analysis was received from  Innovation Fund Denmark  ( 4105-00007A , led by  S. U. Andersen)  to J. P. W. Y. ; European Community  FP7 'Legumes for the Agriculture of Tomorrow'  ( LEGATO ,  FP7-613551 ) to M. L. and J. P. W. Y. ;  Agence Nationale de la Recherche (GrasP)  to M. L. ; Rural and  Environment Science and Analytical Services (RESAS, Scotland)  and ' Transition paths to sustainable legume based systems in Europe' (TRUE, EC Horizon 2020 , 727973 led by  P. Iannetta ) to M. M. ; Genomia Fund to E. K. J. , RSF  17-76-30016  to A. A. ; RSF  19-16-00081  to E. E. A. ;  VA2I/463AC06  and  CLU-2018-04  to R. R. G. ;  SERB-DST, India  ( YSS/2015/000149 ) to P. R. ; a  University of Otago   Research Grant  to B. J. P. ;  CSIR, India  ( BSC0117 ) and  CSIR-HRDG, India  ( 21(1023)/16/EMR-II ) to A.
Prescribed CO 2 is the primary factor controlling global warming across the ensemble. On average, elements other than CO 2 , such as Miocene paleogeography and ice sheets, raise global mean temperature by ∼2°C, with the spread in warming under a given CO 2 concentration (due to a combination of the spread in imposed boundary conditions and climate feedback strengths) equivalent to ∼1. 2 times a CO 2 doubling. This study uses an ensemble of opportunity: models, boundary conditions, and reference data sets represent the state-of-art for the Miocene, but are inhomogeneous and not ideal for a formal intermodel comparison effort. Acknowledging this caveat, this study is nevertheless the first Miocene multi-model, multi-proxy comparison attempted so far. This study serves to take stock of the current progress toward simulating Miocene warmth while isolating remaining challenges that may be well served by community-led efforts to coordinate modeling and data activities within a common analytical framework.
g. , CO 2 vs. paleogeography). Nevertheless, the informal model intercomparison presented in this paper serves to provide a "multi-model-ensemble of opportunity" view of the spread in model responses to a range of Miocene boundary conditions and CO 2 forcings, within a single model-data comparison framework; an approach that has previously been used in deep-time paleoclimate modeling  (Lunt, Dunkley Jones, et al. , 2012; Lunt, Haywood, et al. , 2012). The results presented in this study serve to synthesize the current range of model-data agreement seen across Miocene modeling efforts to date, highlight robust mechanisms operating across Miocene modeling efforts, and take stock of the current progress toward simulating Miocene warmth while isolating remaining challenges. Our eventual aim is to catalyze a formalized and community-based MioMIP project.
07 W m -2. These simulations were then extended, and the output was archived for this study. COSMOS: COSMOS, with its ECHAM5  (Roeckner et al. , 2003)  atmospheric component and MPIOM  (Marsland et al. , 2003)  oceanic component, has been used for simulating the Late Miocene in  Knorr et al. (2011)  and the Middle Miocene Climate transition in  Knorr and Lohmann (2014). For further details regarding the paleogeography reconstruction and proxy-based reconstruction of the Late Miocene vegetation, refer to  Micheels et al. (2007 Micheels et al. ( , 2011) )  and the references therein. The corresponding boundary conditions are available at PANGAEA  (Knorr et al. , 2019). Further details on ECHAM5/MPIOM are described in  Jungclaus et al. (2006). Furthermore, Middle to Early Miocene simulations have been performed in a COSMOS model configuration  (Huang et al.
When, for a given model setup, a grid point that corresponds with a given terrestrial MAT (SST) site location, as well as its neighboring grid points, are in the ocean (over land) the site is removed from the bias calculation. Note that rather than using the mean reconstructed SST values for the given time interval, ± the assigned data uncertainty, an alternative approach would be to fully encompass the variability in SST estimates seen across the Late Miocene, Middle Miocene, and MCO time intervals and use maximum and minimum recorded values plus uncertainty-as in  Bradshaw et al. (2012 Bradshaw et al. ( , 2015)). Given that the SST reconstructions have varying temporal resolutions, and are not collocated in time, this approach has the potential to mask spatial covariance signals (e. g. , a higher resolution record can capture more of the sample variance even when the two samples have similar means, which in turn could lead to a smaller "bias" derived using the approach outlined above, even though the means are similar).
Global Mean Surface Temperature Estimates Global Mean Surface Temperature (GMST) estimates for the Late Miocene, Middle Miocene, and MCO are calculated using the proxy MAT and SST syntheses compiled (Section 3. 2). We use a method that has been previously employed to provide Eocene GMST estimates. Despite its simplicity, when applied to the same set of Eocene temperature reconstructions  (Hollis et al. , 2019) , this method produces values that are largely consistent with a range of other methods  (Inglis et al. , 2020). The zonal mean temperature (T) profile at sea level is approximated using a simple model:         T cos a b c (1) where coefficients a, b, and c are chosen to minimize the sum of the squared residuals relative to the Miocene surface temperature proxy data (Figure  3 ).
Using clear-sky (cs) radiative fluxes Δ emm T can be decomposed into the contribution due to the greenhouse effect (CO 2 , water vapor, and lapse rate),               Δ ,            Δ Δ Δ lwc emm gg T T T. Similarly Δ alb T can be decomposed into the contribution of clearsky changes due to surface albedo and aerosols,               Δ ,            Δ Δ Δ. swc alb salb T T T There are limitations in this diagnostic approach, particularly when it comes to separating out the changes due to clear-sky and cloud radiative forcing in the presence of sea-ice, and we have not separated the contribution of changes in surface elevation from the greenhouse effect. Nevertheless, it provides insight into the relative contribution of the five components,     ΔT         Δ Δ gg lwc T T      Δ Δ salb swc T T    Δ tran T , based on what was feasible given the output available within our MioMIP data set.
Model-Data Comparison Starting with the Late Miocene, Figures 9 and 10 compare the simulated zonal-mean land and SST profiles for all the experiments targeting the Late Miocene against available proxy data estimates. The experiments are again stratified by CO 2 forcing. Both the terrestrial and SST records indicate a reduced meridional surface temperature gradient (Figures  9  and 10 ). Generally speaking, subtropical to midlatitude warmth tends to fall into better alignment with the proxy data as CO 2 levels increase. For the Northern Hemisphere, the NorESM-L simulation with 10 Ma boundary conditions and 560 ppm of CO 2 appears to provide a reasonable fit from a zonal mean SST perspective (Figure  10 ), but there are however large zonal asymmetries such that midlatitude SSTs are too warm in the Pacific basin, yet too cold in the Atlantic (Figures  13e  and S9 ).
Both the tropical Middle Miocene SST proxies and the tropical Middle Miocene terrestrial proxies indicate that 850 ppm of CO 2 leads to too much tropical warming (Figures  14g  and 14h ). That said, all the simulations with lower CO 2 concentrations generally suffer from cold biases everywhere outside of the tropics (Figures  14  and S11 ), supporting the conclusion that the models are generally failing to capture the weakening of meridional surface temperature gradients and the full extent of Middle Miocene warmth seen in the proxy data (Figure  4 ). This finding holds and intensifies when one narrows the data window to the MCO (Figure  S12 ). polar amplified warming • Strong regional warming is seen in response to prescribed changes in the Antarctic Ice Sheet but the influence beyond the Southern Hemisphere high latitudes is limited • The degree of polar amplified warming and weakening of the meridional temperature gradient increases with prescribed CO 2 forcing, but generally falls short of the reconstructed meridional gradient weakening seen in the proxies • "Hot spots" in which the SST response within a narrow range of CO 2 forcings is particularly variable across models including the eastern equatorial upwelling regions, the subpolar gyres, and the Southern Ocean, and appear to be linked to the spread in cloud albedo and ocean responses in these regions • Simulations with perturbed orbital parameters show important, yet relatively small and regional, surface temperature sensitivity with an increased sensitivity to orbital changes as CO 2 concentrations, or the extent of the Antarctic Ice Sheet, decrease The extent to which the Miocene surface temperatures within the ensemble align with available terrestrial and SST reconstructions have been assessed.
Broadly speaking, the low meridional temperature gradient indicated by the proxies, specifically between the mid-latitudes and tropics, is difficult to reconcile with the simulations. Under Miocene-like CO 2 forcing (the 350-450 ppm experiments), the modeled climates still retain relatively large equator-to-pole temperature gradients, especially in the Northern Hemisphere. The models do not exhibit the estimated 5°C-10℃ warming of the high latitudes with these CO 2 levels. In practice, this means that to produce realistic warming of the northern high-latitudes, the models require higher CO 2. Increasing CO 2 forcing leads to enhanced warming bringing the midlatitudes into better alignments with the data, but this in turn leads to overshooting in the tropics that become too warm. This pattern is surprisingly robust across both the Late Miocene, Middle Miocene, and MCO data sets, as well as the independent terrestrial and ocean data sets.
[2017] ). VI. Finally, simulation should be run with a set of CO 2 concentrations that span the spread of uncertainty within Miocene CO 2 reconstructions. With the "best estimate" CO 2 concentration specified for when modeling groups can only carry out one simulation. While a coordinated Miocene model intercomparison effort is an aspiration within the Miocene community, the analysis of the informal multi-model ensemble presented here has focused only on annual mean surface temperature and albedo responses and there remain several other aspects within this valuable data set for investigation in future study, for example, the hydrological cycle, subsurface ocean temperature, and circulation, differences in seasonality, and so on. We hope that the synthesis presented here will serve as a valuable starting point.
Data Availability Statement The new synthesis of terrestrial MATs generated here for the Middle Miocene can be found in the supporting information of this study (additional excel file Table  S1 ). As described in Section 3 of the study, the Late, Middle and MCO SST estimates where compiled using published data sets, a summary of these data sets with links to their repositories has been complied in the supporting information, together with the average estimates derived from each data set for each time period (additional excel file Table  S2 ). This effort to synthesis available Miocene SST will be continually updated as part of the Miocene Temperature Portal (https://bolin. su. se/data/miocene-temperature-portal) hosted at Stockholm University. These Tables S1 and S2 excel files have been archived in the Zenodo repository https://doi.
org/10. 5281/zenodo. 4568897 together with NetCDF files containing the updated Middle Miocene paleogeography and land surface data sets described in supporting information, and a NetCDF file containing the MioMIP variables used to make all the figures shown. The code used to make the figures is available on GitHub (https://github. com/nburls/ MioMIP1). A description of each model's setup is provided in Section 3 of this study, together with the relevant configuration information and reference to the original modeling study in Tables  1  and 2.
a b s t r a c t Mobile crowdsourcing is being increasingly used by industrial and research communities to build realistic datasets. By leveraging the capabilities of mobile devices, mobile crowdsourcing apps can be used to track participants' activity and to collect insightful reports from the environment (e. g. , air quality, network quality). However, most of existing crowdsourced datasets systematically tag data samples with metadata (e. g. , time and location stamps), which may inevitably lead to user privacy leaks by discarding sensitive information in the wild. This article addresses this critical limitation of the state of the art by proposing a software library that empowers legacy mobile crowdsourcing apps to increase user privacy without compromising the overall quality of the crowdsourced datasets.