dataesr/scientific-paragraphs-categorization huggingface dataset:
TLT microbial diversity is modulated by environmental stressors. A total of 2,798,737 good quality 16 S rRNA gene sequences were obtained. The number of total reads, mapped reads and selected reads, as well as most microbial diversity estimates (Richness, Shannon and Phylogenetic diversity (PD) indices) differed quite markedly among sites (Supplementary information Table  2 ). Based on the number of observed OTUs, the highest bacterial richness was observed between neutral and slightly acidic sites 6 and 7 (pH 6. 2 and 5. 9, respectively), at around 4,000 meters above sea level (m a. s. l. ) along the TLT (Supplementary information Table  2 ). These same sites presented the highest microbial diversity values (Shannon and PD indices). In total, 4,437 OTUs were identified along the three replicates of the eight sites when mapped to Greengenes database  21.
01, after Bonferroni correction) to the microbiomes of samples from Section 1 (sites 1 to 4), while C, N (including total N, NO 3 and NH 4 ), P, Fe and Zn were more related (p < 0. 01) to microbiomes of samples from Section 2 (sites 5 to 8). TLT co-occurrence microbial networks from Section 1 and Section 2 are dissimilar. Inference of microbial ecological interactions was obtained from patterns of occurrence of distinctive 16 S rRNA sequences, physicochemical parameters and soil nutrient content. Co-occurrence networks were constructed using CoNet algorithm, as described previously  22, 23  (Supplementary information Fig. 5 ). The network of OTUs from Section 1 (Supplementary Dataset Table  4 ) contained 484 nodes (476 OTUs and 8 variables) connected by 3,404 edges (2,460 co-presences and 944 exclusions), while for Section 2 (Supplementary Dataset Table  5 ), the network contained 597 nodes (591 OTUs and 6 variables) and 3,270 edges (2,716 co-presences and 554 exclusions).
3 ). The most connected variables were C (9 edges), Cu (7 edges) and P (7 edges) within Section 1 network, while in the network from Section 2, pH (8 edges) and S (7 edges) where the most connected. Taxonomic and phylogenetic relationships between the networks were visualized with the Interactive tree of life (iTOL) (Fig. 4 ). OTUs from Section 1 network were predominantly Actinobacteria (42. 2%), with a smaller representation of Proteobacteria (27. 7%) (Supplementary Dataset Table  4 ). In contrast, OTUs from the Section 2 network predominantly belonged to Proteobacteria (34. 0%), while Actinobacteria (26. 9%) was the second most abundant phyla (Supplementary Dataset Table  5 ). Detailed analysis of OTU's attributes, such as centrality in the network and relative abundance, showed the existence of phylogenetic clusters of OTUs that accounted for some of the differences between the networks (Fig.
4 , Supplementary Dataset Table  4 ; Supplementary Dataset Table  5 ). For instance, the phylum that had the highest density of core OTUs in the network from Section 1 was represented by Acidobacteria (57. 8%), while in Section 2 network, the majority of the core OTUs were Actinobacteria (62. 3%). The most abundant OTUs Figure  3. Section 1 and 2 subnetworks. Squared nodes correspond to physicochemical parameters and circle nodes correspond to OTUs. Same color nomenclature for circle nodes and edges from Supplementary information Fig. 5  are used: light blue, Acidobacteria; dark yellow, Actinobacteria; green, Bacteroidetes; light green, Chloroflexi; light yellow, Cyanobacteria; dark green, Firmicutes; pink, Gemmatimonadetes; blue, Nitrospirae; grey, Planctomycetes; purple, Proteobacteria; orange, Verrucomicrobia; red, Armatimonadetes, FBP, TM7 or WPS-2; green edge, positive correlation; red edge, negative correlation.
(>0. 1% relative abundance) in both networks were generally found to belong to the phyla with the highest number of members, and were mostly core rather than noncore OTUs (Fig. 4 ; Supplementary Dataset Table  4 ; Supplementary Dataset Table  5 ). In the case of relative abundances of OTUs that significantly and highly correlated (10% higher correlation values) with the physicochemical and nutritional variables common to both networks (pH, Fe, Cu, Zn and N), we noticed that in Section 1 network, 31 of the OTUs that correlated with one of these variables, also correlated with the other 4 variables, a feature that was never observed in Section 2 network (Supplementary Dataset Table  4 ; Supplementary Dataset Table  5 ). Moreover, from the 31 OTUs with multiple correlations to variables in the network from Section 1, five also presented high centrality values.
Then, sequences <150 bp or with ambiguous base calls were removed. Remaining sequences were filtered using the USEARCH clustering algorithm at 4% sequence divergence to remove chimeras and clusters consisting of only one sequence (i. e. singletons)  44, 45. Finally, sequences were quality filtered with Mothur v. 1. 22. 2  46  with the minimal quality average set to 30. All sequence data used in this study have been deposited in the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI) under the BioProject accession number PRJNA358231. Sequence analysis and taxonomic identification using Greengenes database. Sequences were analyzed with the software Quantitative Insights Into Microbial Ecology (QIIME v1. 8. 0)  47. Briefly, we used QIIME script 'pick_closed_reference_otus.
py' to extract all 16 S rRNA reads from the amplicon data that matched GreenGenes r16S database  21  , release gg_otus_13_08) at 97% of similarity or 3% divergence, with the taxonomy of the resulting Operational Txonomic Units (OTUs) assigned directly from the closest sequence match ("mapped reads"). 5 using QIIME default parameter values (-s 0. 97 -z True-max_accepts 1-max_rejects 8-word_length 8 -minlen 64-usearch61_sort_method abundance). This closed-reference phylotype picking process generated 4,437 OTUs comprising 623,530 reads across all 24 samples. OTUs unassigned or assigned to mitochondria and chloroplast were removed. Singletons were also removed. For analyses, we selected reads that mapped with OTUs that where identified in at least two out of three replicates ("selected reads"), in order to analyze data using representative OTUs from each site so we could make robust comparative analyses between the different bacterial communities.
Microbial diversity and composition and Multivariate analyses. To characterize microbial diversity patterns, we calculated alpha OTU diversity by randomly subsampling (without replacement) each soil sample using the alpha_rarefaction. py script in QIIME. The Shannon and Faith's Phylogenetic Diversity (PD) indices, along with the observed number of OTUs ('richness'), were calculated. Rarefaction curves for each of these metrics were obtained by serial subsampling in increments of 389 sequences and 10 iterations per increment, to a standardized 3,900 sequences per sample. This number represented the second lowest number of curated sequences obtained across our samples. The samples with the lowest number of sequences were excluded from this analysis. To establish the association among sites in terms of the 14 correlated abiotic variables (physicochemical and nutritional), we conducted a Principal Component Analysis (PCA) using the prcomp function of the R package stats version 3.
2. 3 using centered and scaled variables (pH, N total, NO3, NH4, C total, P, S, K, Ca, Na, Mg, Fe, Cu and Zn). To examine compositional changes in the microbial community across the gradient and to establish associations with soil environmental and nutrient variables, OTU abundance data was converted into incidence data (presence/absence) and then we conducted a Canonical Correspondence Analysis (CCA) using R package vegan v2. 3-2  48. Our data consisted of 3,072 OTUs by 8 sample matrix and the same 14 variables used for PCA.
This procedure was conducted via in house python script and using networkx and random library. Topological graph alignment of co-occurrence networks. To examine the extent to which the individual OTU's change the way they interact with other OTUs in the network when inhabiting contrasting pH environments, co-occurrence Section 1 and Section 2 networks were compared via the graph-alignment technique L-GRAAL  34. Originally applied to compare protein-protein interaction networks, this method aligns nodes of two graphs when they share both similar topological properties (i. e. , for each OTU in each network, the graph-let decomposition depicts the number of theoretical motifs in which the given OTU is involved) in their respective graph and similar labeling properties (i. e. , sequence). In the graph alignment analyses, nodes of Section 1 and Section 2 networks are ranked by their relative abundance (in log-scale) and duplicated on two axes to represent the co-occurrence network structure (Blue and Red for Section 1 and Section 2, respectively, Fig.
Acknowledgements The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. This work was supported by  Fondecyt  grants  1151384  to MG,  3170523  to DM and  1160802  to VC, Fondap Grant  15090007 , Basal Grant of the  Center for Mathematical Modeling UMI2807 UCHILE-CNRS  and  CIRIC-INRIA Chile  project. JM was supported by  CONICYT  PhD fellowship 21120313. Funding to FPD and CL was provided by  IEB  (grants  PFB-23 ,  P02-005 ICM ) and Fondecyt grants  1150763  (to CL) and  3150616  (to FPD). SAN acknowledges Financiamiento Basal  FB-0002  for making this collaboration possible. ED, AB, GJ and DE were supported by  GRIOTE Pays de la Loire  funding.
To cover the frequency range of the rotational band, the coherent (15-35 cm -1 ) and standard (50-500 cm -1 ) radiation modes of the synchrotron were used. In order to refine the magnitude and clarify the physical origin of the continuum, spectra of the two major water isotopologues, H2 16 O and H2 18 O, were considered. Recordings at several water vapor pressures were used to check the expected quadratic pressure dependence of the continuum. The continuum was obtained by difference between experimental absorption EXP  and modeled resonance absorption WML  : WML EXP C     . Resonance absorption was modeled as sum of all significant lines of 7 most abundant water isotopologues:     7 1 ) ( ) ( j i j i WML Ф S    , where Ф(v) is Van Vleck -Weisskopf profile with commonly accepted wings cut off at 25 cm -1 and line shape parameters (including line strength Si, center frequency and collisional broadening coefficient) from HITRAN 2016 database  [3].
We used capture-recapture data collected during a five-year period in a large spatially structured population of B. variegata (8,463    individuals captured in 189 breeding patches). We extended the multievent capture-recapture (CR) model developed by Lagrange et al. 25  to examine the effects of two kinds of TI, gravel tracks and paved roads, on the dispersal of this species. First, we examined whether departure rates and dispersal distances differed between age classes. Next, we examined how age might affect an individual's dispersal when faced with TI and tested the following predictions: (1) Molecular studies have reported that gravel tracks and paved roads may limit gene flow in amphibians  26, 27. Therefore, we expected a negative effect of TI on dispersal in the three age classes. (2) Paved roads are wider (resulting in greater clearance of vegetation), have an artificial surface, and support more intense traffic than gravel tracks, so might be expected to result in higher mortality and/or road avoidance behavior in dispersing organisms.
Accurate and up-to-date information on land use and land cover (LULC) is needed to develop policies on reducing soil sealing through increased urbanization as well as to meet climate targets. More detailed information about building function is also required but is currently lacking. To improve these datasets, the national mapping agency of France, Institut de l'Information Géographique et Foréstière (IGN France), has developed a strategy for updating their LULC database on a update cycle every three years and building information on a continuous cycle using web, mobile, and wiki applications. Developed as part of the LandSense project and eventually tapping into the LandSense federated authentication system, this paper outlines the data collection campaigns, the key concepts that have driven the system architecture, and a description of the technologies developed for this solution.
Another component is the development of different services, particularly the change detection and data quality assurance services. The change detection service applies algorithms to satellite imagery in order to automatically detect potential changes in LULC, which can then be verified by citizens. The quality assurance service is tailored towards processing data from citizens and hence uses various techniques for quality checking that can add confidence to the data collected. As mentioned above, one of the key stakeholders in the LandSense citizen observatory is local government. With the adoption of open data policies, local governments have proposed different initiatives to share data and engage in a dialogue with citizens  [9] , collect data for urban planning  [10] , or even define new regulations, e.
g. , in advertisements  [11]. Recent research has also shown that national mapping agencies (NMAs) are starting to engage with citizens and communities by proposing their own platforms to obtain feedback on existing data or to collect new data  [12]. For example, the National Land Survey of Finland has explored the possibility of improving the quality of their authoritative topographic database by proposing a collective map layer for citizen contributions, as outlined in Laakso et al. [13]. By engaging with schools, Bol et al. [14]  showed how the data collection process can be included in the primary school curriculum, as demonstrated in an innovation pilot proposed by Kadaster, the Dutch NMA. The French NMA, which is also a partner in the LandSense project, has proposed a community-and citizen-based tool for collecting feedback from citizens and public bodies such as firemen and local municipalities in order to reduce the time needed between two releases of the data  [15].
However, platforms proposed by NMAs to date have mainly been used for collecting data, with no examples of collaborative mapping. Moreover, they are mainly targeted at public partners, with very few oriented towards citizens, and none of them, to the best of our knowledge, have involved different types of communities. This paper is an attempt to propose a system that will fill this gap. In this paper we focus on the mapping of LULC data and the monitoring of change, both of which are crucial for decision-making. However, LULC data are complex, especially for non-experts. First, there is confusion between land cover and land use  [16]. Secondly, there are different nomenclatures for LULC proposed at the country and European levels, e. g. , Corine Land Cover (CLC)  [17] , the Urban Atlas  [18] , INSPIRE (Infrastructure for Spatial Information in the European Community)  [19] , and CNIG (National Concil of Geographic Information)  [20].
French National Needs for Mapping LULC Data Reducing the increase in soil sealing, i. e. , impervious surfaces due to urbanization, is an essential measure for protecting ecosystems, maintaining biodiversity, and mitigating climate change  [26]. France has ambitious climate targets and these cannot be achieved without better management of the country's soils, particularly those found on agricultural lands. To respond to these targets, local authorities and decision-makers need up-to-date and accurate LULC data. The French NMA has recently started to produce a new LULC database at the regional scale. The nomenclature of LU and LC classes is INSPIRE-compliant and the specifications of the production were defined to take into account the needs of the end user, as expressed by different stakeholders in a consultation process.
An interesting aspect of the LULC product is that it is 'on-demand'. The LULC database relies on a skeleton layer, which has been defined by the main road and rail networks in order to provide the basic structure of the landscape and to ensure the stability of the data. The LULC data are stored in a vector polygon database, where each polygon is classified with both an LU and LC class. The LULC data are produced by using different French NMA products (e. g. , topographic layers, the address layer, the forest layer, etc. ) and by photo-interpretation. The LULC data are updated on a three-year cycle, which corresponds to the production cycle of orthophotos. LULC data were first produced for the Midi-Pyrénées, a sub-area of the Occitanie region in France. For this region, a nomenclature was developed containing 17 LU classes and 14 LC classes.
However, not every combination of LU and LC classes, i. e. , 17 × 14, is possible. The current version was released in 2016 and represents the LULC in 2013. A cyclical update to the LULC database is one of the most important requirements of the end users as this allows for snapshots in time to be specified, which is necessary for defining annual indicators. A number of different issues can be highlighted regarding the LULC nomenclature and the production process. First, as a result of being produced mainly by photo-interpretation, some LU classes are lacking in information. For example, the LU classes Secondary Production (LU2), Tertiary Production (LU3), and Residential (LU5) are grouped into one LU class (LU235) due to the lack of information concerning the main function of the buildings, e.
In this context and based on the LEP, our goal is to propose a framework where participants can make contributions in order to improve and update the LULC database. To achieve this goal, this research aims to adapt and implement the data collection protocol proposed by Mooney et al. [27]  by following the guidelines addressed in Olteanu-Raimond et al. [28]. LULC data are a difficult theme to map because of the complex and detailed nomenclature used to describe LULC. Thus, one of the key questions we asked was: how can this task be made easier for contributors? We came up with a series of ideas for implementation as follows: - Involve different communities and define different roles. It is essential to engage with these different communities and identify their needs and motivations as they may differ between communities.
Scheduling of the Pilot Campaigns Two main campaigns have been planned: the first campaign will run in 2018 followed by a second campaign in 2019. As mentioned earlier, the LULC database is a dated product released for a specific year. The production of the LULC product is subject to two main requirements. First, there must be temporal consistency between the ground truth and the year of production, i. e. , these must be in the same year. Secondly, if a feature is modified in the dataset, it is essential to know whether the modification is due to a change in the real world or whether it is an error in the database that has been corrected. In order to fulfill these requirements, the strategy for the campaign is illustrated in Figure  1. LULC 2013 represents the first version of the product.
During the first campaign, two new products will be produced: a revised LULC 2013, where the errors are corrected, and a new LULC dataset dated 2016, where changes in the real world are captured. For temporal consistency, an orthophoto from 2013 is used for LULC 2013 whereas for LULC 2016, the orthophoto is from 2016. Between the two campaigns, a fusion process will take place in which a data quality assessment will be made, resulting in a new product referred to as LULC 2016 Fusion. This dataset will be used as the starting dataset for the second campaign. The process will be the same as that used for the first campaign: the two products obtained will be LULC 2016 revised and LULC 2019. Overall, the workflow shown in Figure  1    The exception to this process described above is for information on buildings.
g. , areas under construction, abandoned or inactive quarries, etc. ). Each location to visit is characterized by a coordinate and the type of task to be achieved. The second concept is feedback, which represents the data collected by the contributors when they visit a location, while the third involves contributors reporting on the current state of the LULC map compared with the real world (e. g. , a building does not exist anymore, a new construction, etc. ). To facilitate this process, a taxonomy of reports (i. e. , delete a feature, add a feature, modify the geometry of the feature, etc. ) has been defined. Finally, mapping or modifying features consists of adding new features to the database or modifying existing features, which can be both the attributes and/or the geometry. To do this, supplementary information can be used such as the local knowledge of the contributor, reports made by other contributors indicating areas of change, or errors in the database, aerial photographs, etc.
Contributions from the collaborative process aim to improve and update the existing LULC database. There are four types of contributions that correspond to the tasks that contributors can make: -Edit a feature: This deals with changing one aspect of an existing feature. Modifications can be made to the geometry (e. g. , mapping a new feature or splitting an existing one, deleting a feature by dropping it or by fusion with another feature) or changes to the attribute values that are already present in the initial database. -Add new information about the feature: Here the focus is on attribute information. This task consists of adding one or several attribute values that have not been filled in yet in the initial database. -Make a report: This refers to adding markers on the map that highlight a change or an error and choosing an element of the taxonomy that describes the report.
Types of Campaign Contributions can be made in two different types of campaign: opportunistic and guided. The reason for having two campaign types is to cover different contributor motivations and address how the contributions will be made, i. e. , in situ or online. Contributors must have access to mobile devices to make in situ contributions or a computer to make contributions from their workplace, as the online system is mostly targeted at local government partners. Opportunistic campaigns can run at any time and can be placed in the chosen test area, covering all features in the dataset. Hence, there is no guidance towards specific features as all types of task are available to the contributors. Contributors map and validate features in the defined test areas where differences or errors exist between the real world and the database.
For example, a feature can be an LULC polygon corresponding to a combination of an LU value and an LC value in the nomenclature provided or a building polygon in the production database. In contrast, a guided campaign consists of specific tasks chosen by the campaign organizer. In this case, contributors are encouraged to visit locations that correspond to a particular aspect of the landscape. Guided campaigns also run for restricted periods of time and focus on five different types of task, which correspond to specific features that are selected as locations to visit: Reports of any type of change can be made in either the opportunistic or guided campaigns as this reporting option exists in both the mobile and online applications. Finally, we can also run what we refer to as 'flash' campaigns, which are campaigns limited in space and time in order to gather information quickly over a short period of time.