dataesr/scientific-paragraphs-categorization huggingface dataset:
The proprietary ICOLD database (International Commission on Large Dams 2020) includes information on nearly 60 000 dams and has the benefit of regular updates, but it also focuses on large dams, not all dams have been georeferenced, and the data are not open-access. Some recent studies have started to combine parts of these different sources towards a specific objective (for example the georeferenced global dam and reservoir dataset (GeoDAR; Wang et al 2021), yet to our knowledge none contains the full suite of available records and attributes. This mix of regional and global data with varied characteristics complicates the decision over which dataset to use in the evaluation of impacts or potential mitigation solutions, and none may be fully adequate for the task. Equally, the complex procedure of combining and cleaning such disparate databases continues to fall on individual endusers, and ultimately leads to duplication of effort while producing a variety of products that are tailored to the individual focus of the users.
All together these and other existing datasets provide an inconsistent picture of the world's dams, though they remain useful for analyses within their specific area of application. Given the described shortcomings, we believe that an open-access one-stop-shop for global dam information will accelerate our ability to understand and analyse the world's distribution of dams, their benefits and dis-benefits. There are millions of small dams and instream barriers around the world  (Smith et al 2002 , Lehner et al 2011 , Belletti et al 2020)  that appear in none of the local or global databases. As a result, there is a need for a more comprehensive and consistent inventory to enable quantification of their cumulative benefits, risks and disbenefits for ecosystems and societies  (Kibler and Tullos 2013 , Fencl et al 2015 , Athayde et al 2019 , Jones et al 2019 , Januchowski-Hartley et al 2020)  globally, as well as for comparison between countries and basins.
Such data have direct implications for several UN sustainable development goals, including those related to infrastructure development (SDG 9), affordable and clean energy (SDG 7), food (SDG 2) and water (SDG 6). Currently, tracking changes in dam construction, operation, and removal is difficult to achieve because of the inconsistency of existing static databases and the difficulty involved in manually monitoring such developments over time. However, we believe that these limitations can be overcome through greater coordination and collaboration, coupled with advances in participatory science, remote sensing, and machine learning that could help in inclusion of smaller infrastructure (see  Whittemore et al 2020). We also see emerging opportunities to represent reservoir dynamics, and to document historical dam construction dates, ownership, hydropower capacity, water withdrawal amounts, and other attributes through growing partnerships and participatory science initiatives fostered through GDW.
The Global Dam Watch initiative The GDW initiative is led by a cooperative network of dam and instream barrier data and tool providers, which works with users across sectors and scales to gather, curate and share high quality, georeferenced data. This initiative will provide: a directory of existing local, regional and global databases; an internationally consistent, free, curated, regularly updated global database of dams and instream barriers; and a suite of tools for analysis of dams and their benefits and impacts. As a first step toward these goals, we are creating a single global database that is scheduled for release in late 2021 which will initially harmonize data from: GRanD  (Lehner et al 2011) , GOODD  (Mulligan et al 2009 (Mulligan et al , 2020b)) , FHReD  (Zarfl et al 2015) , the global river obstruction database (Whittmore et al 2020), and the GSW dataset developed by the Joint Research Centre of the European Commission (JRC)  (Pekel et al 2016).
Furthermore, our GDW knowledge-base is online at www. globaldamwatch. org and provides tools for managing dam data, visualisation of attributes, various analyses and downloadable data at the national and basin scale, with global extent. Going forward, GDW aims to curate and maintain a dynamic global-scale dam and instream barrier database (the GDW database) through a combination of geo-wiki approaches (sensu  Mulligan et al 2009 Mulligan et al , 2020a Mulligan et al , 2020b)) , supervised machine learning techniques and expert systems applied to high and medium resolution satellite imagery, in situ and space-borne altimetry level measurements, and relevant emerging methods or technologies. In addition, through the GDW website (www. globaldamwatch. org), we will share links and information on existing regional and country-level databases of dams and freshwater barriers (the GDW directory).
The GDW directory will organize these existing resources together in a single searchable online directory to facilitate their discovery and recognition. These databases can offer locations of smaller run-ofriver dams, wing dams, partial dams, weirs, locks, as well as other instream structures associated with roads such as culverts, fords, and bridges. Finally, we aim to further develop and provide web-based tools and analyses (the GDW knowledge-base) on dams, their benefits, impacts and environmental challenges. All of these will be supported by enhanced use of earth observation and GIS techniques to map more of the full range of instream structures and their properties.
A GDW invitation The provision of a high quality, globally consistent and current georeferenced global dam database will open significant opportunities for further knowledge generation and applications, as well as more consistent analyses across disciplines and scales. While data contained within version 1 of the GDW database will offer a robust starting point, it will need to evolve over time. Alongside updates which include new dams or dam removals, we envision a broad suite of attributes agreed upon by data developers and users that would increase understanding of the status of dams and potential dam futures. Some of the attributes may include, for example, reservoir extent, level, and storage dynamics, dam purpose, operation rules, construction date, hydropower potential, reservoir evaporative loss, temperature profile, water quality, and sedimentation rates.
From these emerging data, we envision analyses and analytical tools that explore interrelations between dam development and other global changes (e. g. population, land use, and climate change) to better understand shifting water dynamics, provision, and vulnerability as well as impacts on local and downstream aquatic and riparian biodiversity, ecosystem health, and ecosystem services (nature's contributions to people). The contribution of dams to local, national and global food, water and energy security will also be better supported with improved data access. We recognize that these goals can only be achieved through collaborative and inclusive effort. Thus, we invite input and partnerships from industry, governments, research institutions, non-governmental organisations, and policy makers to strengthen GDW's utility and relevance to a broad range of communities, help define database content and knowledge-base tools, provide analytical expertise, better understand domain-specific applications, and generally expand the reach of GDW.
Jones et al (2017)or more than 600 000 dams, weirs, and culverts from government databases. The Water Land and Ecosystems-Mekong Dams Observatory (2018) and Stimson Center's Mekong Dam Monitor (2020) provide free data with locations and attributes for hundreds of dams in the six countries that intersect with the Mekong River basin. Government agencies in Brazil (Agência Nacional de Águas 2018), South Africa (list of registered dams), and the United States (National Inventory of Dams) all maintain publicly available national dam databases, which include thousands (in the case of South Africa) to tens of thousands (in the case of Brazil and the US) of records. Academic contributions includeJones et al (2019)who provide a comprehensive database for Great Britain,Jones et al (2017)for the Volta basin and.
Acknowledgments Discussions towards this paper were supported by a series of meetings funded by  WWF Netherlands , the  WWF Innovation Fund , and the  National Socio-Environmental Synthesis Center (SESYNC)  under funding received from the  National Science Foundation   DBI-1639145. The contribution of  B Belletti  was additionally supported by the  EUR H 2  O' Lyon  ( ANR-17-EURE-0018 ). The contribution of  Mark Mulligan  and  Arnout van Soesbergen  was additionally supported by a  King's College London Together Seed Fund. The contribution of  Stephanie R Januchowski-Hartley  was supported by the Welsh European Funding Office and  European Regional Development Fund  Project No. 80761-SU-140  (West).
Listeria genus comprises two pathogenic species, L. monocytogenes (Lm) and L. ivanovii, and non-pathogenic species. All can thrive as saprophytes, whereas only pathogenic species cause systemic infections. Identifying Listeria species' respective biotopes is critical to understand the ecological contribution of Listeria virulence. In order to investigate the prevalence and abundance of Listeria species in various sources, we retrieved and analyzed 16S rRNA datasets from MG-RAST metagenomic database. 26% of datasets contain Listeria sensu stricto sequences, and Lm is the most prevalent species, most abundant in soil and hostassociated environments, including 5% of human stools. Lm is also detected in 10% of human stool samples from an independent cohort of 900 healthy asymptomatic donors. A specific microbiota signature is associated with Lm faecal carriage, both in humans and experimentally inoculated mice, in which it precedes Lm faecal carriage.
There have been reports of Lm asymptomatic faecal carriage among vertebrates including humans and cattle  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]  , and almost all studies have suggested that the prevalence of Lm carriage is below 1%  18, 19, 23. However, these studies were mostly based on culture-based methods  18, 19, 23  , which are less sensitive than molecular detection methods like PCR and sequencing  20, 21, 24. Only one study suggested Lm faecal carriage to be above 1% by using PCR to detect Lm  20. Large molecular studies on the distribution of Listeria species in mammals and the environment are not available  [25] [26] [27]. In this work, we compare the distribution of pathogenic and non-pathogenic Listeria species in 16S rRNA gene datasets from diverse origins. We show that Lm is more host-associated than non-pathogenic Listeria species, and, in contrast to nonpathogenic Listeria species, is also present in the faeces of healthy humans, both in publicly available 16S rRNA datasets and in an independent cohort of asymptomatic individuals.
Results Listeria monocytogenes is more host-associated than nonpathogenic Listeria species. Ecological sampling is influenced by a priori assumptions about potential niches  29, 30. Here we circumvented this limitation by assessing Listeria species distribution in publicly available metagenomic datasets from the large MG-RAST database  31  , to which high-quality metadata are associated, and retrieved 2490 full metagenomes and 11,907 16S rRNA high-quality datasets (see Methods). We assessed the impact of Listeria pathogenic potential on its ecological distribution by comparing the relative abundance (proportion of a species in a given sample, henceforth expressed as fractions, x-axis, Fig. 1 ) and prevalence (occurrence of a species in samples of a given category, y-axis, Fig. 1 ) of the Listeria pathogenic species Lm and L.
ivanovii to that of the non-pathogenic species L. innocua, L. seeligeri and L. welshimeri  32  , termed together as Listeria sensu stricto (see Methods). Listeria sensu stricto was detected in 26. 06% 16S rRNA datasets (Figs. 1, 2a, b). Note that no positive result could be obtained using our approach (see Materials and Methods) analysing full metagenomes, in line with the relatively low abundance of Listeria species  33  and consistent with a higher sensitivity of 16S rRNA gene sequencing compared to full metagenome sequencing for a given sequencing depth  34. Lm was most frequently present in soil (673/1700, ≥39. 59%; mean relative abundance 1. 2 × 10 -4 ), sludge (70/309, ≥22. 65%), sediment (32/170, ≥18. 82%) and hostassociated samples (854/7695, ≥11. 10%; mean relative abundance 9. 0 × 10 -5 ).
We next investigated the human sampling sites in which Lm was present. As expected for a foodborne pathogen, Lm was detected in faecal samples (108/2238, ≥4. 83%; 108/1397, 7. 73% in samples with discriminatory 16S rRNA sequences), but also in oral samples (7/ 108, ≥6. 48%, Fig. 1c ), consistent with reports that Lm may colonise both the gut and the oral cavity  39, 40. Lm was present in sputum (3/50, ≥6. 00%) and skin samples (2/56, ≥3. 57%) and absent in vaginal samples (0/30), but for these categories, only a few datasets were available for analysis. The non-pathogenic species L. innocua and L. seeligeri were not detected in any human-associated samples, while L. ivanovii, the only other pathogenic Listeria species, was detected, albeit far less than Lm, second most frequently in human stools (Fig.
1c ). Lm faecal carriage is common in humans. We aimed to replicate the result of frequent Lm carriage in humans independently and assessed Lm presence by hly PCR in the stools of a cohort of 900 healthy and a cohort of 125 diarrhoeic individuals (see Methods). It was detected in 10% (90/900) of healthy human stool samples and 20. 8% (26/125) of diarrhoeic stools samples (Fig. 2d  and Supplementary Tables  1  and 2 ). We confirmed that the sequence of the amplicon was that of hly in all of the samples we sequenced (N = 10) (Supplementary Data 1). The enrichment of Lm in gut oral skin sputum vagina -5 -4 -3 -2 -5 -4 -3 -2 -5 -4 -3 -2 -5 -4 -3 -2 -5 -4 -3 -2 Log 10 relative abundance of Listeria spp. diarrhoea samples (χ 2 = 11. 702, P = 0. 0018, Benjamini-Hochberg correction) is consistent with the observation that Lm can induce diarrhoea  41, 42.
The difference in healthy asymptomatic donors in this cohort from France, relative to the 16S rRNA gene datasets from MG-RAST may be due to the different sensitivities of the two methods (targeted hly amplification versus total 16S amplification), and sample selection bias reflecting a potential differential exposure to Lm-contaminated food  31. Neither age nor gender was associated to an asymptomatic carriage (Supplementary Table  2 ). Of note, our assay cannot distinguish between dead and viable Lm and the detection of Lm DNA could therefore correspond to dead Lm. Lm carriage correlates with a specific gut microbiota signature. The gut microbiota is a major line of defence against foodborne pathogens, and several commensals exert a protective effect against enteropathogens  43  , including Lm 44.
Lm also produces bacteriocins that can alter microbiota composition  45, 46. In order to assess if microbiota composition has an impact on Lm faecal carriage in humans and vice versa, we investigated the relative abundance of microbiota taxonomic groups in MG-RAST human faecal samples. To take into account the compositional nature of data of different studies  47  , we calculated the ratios between microbiota phylogenetic groups and Lm abundance in the human microbiome datasets where Lm is present (Fig. 3  and Supplementary Data 2 and 3). Lm abundance correlated with the ratio of abundance of Firmicutes to Bacteroidetes phyla (Fig. 3a left) , consistent with the observation that an increase of this ratio correlates with increased susceptibility to Lm 48. This correlation is not due to Lm itself, as this species was excluded when the relative abundance of Firmicutes was calculated.
Lm faecal carriage depends on gut microbiota. Lm shedding from infected tissues back in the intestinal lumen may favour long-term faecal carriage 6,9,10 , in line with the finding that the most virulent Lm clonal complexes are the most adapted to the mammalian gut  8  , and the present observation that nonpathogenic species are not found in stool datasets retrieved from MG-RAST. To study Lm faecal carriage and its determinants experimentally, we inoculated mice intravenously with 5 × 10 3 CFUs of Lm belonging to the hypervirulent clonal complex-1  8, 55. We observed a cage-dependent faecal carriage in 3/7 cages (11/26 mice). Lm could be detected over 30-days postinoculation, at a time when all mice were asymptomatic. We classified faecal carriage as either heavy (>10 6 CFU/g, six mice in two cages) or light (<10 6 CFU/g, four mice in one cage, together with one noncarrier mouse) (Fig.
Lm inoculation only marginally affected microbiota composition, with only few OTUs (16/710) changes upon Lm inoculation. In sharp contrast, very significant OTUs differences were observed between carriers and non-carriers (685/1660) (Fig. 8b ). The observation that the βdiversity difference observed between heavy and noncarrier microbiota preceded Lm inoculation (Axis 1 in Fig. 8a ) suggests that it plays a causative role in the establishment of Lm carriage. To demonstrate that Lm carriage is indeed driven by the microbiota composition, we aimed to perturb it and assess if it would affect their permissiveness to Lm faecal carriage. As we had shown that a lower microbiota α-diversity is associated with Lm faecal carriage both in mice and human 16S rRNA gene datasets, we treated mice with a broad range antibiotic oral cocktail shown to lower α-diversity  57  and let the microbiota regrow for 4 weeks.
Methods Screening of Listeria sp. in 16S rRNA datasets. A summary of the study workflow is represented in Supplementary Fig. 1. We collected 13,749 16S rRNA amplification datasets from MG-RAST from studies with >5 and <50 samples, for studies containing host samples <250 (last accessed: November 2017) as described in ref. 63. When more samples were available, we randomly selected 50 or 250 samples, respectively. We removed those containing non-ribosomal data or less than 2000 sequences using SSU-align v. 1. 01  64. This left us with a total of 11,907 rRNA datasets (Supplementary Data 4, 5 for a list of all host species analysed in this study). Sequences shorter than 60 bp were removed. 16S rRNA sequence datasets were re-aligned using mafft v. 7. 407  65  , and trimmed using trimal v. 1. 4 66 using the 'automated1' algorithm.
The resulting trimmed sequences were then clustered within each sample at 99% identity and 90% coverage using the uclust algorithm from usearch v. 10. 0. 240  67. Non-redundant reads which were present >3 were considered in the analysis. A representative sequence of each cluster was defined according to the distance to the cluster centroid. Henceforth, we will call these our environmental dataset. To identify Listeria ssp. in the environmental dataset we used a maximum likelihood approach. First, Listeria sensu stricto 16-S rRNA reference sequences (accession numbers X56153, X98527, DQ065846, DQ065845 and X98528) were obtained from Genbank  68  and aligned using mafft with the 'linsi' algorithm. The resulting multiple sequence alignment was trimmed using trimal v. 1. 4. Phylogenetic reconstruction was then performed using IQ-tree v.
Otherwise, they were classified as 'Listeria undefined'. Note that this was the case for sequences with a non-discriminative amplicon region at the species level, e. g. V3-V4. In this work, we focused on all Listeria sensu stricto species, which are frequently found in the environment (Lm, L. ivanovii, L. innocua, L. seeligeri and L. welshimeri). We did not include the closest non-pathogenic relative of Lm, L. marthii since it is only rarely sampled in any environment  71. The remaining non-Listeria representative sequences were used to construct a global catalogue of operational taxonomic units (OTUs). To do so, the representative sequences of all datasets were grouped and clustered together at 97% identity using usearch, and the frequency of each OTU was calculated on each dataset. Finally, OTU representatives were taxonomically classified at the genus level using the RDP classifier  72.
At the same time, we defined the α-diversity of each dataset as the Expected Number of Species (ENS). To do so, we did calculate the Shannon diversity index (H′) (1): H 0 ¼ À ∑ R i¼1 p i ln p i À Á where pi is the relative frequency of a specific species in the dataset (the number of sequences associated with the species divided by the total number of sequences assigned to species), and R is the number of datasets. We calculated the ENS as the   5  Lm faecal carriage in mice is cage-dependent. a CFU/g of the stool of male mice 30 days after an iv challenge with Lm at 5 × 10 3 CFU from different cages (2-6 mice per cage). Colour indicates carriage group (<100 CFU/g: none, 100-10 6 CFU/g: light, >10 6 CFU/g: heavy). Horizontal lines indicate the threshold between the groups. b Body weight change of mice after inoculation at 3 days post-inoculation and 30 days post-inoculation according to their carriage group (n none = 16, n low = 4, n high = 6; 3 dpi: none vs.
exponential of the Shannon diversity (2): ENS ¼ e H 0 Statistical analyses on the 16S rRNA datasets. Given the lack of quantitative data associated with the public datasets, data were treated as compositional. In order to avoid methodological biases when comparing the datasets, only comparisons between ratios were performed. To assess associations between Lm abundance and other taxonomic groups, and given the non-parametric nature of the microbiome data, both Spearman correlation and Wilcoxon rank test were used to compare between the relative abundance of Lm and the different taxonomic ratios. Benjamini-Hochberg correction was used to avoid type I statistical error. Screening of Listeria sp. in full metagenomes. We followed the approach described in Garcia-Garcera et al. 73. In brief, we retrieved around 3000 metagenomes (Supplementary Data 6) from MG-RAST.
75% were excluded. Reads were grouped in sequence variants by unoise3  80  and chimeric sequences were removed. Sequence variants were classified with RDP  79  , which uses VSEARCH to match sequences with the reference database  81. Statistical were performed with R and the phyloseq v. 1. 34. 0, vegan and microbiome libraries  82, 83. α-diversity has been calculated by the number of observed species, abundance-based coverage estimator (ACE)  84  and Shannon index  85. β-diversity between samples has been calculated with MDS of Bray-Curtis dissimilarities  86. PERMANOVA and homogeneity between microbiome groups were calculated with Adonis and Betadispers from the vegan library  87. Differentially present OTUs between groups were identified with DESeq2 v. 1. 30. 1  88. To identify OTUs that differ between pre-inoculation and 30 dpi, the '~mouse_id + timepoint' design was used.
Lm is more prevalent in host-associated environments than non-pathogenic Listeria species. Relative abundance and prevalence of Listeria sensu stricto species in 16S rRNA gene datasets in a different environments, b in selected host datasets (i. e. farm animals and/or known Lm reservoirs which were present >100x in our datasets) for which metadata detailing the host species were available and c from different sampling sites of healthy human hosts for which detailed metadata on body sampling site were available. Numbers on the right indicate samples per category.