dataesr/scientific-paragraphs-categorization huggingface dataset:
fetus tree where the distribution of hosts has significantly changed. Sequence nucleotide diversity (Π) was calculated with the APE v4. 0 package  51. Phylogenetic trees were visualized using R v3. 2. 1  52  and Microreact 53. i E. c o l i T W 2 T W 8 F R 1 2 F R 1 1 G E R 2 A R 1 3 G E R 1 0 U K 1 6 A R 9 G E R To evaluate the population structure observed in the core phylogeny, we compared it with the output of HierBAPS  11. This approach performs a Bayesian analysis of population structure to cluster similar samples based on their genomic relatedness. We used two clustering layers and 5, 10, 20, and 30 expected numbers of clusters (k) as input parameters. The monophyletic clades seen in the phylogeny totally agreed with the first layer of clustering. The association between the phylogenetic structure and host types was assessed with Befi-BaTS software v0.
1. 1  17. Accounting for confounding effects for time-dating and sampling bias. To account for the confounding effects due to sampling biases of the C. fetus collection we implemented different approaches. First, we built two additional data sets subsampled from the complete data set, one just with European strains to minimize the uneven sampling at distant geographic regions, and other with the same number of strains belonging to each host type to minimize the overrepresentation of bovine and human strains at some phylogenetic clusters. Second, we built another two data sets by filtering out alignment sites likely to be subject to selection: one approach consisted in removing 106 core genes with evidence of negative selection (dN/dS << 1) and the 95 previously identified core genes with evidence of positive selection (dN/dS>1); giving a filtered core genome alignment representing 779 neutrally evolving genes (839,185 aligned positions).
The other data set was generated by calculating position-specific dN/dS values across the core genome alignment using the "kaksCodon" function from the CorMut package  65  , this resulted in masking 9032 positions with evidence of negative (dN/dS << 1) or positive (dN/dS > 1) selection. Over all data sets we first evaluated the presence of time signal by calculating the linear regression between the root-totip distance and the isolation year. Null distributions of R 2 values were generated by 1000 permutations on the actual isolation year using three approaches: (i) random permutations, (ii) clustered permutations as suggested by Murray et al. 54  , and (iii) clustered permutations by inputing the clusters according to BAPS results. Then, we implemented the Bayesian clustered permutation approach also described in Murray et al.
54  using the "rand. xmls. R" R script to generate the randomized inputs to BEAST analyses. To account for confounding effects due to sampling bias we applied the CTMC host state reconstruction analysis in BEAST by performing 10 independent runs with the complete data set and both subsampled data sets (European and host-balanced). Posterior estimates for the bovine and human host states at the MRCA node are displayed as boxplots in Supplementary Fig. 7. Analysis of selective pressures. The aligned nucleotide sequences of each 980 single copy core genes were extracted from Roary's output. The ratio between the number of non-synonymous mutations (Ka) and the number of synonymous mutations (Ks) was calculated for the whole alignment and for the respective subsets of strains belonging to the bovine and human lineages.
The Ka/Ks ratio for each gene alignment was calculated with SeqinR v3. 1. A Ka/Ks > 1 was considered as the threshold for identifying genes under positive selection. To evaluate the strength of the positive pressure acting over the same gene in bovine and non-bovine strains, we used the ranked absolute difference between the individual Ka/Ks ratios calculated over each subset of the corresponding alignments. Accessory genome analyses. The accessory genome was defined as gene sets present in less than 100% of the analyzed genomes. The Jaccard pair-wise distance between accessory gene patterns was calculated with the APE v4. 0 package  51. A discriminant analysis of principal components was used to identify differences in the distribution of accessory genes across the genomes, as implemented in the Adegenet v2.
0. 1 package  55. The identification of virulence genes was performed by comparing the annotated protein sequences of each genome with Blast + blastp  56  against the VFDB database 57 , the Victors database (http://www. phidias. us/victors/) and the PATRIC database of manually curated virulence genes  58. First, genes were considered as present with query coverage or subject coverage ≥80% and identity ≥80%. Then, a second round of blastp was performed as previous but incorporating into the databases the C. fetus genes recovered from the first round, in order to minimize possible false negatives due to sequence divergence. The same approach was used to confirm the presence or absence of accessory genes present in bovine-associated or human-associated lineages. The Cas genes were identified by running hmmsearch  59  (e-value < 1 × 10 -5 ) against the Cas HMM profiles retrieved from Pfam  60.
The same regression analysis was performed using the genomic fluidity values against the number of recombinant bases present on each sample. (ii) To test if the observed accessory gene patterns are not likely to be explained only by the clonal frame (evidence of homoplasy), for each accessory gene we calculated its consistency index (CI) using phangorn  64. CI is defined as minimum number of changes divided by the number of changes required on the tree by maximum parsimony, and is equal to one if there is no homoplasy. We then set an empirical threshold of CI<0. 25 to define genes with evidence of homoplasy. Analysis of human gut metagenomes. Human gastrointestinal metagenome analysis was performed using the Human Pan-Microbe Community database (http://www. hpmcd. org/). Genomes were included in the normalized database generation and scanned using the standard scanning algorithm.
Abundance was scaled by genome uniqueness as described previously  28. Abundance was scaled by read count for sample comparison. Data availability. The assembled genomic data and raw sequences have been deposited in the European Nucleotide Archive under the accession codes provided in Supplementary Data 1. The authors declare that all other data supporting the findings of the study are available in this article and its Supplementary Information files, or from the corresponding authors upon request. provided samples and/or contributed to data analysis interpretation. G. I. , S. C. F. , N. K. and T. D. L. wrote the paper with suggestions from all authors. All authors approved the manuscript prior to submission.
Acknowledgements We acknowledge the  Pathogen Informatics  and Sequencing groups at the  Wellcome Trust Sanger Institute  for technical support. We also thank to  Mark Stares  and  Hilary Browne  at the  Host-Microbiota Interactions Laboratory ,  Wellcome Trust Sanger Institute , for their technical support. G. I. is supported by the  Comisión Sectorial de Investigación Científica  ( CSIC, Uruguay ) and by the  Agencia Nacional de Investigación e Innovación (ANII, Uruguay)  grant  FSSA_X_2014_1_105252. S. C. F. is supported by the  Australian National Health and Medical Research Council  ( 1091097 ) and the  Victorian Government's Operational Infrastructure Support Program. This work received financial support from  Fondo de Convergencia Estructural del Mercosur (FOCEM)  grant  COF 03/ 11 , the  Wellcome Trust  grant number  098051  and the  Medical Research Council UK  grant number  PF451.
a b s t r a c t We introduce the concept of time mask, which is a type of temporal filter suitable for selection of multiple disjoint time intervals in which some query conditions fulfil. Such a filter can be applied to timereferenced objects, such as events and trajectories, for selecting those objects or segments of trajectories that fit in one of the selected time intervals. The selected subsets of objects or segments are dynamically summarized in various ways, and the summaries are represented visually on maps and/or other displays to enable exploration. The time mask filtering can be especially helpful in analysis of disparate data (e. g. , event records, positions of moving objects, and time series of measurements), which may come from different sources. To detect relationships between such data, the analyst may set query conditions on the basis of one dataset and investigate the subsets of objects and values in the other datasets that co-occurred in time with these conditions.
In particular, it may be very useful in joint analysis of several time-referenced datasets for finding relationships between different phenomena. In this paper, we explain and demonstrate the possibilities of using the time mask filter in analysis of spatiotemporal data. We have implemented an interactive visual tool for setting and applying time mask filters. This tool has been used for creating the illustrations in this paper. However, the paper does not aim at presenting this specific tool, which is merely one of many possible implementations. The main goal is to show the analytical opportunities provided by the time mask filter as a generic technique, irrespective of the possible implementations, and to explain the specifics of dealing with the outcomes of filtering. The filter, in general, can select data items from multiple disjoint and irregularly spaced time intervals of differing lengths.
In analysing large datasets covering long time periods, it is unfeasible to consider each interval individually. The possibility of looking at these intervals sequentially using animation is questionable due to the irregularity of the intervals and the gaps between them. The most reasonable way to deal with the selected data items is to create and analyse various aggregates. Furthermore, these aggregates should be dynamic: whenever the filter condition change, the aggregates should be recreated by applying the aggregation operations to the newly selected subset of data items. Hence, the primary contribution of the paper is presentation of the time mask filter as a generic analytical technique used together with dynamic aggregation of filter-selected data. The use of the time mask filter is demonstrated with two examples of analysing complex real-world data related to the air and sea traffic.
Related works Our concept of time mask is similar to the concept of temporal element in temporal databases, which is defined as a finite union of n-dimensional time boxes  (Jensen et al. , 1992). Query languages for temporal databases allow selection of time-referenced data tuples the times in which belong to several disjoint time intervals satisfying some query conditions  (Gadia, 1988). However, interactive tools enabling this kind of querying in the context of visual data exploration have been missing so far, although a variety of interactive querying and filtering techniques have been developed since the pioneering work of  Shneiderman (1994). The book on visual analytics of movement  (Andrienko et al. , 2013a)  describes multiple filter types that are useful in exploration of spatiotemporal data, in particular, movement data, and provides examples of using different filters in combination.
The filter types include the linear temporal filter, several variants of spatial filters, attribute-based filter, filter by direct selection, trajectory segment filter, and cross-filtering between two datasets using references from one of them to items in the other one. These types of filters have been used in the data analyses described further on in this paper. Weaver (2010)  discusses interactive cross-filtering across multiple coordinated displays by direct manipulation in the displays. Aigner et al. (2011)  give a brief overview of a few types of interactive filtering (a. k. a. querying) that can be used in visual analysis of time-referenced data. Apart from linear temporal filtering and attribute-based querying, they mention Time Searcher  (Hochheiser and Shneiderman, 2004) , a tool for interactive selection of time series lines with particular shapes in a time graph.
To visually represent results of interactive filtering in an aggregated form, the concept of ''dynamic aggregators'' has been proposed  (Andrienko et al. , 2008; Rinzivillo et al. , 2008). A dynamic aggregator is a special object having references to a number of data records. It is able to check which records satisfy current filters and to derive certain statistical summaries from these records. The aggregators are responsible for the representation of the summaries on visual displays and for updating the view when the filters change. Generally, aggregation is a common technique used for visualizing large datasets  (Fredrikson et al. , 1999). Based on a hierarchical organization of data in an OLAP (Online Analytical Processing) database, the level of aggregation of visualized data can be dynamically changed as the user zooms in or out in a data display  (Stolte et al.
, 2002 (Stolte et al. , , 2003)). This dynamic behaviour is possible because all meaningful data aggregates at different levels are pre-computed and organized in special data structures called data cubes  (Gray et al. , 1997). However, a full data cube is often too big to fit in the main memory of a computer, while accessing it in an external database may be prohibitive to various interactive operations, particularly, brushing and linking among multiple coordinated views. Recently, visualization researchers have been devising such representations of very large datasets that can fit in the main memory to enable effective visualizations and prompt responses to user's interactive operations. Liu et al. (2013)  decompose a full data cube into sub-cubes with at most four dimensions, which are sufficient for supporting brushing and linking between any pair of one-or twodimensional binned plots (i.
e. , visual displays in which data are aggregated into bins). Furthermore, the decomposed cubes are further segmented into multivariate data tiles. The representation scheme supports parallel processing in response to user's queries. Nanocubes  (Lins et al. , 2013)  is a compact representation of a data cube designed specifically for spatiotemporal data. The memory is saved by maximizing shared links across the data structure. Hashed cubes  (Pahins et al. , 2017)  that appeared recently allow even more compact representation and much simpler implementation. Gaussian Cubes  (Wang et al. , 2017)  extend the idea of data cubes even further: instead of counts and sums, the best multivariate Gaussian models are pre-computed for data subsets. This allows novel ways of visual exploration of large datasets, e.
e. , the selected time intervals. A possible implementation is demonstrated in Fig. 1. The horizontal display dimension is used to represent time. The space along the vertical dimension is divided into rows in which the value variations of different attributes are shown. Qualitative (categorical) attributes can be represented by coloured segmented bars, as in the upper two rows of the display in Fig. 1 ; the colours encode different attribute values. Numeric attributes can be represented by line graphs, as in the lower two rows of the example display. The numeric values are mapped onto vertical positions within the rows, and consecutive positions are connected by lines. The display may include controls for temporal zooming, such as the slider bar visible in the upper image in Fig. 1. Obviously, the time series included in the display may come from different and disparate datasets and data sources.
For data representing trajectories of moving objects, only those trajectory segments are selected that represent movements during the selected time intervals. Data subsets selected by a time mask filter can, in principle, be visually represented by direct depiction of each selected item. However, this approach is not scalable to the size of the data. In practical applications dealing with large datasets, it is necessary to apply various ways of data aggregation: temporal, spatial, and categorical  (Fredrikson et al. , 1999). Multiple examples of aggregation are provided later on in our paper. It is important that the aggregation is not static but dynamically reacts to changes of the time mask filter (as well as other filters), i. e. , the aggregation operations are re-applied to the selected data subsets when the selection changes.
In analyses of complex data, it may be necessary to combine several types of filters  (Andrienko et al. , 2013a). A time mask filter can be combined with all other filter types, including linear and cyclic time filters. Furthermore, a time series display as shown in Fig. 1 ) allows convenient specification of a linear time filter by dragging the mouse horizontally, i. e. , along the temporal dimension of the display. In this way, the user can select a time interval with interesting variation of attribute values for a detailed inspection. As mentioned in the introduction, time mask filtering may be instrumental for detecting and exploring relationships between several datasets. This kind of analysis is performed by applying the following workflow. The analyst defines some conditions of interest based on one dataset (it may also be two or more), creates a time mask, propagates it to the other datasets, and examines the features of the selected data subsets.
DatAcron project The EU-funded project datAcron (http://datacron-project. eu/) aims at advancing the state of the art in methods and technologies for management and analysis of very large amounts of spatiotemporal data, including ''data-at-rest'' (archived data collected in the past) and ''data-in-motion'' (constantly arriving new data). The application areas are air traffic management and maritime traffic surveillance, where there is a need to improve both the overall understanding of how the respective complex systems function and the situational awareness and decision making in the day-today operations. The vision of datAcron is to significantly advance the capacities of systems and humans and thereby promote the safety and effectiveness of critical operations for large numbers of moving objects in large geographical areas.
Data and exploratory task In our example, we use two datasets describing (1) the flights performed fully or partly in the air space of Spain during April 2016 and (2) the regulations that were issued during this period in ECAC (European Civil Aviation Conference) area and affected some of the flights. The data, which were generated by the Spanish ANSP ENAIRE and EUROCONTROL as the network manager, were gathered, prepared, and provided by CRIDA (www. crida. es)-Reference Centre for Research, Development, and Innovation in ATM (air traffic management). The flight data include, for each flight, the flight identifier, the codes of the departure and arrival air ports, and the estimated and actual times of take-off (ETOT and ATOT) and arrival (ETA and ATA). For the regulation-affected flights, the data additionally include the calculates departure and arrival times (CTOT and CTA) prescribed by the regulations, the count of the applied regulations, the identifier of the most penalizing regulation, and the ATFM delay, which is the difference in minutes between CTOT and ETOT.
The delay durations range in this dataset from 0 to 408 min, with 99% of them being in the range from 0 to 60 min. As ATOT may differ from CTOT due to the aforementioned [-5, +10] minutes tolerance window (in particular, ATOT may be earlier than CTOT), the actual delay may differ from the ATFM delay. The flight dataset describes 152,051 flights, of which 25,600 (16. 8%) were regulated, 15,512 (10. 2%) were assigned ATFM delays (CTOT-ETOT) of at least 1 min, and 15,103 of the latter flights (9. 9% of all) were actually delayed by at least 1 min (ATOT-ETOT >= 1 min). Our exploration focuses on these actually delayed flights. The regulation dataset describes 2704 regulations. For each regulation, the data include the start and end times, the reference location, the duration, in minutes, and the regulation reason code.
The reason codes that occurred in the dataset can be seen in the leftmost column of the table in Fig. 2. For a small fraction of the regulations, brief text descriptions are provided. To see the frequency and severity of the regulations, due to the different reasons, we aggregated the data of the delayed flights based on the identifiers of the most penalizing regulations. In Fig. 2 , we see that the most frequent regulation reason is C (ATC capacity deficit), which occurred 633 times, and it is also responsible for the maximal number of delayed flights (6904) and the longest total delay duration (112,168 min = 1869. 47 h = 78 days). The other reasons for the regulations that affected many flights and caused much overall delay were P (special event; most of these regulations Our exploratory task is to understand the spatial and temporal patterns in the creation of regulations, particularly those that are caused by ATC capacity deficits.
Among these intervals, the first quartile, median, and third quartile of the delayed flight counts are 5, 20, and 52, respectively. In the lower part of Fig. 7 , we have applied a query condition that selects the time periods when the numbers of the delayed flights exceeded the third quartile. The filter selects 833 10-minutes intervals, which are marked by yellow background painting. To see the spatial patterns of the flight delays in these intervals, we propagate the time mask filter from the time series display to all time-referenced datasets. The application of the time mask filter to the set of delayed flights selects those flights that were conducted during the selected time intervals. This triggers reaggregation of the flights by the origin and destination areas. Only the selected flights are aggregated.
We have seen so far that most of the peaks of the flight delays due to the ATC capacity deficits (row 1 of the time series display in Fig. 11 ) were caused by regulations in Paris-Nantes (row 2) and western France (row 3); however, some of the peaks emerged when the regulations in these areas were small. We create a time mask filter (Fig. 11 ) to look at the spatial patterns of the regulations and delayed flights at the times of these peaks. Being propagated to all datasets, the filter selects the regulations and the delayed flights that existed during these times. The map on the left of Fig. 13  shows the density of the ''C'' regulations weighted by the number of the affected flights. The main hotspot is around Barcelona; there are also lesser hotspots around Lisbon and on Canary Islands.
The length of the time series is 7482 h, according to the duration of the whole time span of the data. Please note that only 1860 hourly intervals within this time span contain data about vessel positions. Thus, the first row in the display in Fig. 16  shows the time series of the vessel counts by the hourly intervals. Large gaps in the temporal coverage of the data are clearly seen. The second row of the display shows the time series of the counts of the near-location events. We interactively select the time intervals containing at least one near-location event. Our query selects 248 hourly intervals, in which the event counts range from 2 to 71. We propagate the time mask filter resulting from the query to all time-referenced datasets. The filter selects 615 vessel trajectories that existed during the selected time intervals when nearlocation events occurred.