dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome alignment All the CUT&Tag/ACTseq, eSPAN, OKseq, and RNAseq libraries were constructed in a strandspecific way and sequenced using a pairedend method on Illumina platforms. First, adaptor sequences of all raw reads were removed by Cutadapt, and reads of <10 nt were also removed. CUT&Tag/ACTseq, eSPAN, and OKseq data were mapped to yeast (sacCer3) and mouse (mm10) reference genome according to the species using Bowtie2 software  (64). RNAseq data of mouse ES cells were mapped to the mouse (mm10) reference genome using STAR software  (65). Only pairedend reads with both ends mapped correctly were kept for further analysis. Except for repeat element analysis, all multimapped reads were removed using SAMtools (MAPQ < 40) for further analysis  (66). For the CUT&TAG/ACTseq, eSPAN, and OKseq libraries, duplicate reads were removed using Sambamba software  (67).
eSPAN and CUT&Tag/ACT-seq bias calculation The bias of each histone modification eSPAN and CUT&Tag/ACT seq at selected replication origins was computed from trimmed, mapped, unique reads in each bin using the formula Bias = (W -C)/ (W + C), where W and C are the number of reads mapped on Watson and Crick strand in each bin, respectively. The bias value ranges from [-1,1] to quantify the histone segregation equality. Read cov erage in each bin was calculated using the R package of Rsamtools. In mouse ES cells, the bias was calculated in each bin of 5 kb, and the bias in yeast cells was calculated on the basis of nucleosome positions annotated previously  (68). In mouse ES cells, the bias was further smoothed by flanking five bins for visualization. To calculate the relative amounts of parental or new histones at each strand between mutant and WT strains for yeast cells, the fol lowing calculation was applied.
First, the corresponding BrdUIP ssSeq peaks were called to identify replication regions using SICER software  (69). Then, the eSPAN signals were split into left and right based on the identified replication origins. Each replication region can be split into four quadrants: Watson strand at the left (WL) and the Crick strand at the right (CR), which are lagging strands; Crick strand at the left (CL) and the Watson strand at the right (WR), which are leading strands. On each replication quadrant, the eSPAN raw reads were normalized to the total number of mapped reads and the corresponding BrdU signal to quantify the histone enrichment per unit of newly synthesized DNA. Last, the normalized eSPAN signal was used to calculate the relative amount of histone enrich ment between mutant and WT cells as follows: relative amount = (norm:eSPAN mut -norm:eSPAN wt )/norm:eSPAN wt × 100%.
Analysis of gene expression and histone modifications at repeat elements To quantify the reads at repetitive regions of the genome, it is chal lenging due to the mapping uncertainty of those multimapped reads from highly similar sequences in the genome. To allow for the multimapped reads mapping up to 100 locations, RNAseq data of mouse ES cells were mapped to the mouse (mm10) reference genome by STAR software with the parameter " outFilterMultimapNmax 100 winAnchorMultimapNmax 100"  (65). The coding gene anno tation was downloaded from GENCODE, and the repetitive elements annotation was downloaded from University of California, Santa Cruz Table Browser track "RepeatMasker"  (70, 71). To achieve robust re sults for ERV gene expression, we repeated the analysis using two approaches when counting reads.
First, we used TEtranscripts, a specialized tool to assign ambiguously multimapped reads in an expectationmaximization algorithm  (72). Second, we used featureCounts to assign the multimapped reads in a fractional way with parameters "allowMultiOverlap = T, countMultiMappingReads = T, fraction = T"  (73). For both of two approaches, read counts at the coding gene and ERV genes were combined together for analysis of differential gene expression using DESeq2  (74). The differential genes resulted from two independent approaches were quite consistent, and the cutoffs for differ ential genes were false discovery rate < 0. 05 and |fold change| > 1. 5. To analyze the enrichment of a histone modification using the CUT&Tag/ACTseq datasets, we performed differential enrichment analysis similarly to differential expression analysis.
Acknowledgments: We thank  K. Zhao ,  T. Fazzio , and  S. Henikoff  for help on CUT&Tag/ ACT-seq. We thank  S. Goff  for advices on ERV and critical reading of this manuscript. Funding: This work is supported by  NIH  grants  GM R35118015  (to Z. Z. ),  K99GM134180  (to A. S. -C. ), and in part through the  NIH/NCI Cancer Center Support  Grant  P30CA013696  to the  Herbert Irving Comprehensive Cancer Center. This work is also funded by grants from the  Ministry of Science and Technology of China  ( 2019YFA0508900  and  2018YFE0203301 ) and  Natural Science Foundation of China  ( 31521002 ) to R. -M. X. Author contributions:.
(2006)  (19 cM/Mb). Given the low genome-wide variation in recombination rate, this ratio was kept constant for all chromosomes. If available and extractable, information from transcriptomic studies was added to the scheme, excluding studies evaluating <25 genes (Supplementary Table  S1 ). We converted Differentially Expressed Genes (DEGs) reported in these studies in old and new Beebase IDs (hymenopteragenome. org, metazoan. ensembl. org) whenever possible and obtained their location on the honey bee genome from the Ensembl Genome database  (Kersey et al. , 2018)  and the Hymenoptera Genome Database  (Elsik et al. , 2018). This analysis resulted in 159 DEGs found in at least two of the eight studies, of which we could retrieve information on the location on the genome for 118 DEGs. The most striking result from this literature survey is the lack of overlap in the findings across studies (Fig.
) is quite remarkable and demonstrates the strong link between those biological functions and varroa resistance related to VSH and HYG. We performed an enrichment analysis on the 159 DEGs identified in at least two different transcriptomic studies of varroa resistance (using DAVID,  Huang et al. , 2009) , but no molecular function and biological process was found to be significantly enriched, partly due to the relatively low number of DEGs (118 annotated) but also the different tissues that were analysed. However, investigating these 159 DEGs, five encode ion channel proteins (GB49268 -glutamate receptor ionotropic kainate 2, GB50159 -neuronal acetylcholine receptor subunit alpha-10, GB42728 -sodium channel protein paralytic, GB51897 -cacophony, GB50377 -two pore potassium channel protein sup-9) and six function as oxidoreductase (GB49380 -fatty acyl-CoA reductase 1, GB53651 -nitric oxide synthase, GB49878 -probable cytochrome P450 6a14, GB51356cytochrome P450 4G11, GB41212 -laccase-5, GB50627 -putative fatty acyl-CoA reductase).
(2016) obp15 GB46224 VSH Proteomics Antennae Down Hu et al. (2016) obp16 GB46225, GB16826 HYG Proteomics Antennae Up Guarna et al. (2017) obp17 GB46226, GB11092 VSH Proteomics Antennae Up Hu et al. (2016) obp18 GB46227 HYG, VSH Proteomics Antennae Up Hu et al. (2016); Guarna et al. (2017) obp18 GB46227 VSH Proteomics Brain Up Hu et al. (2016) obp18 GB46227 VSH Proteomics Hemolymph Down Hu et al. (2016) obp21 GB46230, GB15460 HYG Microarray Brain Up Gempe et al. (2016) HYG, hygienic behaviour; VSH, Varroa-sensitive hygiene; GRM, grooming; RT-qPCR, real-time quantitative PCR. Accession numbers are from old and new Beebase IDs (hymenopteragenome. org, metazoan. ensembl. org).
The scientific community encourages the use of raw data graphs to improve the reliability and transparency of the results presented in articles. However, the current methods used to visualize raw data are limited to one or two numerical variables per graph and/or small sample sizes. In the behavioral sciences, numerous variables must be plotted together in order to gain insight into the behavior in question. In this article, we present ViSiElse, an R-package offering a new approach in the visualization of raw data. ViSiElse was developed with the open-source software R to visualize behavioral observations over time based on raw time data extracted from visually recorded sessions of experimental observations. ViSiElse gives a global overview of a process by creating a visualization of the timestamps for multiple actions and all participants into a single graph; individual or group behavior can then be easily assessed.
, 2017) , allowing readers to explore the dataset with customizable graphs, and measures of central tendency and errors. These solutions successfully answer the need for reliability and transparency in publications. However, the visualization of raw data is often limited to one or two numerical variables per graph and/or small sample sizes. There are no known models that include an innovative raw time data visualization tool displaying an entire process for large samples of participants in a single graph. ViSiElse is a graphical tool developed to fill the need for such a model and can provide a visualization and global insight of individual and/or group actions over time. ViSiElse was developed with the statistical programing language R (R Core Team, 2018) and is available on the Comprehensive R Archive Network (CRAN) web-site: https://CRAN.
Rproject. org/package=ViSiElse. This program allows for the visualization of raw time data extracted from any experimental observation. The package includes options for additional graphical information of the data tendency (mean, standard deviation, quartiles, or statistical tests) and there are time constraints for each action that check the accuracy of the realized actions. ViSiElse offers a new solution for data reliability and transparency with the visualization of a complete raw dataset in a single graph. We will provide a step-by-step presentation of the main features of ViSiElse, describing how to set up the data to create a ViSiElse plot, how to customize this plot to get a clear view of the individuals' actions, and how to add time limits or statistical measurements. An example using the actions performed on a typical day will provide a simulated dataset to illustrate the use of this program.
ViSiElse is compared with other raw data visualization tools and the various applications and the range of possible uses of ViSiElse are discussed. The Supplemental Material contains the R code needed to reproduce the presented results. Two vignettes (R online documentation) are available for this package; the first describes the ViSiElse process step-by-step using the example of how to make coffee (https://cran. r-project. org/web/packages/ViSiElse/vignettes/ViSiElSe_Step_by_Step. html) and the second follows the example and R script introduced in this article (https://cran. rproject. org/web/packages/ViSiElse/vignettes/ViSiElSe_Paper_Walkthrough. html).
Creation of the dataset and ViSibook objects In order to build the dataset correctly, researchers have to a priori translate a behavior or procedure into a linear process of actions. This process has been described by  Almeida & Azkune (2018)  and demonstrates how behavior is deconstructed into activities and then activities into actions. A list of actions, their type, and their order must be defined in the ViSiElse program. The fundamental question to answer is: "What are the elementary actions comprising the behavior?" The elementary action is defined as an action that cannot be divided into shorter actions with regard to the time scale. For example, a typical set of daily tasks can be divided into the following elementary actions: sleep, wake up, shower, eat breakfast, drink the first coffee of the day, start and stop working, lunch break, pick up the kids, cook and eat dinner, and then go to sleep.
It is not relevant to assign a rank to an action if that action is defined as a punctual action meant only to indicate the start or stop of a long action. However, actions that are not ranked will not be plotted. In our example, the finalized list of actions with their rankings and classifications is: From raw data to datasets Raw data encompasses the elapsed time from the study's starting point to the completion of each punctual action for all participants. In our example of a typical day, the starting point is midnight and each value is the time elapsed between midnight and the completion of each action (in min). The dataset is the organized raw data in which the first column identifies the individuals and the following columns categorize the punctual actions including those that delimit the long actions.
Building the ViSibook While the dataset contains the raw time data of the studied behavior, the ViSibook provides its structure in a table consisting of the characteristics of every action. The minimum structure for a ViSibook requires that each action be named, labeled, defined as being punctual or long, ordered in the process, and long actions must be associated with the name of the two punctual actions delimiting its beginning and its ending. The ViSibook can also include time constraints (green and/or black zones) which allows users to check the time accuracy of the realized actions. To create a ViSibook, users can define or import a table, although the users must take care to import the names and the order of the ViSibook's columns (see ViSiElse CRAN documentation https://cran. csiro.
au/web/ packages/ViSiElse/ViSiElse. pdf). The ViSibook is an optional parameter of the main function, "visielse," which generates a ViSiElse graph of the studied behavior, according to the dataset and the ViSibook. When ViSibook is not specified, "visielse" will compute a default ViSibook from the dataset; when this occurs, the process order is determined by the names of the dataset columns and all actions are automatically determined to be punctual actions. The ViSibook can be extracted from an execution of the "visielse" function at any time and users can modify any information saved in the ViSiBook to adjust the plotted behavior. For example, on the ViSiElse graph, the names of the actions on the Y-axis are the labels from the ViSibook, which by default, are the variable names defined by the dataset column names.
Variable names are typically brief and lack clarity to describe an action, therefore a more explicit description is preferred on the displayed Table  1  First five rows of the typical day dataset. The first column is the subject ID and the following columns are the timestamps of the actions (in minutes). The timestamps are the duration elapse from the starting point to the action. In the typical day dataset, the starting point is midnight ("start_sleep") and, for example, the timestamp of the action "wake up" is the duration between midnight and the waking moment in minutes. Subject 1 woke up at 6 h:06 min so the timestamp is 366 min.
Creating the first plot Running the "visielse" function with a dataset and an optional ViSibook as arguments will create and display the ViSiElse graph. Figure  1  shows a simulated typical day dataset for one hundred participants. Actions on the graph are organized on the Y-axis and their To access and adjust the formatting options for the graph, the ViSiElse object should be plotted using the R basic plot function. There are many options for formatting, including changing the size and color of a label, adding a title, or modifying the time interval size and unit, which is set to 10 s by default. The users can modify the size of the time interval using the plot function with the scal. unit. tps parameter. However, this will only change the breaks in the time axis and not the size of the time interval that is, used to determine the intensity of the color representing the punctual actions, which is calculated by the pixel parameter.
Compare group behavior ViSiElse differentiates between two subsets of participants using color distinctions. The ability to distinguish between experimental groups of participants helps to identify different patterns of behavior. In the example of the typical day dataset, two groups were created: people who employ a babysitter (in blue) and people who do not (in pink). To display groups within ViSiElse, users simply specify the group and the method arguments in the visielse function. The first argument is a vector containing the group distribution for each individual. The second argument is the name of the chosen visualization method. ViSiElse provides three methods with which to plot groups: 1. The cut method where each group is represented one under the other in different colors (see Fig.
3A ). This representation can be used to compare groups as group data are completely graphically dissociated. 2. The join method where groups are spatially mixed but are differentiated by distinct colors (see Fig. 3B ). With this method, users can analyze the group distribution among the data. 3. The within method where all data are plotted together in blue and one of the groups is plotted again in pink (see Fig. 3C ). This visualization allows users to examine a specific group's behavior against the global population. As the ViSiElse package only allows two colors of distinction, this method is the most suitable option for data containing more than two groups.
Limitation of usual raw data visualization tools There are many graphical tools available with which to visualize data; three of those methods were selected for comparison and their characteristics are summarized in Table  2. Scatter plots are commonly used to visualize raw data and are preferred for their ease-of-use with a small number of variables. However, for a highly dimensional dataset, users need to display all variables one by one. For example, Fig. 4A  shows the 12 graphs required to see the dataset for the punctual actions of the typical day. It is difficult to interpret the scatter plots as there is no global overview of the process and the order of the actions is unclear. To analyze behavioral data, all actions should be plotted together. Figure  3  ViSiElse graph with three different methods to plot groups.
For example, Fig. 5  shows the EI process performed by 37 midwives students. The dataset is a subset of the data collected from the SIMULRUN 1 project (POE FEDER Number RE0001879) that investigated the neonatal resuscitation training of midwives via high-fidelity simulation. All participants gave written informed consent to participate in the study. The study was performed according to the guidelines of the Declaration of Helsinki. In the ViSiElse graph (Fig. 5 ), the long action, entitled "intubation duration" allows us to see that midwives performed EI heterogeneously during neonatal resuscitation. Some midwives intubated early in their resuscitation efforts while others started after 4 min elapsed. ILCOR recommendations state that intubation should not occur during the first minute of life.
Online shopping behavior Online shopping behavior is defined as the process in which consumers purchase items over the Internet. Comegys, Hannula & Väisänen (2006)  described this process in a five step model: need recognition, information searches, evaluation, purchase decision and post-purchase behavior. The authors compared online shopping behavior in 2002 and 2004/2005 in two countries (USA and Finland) and discovered that many factors influenced the buying process, including gender, age, education and income  (Jusoh & Ling, 2012; Wu, 2003). ViSiElse enabled the visualization of different groups of behavior and the first four steps of the online shopping behavior model used in  Comegys, Hannula & Väisänen (2006)  are modeled in a ViSiElse example (Fig. 6 ). The dataset is simulated for one hundred consumers divided into groups of 50 women in pink and 50 men in blue, allowing researchers to visually assess the differences in online shopping behavior between different categories of consumers.
The ViSiElse graph also displays the The package includes many features to provide a global overview of the data and can be used in the following ways: 1. Inspection of raw data. 2. Verification of the time adequacy to a procedure. With the inspection of the raw data, users can visualize the data distribution and identify outliers, which is especially useful when performing parametric tests that are sensitive to non-normal data and outliers. ViSiElse helps check statistical assumptions before running parametric tests.
Limitations ViSiElse is currently limited to the visualization of raw data from procedures that can be linearized. Many complex procedures can be divided into processes that can be linearized, however, it is not possible to visualize events that involve multitasking or teamwork at this time. ViSiElse only offers descriptive support of raw data and must be integrated with complementary tools for a complete data analysis. However, ViSiElse is also able to extract patterns from graphs, run complete statistical analyses, and examine the quality of the action's execution. As ViSiElse uses raw time data, it can be associated with software that manages data extraction from video recorded sessions or software that directly provides time data. ViSiElse's visualization on-screen is limited by the pixel pitch; the maximum discrimination capacity for long actions is limited to 725 individuals on a 21.
DISCUSSION ViSiElse is a graphical tool developed to visualize raw data gathered from experimental observations of individual and/or group behavior over time. ViSiElse is a package of the open-source software R that can be applied to visualize any behavioral interactions between an organism and a process. those already used for punctual actions, may be added for long actions, which will remove the restriction on the number of individuals that can be plotted in a single graph. In addition to improving the features of ViSiElse, future work will expand the ranges of data types allowed by creating an option to change the X-axis unit. Users could then visualize any quantitative variable. For example, concentrations or intensity could be visualized under different conditions. This improvement could extend the potential uses of ViSiElse.
CONCLUSION ViSiElse is a package from R, the open-source software for statistical computing and data analysis. ViSiElse transforms a raw time data matrix into a comprehensible graph for immediate insight into the behavior of an individual or group. In a single one-page graph users are able to check their raw data, visualize time accuracy, compare groups, and analyze data distribution using summary statistics and tests. ViSiElse is accessible by ViSibook for the action characteristics (names, labels, types, order, delimitations and green and black zones), through the arguments of the visielse function for analysis features (time scale, groups, statistics), and through the arguments of the plot function for formatting options (labels size and color, adding a title, time interval size and unit).
INTRODUCTION China has a rich cultural heritage and has concentrated on producing digital data under the first wave of digitization. This is also true of the knowledge domain of Chinese ceramic vessels. The domain of Chinese ceramic vessels is rich, yet it lacks knowledge representation models (ontologies) to capture Chinese pottery concepts, express them in Semantic Web compatible interchange formats, and make them shareable and linkable to other data. As there is no ontology in the domain of Chinese ceramics providing the semantics of relevant data, most heritage institutions in China have not yet published cultural heritage data on the Semantic Web. Furthermore, every institution accumulates its data in its own traditional database system rather than linking https://orcid. org/0000-0002-6849-1059 data through an open data policy.