Abstract To break with the traditional model of Basic Statistics classes in Higher Education, we sought on Statistical Literacy and Critical Education to develop an activity about graphic interpretation, which took place in a Virtual Learning Environment (VLE), as a complement to classroom meetings. Twenty-three engineering students from a public higher education institution in Rio de Janeiro took part in the research. Our objective was to analyze elements of graphic comprehension in an activity that consisted of identifying incorrect statistical graphs, conveyed by the media, followed by argumentation and interaction among students about these errors. The main results evidenced that elements of the Graphic Sense were present in the discussions and were the goal of the students' critical analysis. The VLE was responsible for facilitating communication, fostering student participation, and linguistic writing, so the use of digital technologies and activities favored by collaboration and interaction are important for statistical development, but such construction is a gradual process.

Ask Ubuntu temporal network

https://snap.stanford.edu/data/sx-askubuntu.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Ask Ubuntu (http://askubuntu.com/). There are three different types of
interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph sx-askubuntu-a2q) user u commented on user v's question at time t (in the graph
sx-askubuntu-c2q) user u commented on user v's answer at time t (in the
graph sx-askubuntu-c2a)

The graph sx-askubuntu contains the union of these graphs. These graphs
were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-askubuntu)
Nodes 159,316
Temporal Edges 964,437
Edges in static graph 596,933
Time span 2613 days

Dataset statistics (sx-askubuntu-a2q)
Nodes 137,517
Temporal Edges 280,102
Edges in static graph 262,106
Time span 2613 days

Dataset statistics (sx-askubuntu-c2q)
Nodes 79,155
Temporal Edges 327,513
Edges in static graph 198,852
Time span 2047 days

Dataset statistics (sx-askubuntu-c2a)
Nodes 75,555
Temporal Edges 356,822
Edges in static graph 178,210
Time span 2418 days

Source (citation)
Ashwin Paranjape, Austin R. Benson, and Jure Leskovec. "Motifs in Temporal Networks." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017.

Files
File Description
sx-askubuntu.txt.gz All interactions
sx-askubuntu-a2q.txt.gz Answers to questions
sx-askubuntu-c2q.txt.gz Comments to questions
sx-askubuntu-c2a.txt.gz Comments to answers

Data format

SRC DST UNIXTS                             

where edges are separated by a new line and

SRC: id of the source node (a user)                  
TGT: id of the target node (a user)                  
UNIXTS: Unix timestamp (seconds since the epoch)            
               ...

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Statistical table of the number of cases by region, age group, and gender since 2003 (Disease name: Scrub typhus, Date type: Onset date, Case type: Confirmed case, Source of infection: Domestic, Imported).

Graph and download economic data for Consumer Price Index for All Urban Consumers: Gasoline (All Types) in Dallas-Fort Worth-Arlington, TX (CBSA) (CUURA316SETB01) from Feb 1978 to May 2025 about Dallas, gas, urban, consumer, TX, CPI, inflation, price index, indexes, price, and USA.

Usage metrics for all types of scholarly output are one of the measures to assess Open Access impact and are a value added service of Open Access repositories. OpenAIRE has a succesful record in providing usage metrics for for a large number of repositories from around the world. OpenAIRE's Usage Counts service collects usage activity from OpenAIRE content providers, like Insitutional Repositories or national aggreegators like IRUS-UK, or LaReferencia, for usage events related to research products of the OpenAIRE graph, like publications. It subsequently creates and deploys aggregated statistics for these products and delivers standardized activity reports following the COUNTER CoP, about research usage and uptake. It complements existing citation mechanisms and assists institutional repository managers, research communities, research organizations, funders and policy makers track and evaluate research from an early stage. Following its successful record in publications, OpenAIRE's Usage Counts service is ready to be applied to another product of the OpenAIRE reasearch graph, i.e., the research data. The service will monitor and analyze usage activity for research data repositories, as well as usage reports from aggregators like Datacite. This usage will not only be aggregated but also combined with usage activity from publications, by exploiting other OpenAIRE services like the OpenAIRE ScholeXplorer. In this manner OpenAIRE Usage Counts Service will operate as a hub of usage statistics, linking together all kinds of scholarly output, offering a value added service for Open Access and realize the Open Analytics Framework and Infrastructure required for scientific reward in European Open Science Cloud. From the technical perspective, usage data will be collected in two ways: (1) by collecting usage events directly from data repositories and (2) from research data statistics aggregators exposing consolidated statistics via SUSHI-Lite. The final outcome is an OpenAIRE service for tracking, collection, cleaning, analysis, evaluation and COUNTER-compliant reporting of research data combined with other products from OpenAIRE research graph. The poster will describe two aspects: 1) The potential of the OpenAIRE Usage Counts service to explore a number of multidimensional scholarly performance indicators. 2) Contributing as a Usage Counts Hub to services aggregating OpenAIRE Research Graph product-level metrics.

The evolutionary implications and frequency of hybridization and introgression are increasingly being recognized across the tree of life. To detect hybridization from multi-locus and genome-wide sequence data, a popular class of methods is based on summary statistics from subsets of 3 or 4 taxa. However, these methods often carry the assumption of a constant substitution rate across lineages and genes, which is commonly violated in many groups. In this work, we quantify the effects of rate variation on the D test (also known as ABBA-BABA test), the D₃ test, and HyDe. All three tests are used widely across a range of taxonomic groups, in part because they are very fast to compute. We consider rate variation across species lineages, across genes, their lineage-by-gene interaction, and residual variation across gene-tree edges. We do so by simulating gene trees within species networks according to a birth-death-hybridization process so as to capture a range of realistic species phylogenies. For all three methods tested, we found a marked increase in the false discovery of reticulation (type-1 error rate) when there is rate variation across species lineages. The D₃ test was the most sensitive, with around 80% type-1 error, such that D₃ appears to be more sensitive to a departure from the clock than to the presence of reticulation. For all three tests, the power to detect hybridization events decreased as the number of hybridization events increased, indicating that multiple hybridization events can obscure one another if they occur within a small subset of taxa. Our study highlights the need to consider rate variation when using site-based summary statistics and points to the advantages of methods that do not require assumptions on evolutionary rates across lineages or across genes.

This graph shows the sales volume of iced tea in Italy between November 2017 and November 2018, by type of tea. According to data, the sales of peach iced tea reached ** million liters, the most sold type of tea over the period of consideration, followed by lemon iced tea with **** million liters. Green iced tea ranked third with roughly *** million liters sold in 2018.

Statistical table of the number of cases by region, age group, and gender since 2003 (Disease name: Epidemic Typhus, Date Type: Date of diagnosis, Type of case: Confirmed case, Source of infection: Domestic, Imported)

Overview

Data points present in this dataset were obtained following the subsequent steps: To assess the secretion efficiency of the constructs, 96 colonies from the selection plates were evaluated using the workflow presented in Figure Workflow. We picked transformed colonies and cultured in 400 μL TAP medium for 7 days in Deep-well plates (Corning Axygen®, No.: PDW500CS, Thermo Fisher Scientific Inc., Waltham, MA), covered with Breathe-Easy® (Sigma-Aldrich®). Cultivation was performed on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m²s). Then 100 μL sample were transferred clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Fluorescence was measured at excitation 575/9 nm and emission 608/20 nm. Supernatant samples were obtained by spinning Deep-well plates at 3000 × g for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. To compare the constructs, R Statistic version 3.3.3 was used to perform one-way ANOVA (with Tukey's test), and to test statistical hypotheses, the significance level was set at 0.05. Graphs were generated in RStudio v1.0.136. The codes are deposit herein.

Info

ANOVA_Turkey_Sub.R -> code for ANOVA analysis in R statistic 3.3.3

barplot_R.R -> code to generate bar plot in R statistic 3.3.3

boxplotv2.R -> code to generate boxplot in R statistic 3.3.3

pRFU_+_bk.csv -> relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_+_bl.csv -> supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_raw.csv -> supernatant mCherry fluorescence dataset of 96 colonies for each construct.

who_+_bl2.csv -> whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

who_raw.csv -> whole culture mCherry fluorescence dataset of 96 colonies for each construct.

who_+_Chlo.csv -> whole culture chlorophyll fluorescence dataset of 96 colonies for each construct.

Anova_Output_Summary_Guide.pdf -> Explain the ANOVA files content

ANOVA_pRFU_+_bk.doc -> ANOVA of relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_sup_+_bk.doc -> ANOVA of supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_who_+_bk.doc -> ANOVA of whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_Chlo.doc -> ANOVA of whole culture chlorophyll fluorescence of all constructs, plus average and standard deviation values.

Consider citing our work.

Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433

Retail Sales in the United States decreased 0.90 percent in May of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The study examines different graph-based methods of detecting anomalous activities on digital markets, proposing the most efficient way to increase market actors’ protection and reduce information asymmetry. Anomalies are defined below as both bots and fraudulent users (who can be both bots and real people). Methods are compared against each other, and state-of-the-art results from the literature and a new algorithm is proposed. The goal is to find an efficient method suitable for threat detection, both in terms of predictive performance and computational efficiency. It should scale well and remain robust on the advancements of the newest technologies. The article utilized three publicly accessible graph-based datasets: one describing the Twitter social network (TwiBot-20) and two describing Bitcoin cryptocurrency markets (Bitcoin OTC and Bitcoin Alpha). In the former, an anomaly is defined as a bot, as opposed to a human user, whereas in the latter, an anomaly is a user who conducted a fraudulent transaction, which may (but does not have to) imply being a bot. The study proves that graph-based data is a better-performing predictor than text data. It compares different graph algorithms to extract feature sets for anomaly detection models. It states that methods based on nodes’ statistics result in better model performance than state-of-the-art graph embeddings. They also yield a significant improvement in computational efficiency. This often means reducing the time by hours or enabling modeling on significantly larger graphs (usually not feasible in the case of embeddings). On that basis, the article proposes its own graph-based statistics algorithm. Furthermore, using embeddings requires two engineering choices: the type of embedding and its dimension. The research examines whether there are types of graph embeddings and dimensions that perform significantly better than others. The solution turned out to be dataset-specific and needed to be tailored on a case-by-case basis, adding even more engineering overhead to using embeddings (building a leaderboard of grid of embedding instances, where each of them takes hours to be generated). This, again, speaks in favor of the proposed algorithm based on nodes’ statistics. The research proposes its own efficient algorithm, which makes this engineering overhead redundant.

This statistic illustrates consumption, production, prices, and trade of maps and hydrographic or similar charts of all kinds, including atlases, wall maps, topographical plans and globes, printed in Antigua and Barbuda from 2007 to 2021

Statistics illustrates consumption, production, prices, and trade of Maps and hydrographic or similar charts of all kinds, including atlases, wall maps, topographical plans and globes, printed in Haiti from 2007 to 2024.

Snapshot img

Chart Recorder Market Size 2024-2028

The chart recorder market size is forecast to increase by USD 464.5 million at a CAGR of 5.9% between 2023 and 2028. The market is experiencing significant growth due to the increasing demand for multi-channel recording solutions, particularly in sectors like water purification systems where accurate data recording is essential for monitoring system performance. Strip chart recorders and circular chart recorders continue to be popular choices for continuous analog record keeping. However, the emergence of data loggers and automated data acquisition systems has introduced digital file storage as a viable alternative, offering enhanced efficiency and accuracy. Multi-pen recorders offer the advantage of recording multiple data streams on a single chart, which is beneficial in applications such as water quality monitoring. The market is also witnessing the introduction of web-based chart recorders, enabling remote monitoring and real-time data access. Despite these advancements, challenges such as the availability of substitutes and the need for calibration and maintenance persist. Overall, the market is expected to grow as industries, including those involved in water purification, continue to prioritize accurate and efficient data recording solutions.

What will the size of the market be during the forecast period?

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The market for chart recorders, a vital component of data acquisition systems (DAQ), continues to gain traction in various industries, including manufacturing, science, engineering labs, and power plants. These instruments are essential for capturing, recording, and analyzing electrical signals and process parameters such as temperature, pressure, flow, pH, humidity, vibration, movement, diagnostics, and statistical analysis. Chart recorders offer high resolution visualization tools that enable real-time monitoring and analysis of data. Their applications span across numerous sectors, including water purification, where they help monitor turbidity, dissolved oxygen, and sterilization processes. In environmental testing, they assist in tracking the effectiveness of various processes and maintaining optimal conditions. In the manufacturing sector, chart recorders play a crucial role in equipment maintenance and power plant operations.

Moreover, in the context of single-channel and multi-channel applications, chart recorders cater to diverse requirements, offering flexibility and scalability. The demand for chart recorders is driven by the increasing need for accurate and reliable data acquisition and analysis in various industries. As the importance of data-driven decision-making continues to grow, the market for these instruments is expected to expand. Furthermore, the integration of advanced features, such as statistical analysis and diagnostics, enhances their value proposition, making them indispensable tools for various applications.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Digital chart recorders
  Analog chart recorders


Application

  Food and beverage
  Pharmaceuticals
  Industrial applications
  Environmental monitoring


Geography

  North America

    Canada
    US


  APAC

    China
    India
    Japan
    South Korea


  Europe

    Germany
    UK
    France
    Italy


  South America



  Middle East and Africa

By Type Insights

The digital chart recorders segment is estimated to witness significant growth during the forecast period. Digital chart recorders are an essential component of the expanding market, providing sophisticated features and enhanced functionality for modern data recording applications. One notable example is the OMEGA RD8250 by Omega Engineering Inc., which caters to diverse industrial requirements. This advanced digital process recorder boasts dual-function keys and a clear, colored graph display, ensuring a user-friendly experience. The RD8250 offers the flexibility to display real-time data in both digital and trend formats, making it a versatile tool for monitoring temperatures and other vital parameters in dispersed systems, marine installations, and large engines. The front-panel USB port enables seamless data transfer to a PC, enabling efficient analysis and management.

Real-time data can be viewed in both digital and trend formats, ensuring versatility and accuracy in monitoring. The front-panel USB port enables seamless data transfer to a PC via a flash memory card, streamlining data management and analysis. Multi-channel strip chart recorders, circular chart recorders, data loggers, and automated data acquisition systems are other essential components of the digital chart recorder mar

The net job and business growth indicator measures the annual change in both the number of firms and the number of employees between 1978 and 2022. The data is categorized by the size of the firm: those with 1-19 employees, those with between 20 and 499 employees, and those with more than 500 employees.

This data contributes to the big picture of economic conditions in Champaign County. More firms and larger employment numbers are generally positive economic indicators, but any strictly economic indicator should be considered in the context of other factors.

The number of firms and number of employees show very different trends.

Historically, there have been significantly more firms with 1-19 employees than firms in the larger two size categories. The number of firms with 1-19 employees has also been relatively consistent until 2021: there were 95 fewer such firms in 2021 than 1978, and the largest year-to-year change in that 43-year period of analysis was a -3.2% decrease between 1979 and 1980. However, there were 437 fewer such firms in 2022 than 1978. There was a decrease in these firms of 12.5% from 2021 to 2022, the only double-digit year-to-year change and the largest year-to-year change over 44 years.

The larger two size categories have shown an increasing trend over the period of analysis. There were 43 more firms with 20-499 employees in 2022 than 1978, a total increase of 9%. The number of firms with more than 500 employees almost doubled, increasing by 206 firms from 212 in 1978 to 418 in 2022, a total increase of 97.2%.

The trends of employment also vary based on firm size. Firms with 1-19 employees have consistently, and unsurprisingly, accounted for less of the total employment than the larger two categories. Employment in firms with 1-19 employees has also remained relatively consistent over the period of analysis. Employment in firms with more than 500 employees saw an overall trend of growth, interrupted by brief and intermittent decreases, between 1978 and 2022. Employment in the middle category (firms with between 20 and 499 employees) was also greater in 2022 than in 1978.

This data is from the U.S. Census Bureau’s Business Dynamics Statistics Data Tables. This data is at the geographic scale of the Champaign-Urbana Metropolitan Statistical Area (MSA), which is comprised of Champaign and Piatt Counties, or a larger area than the cities or Champaign County.

Source: U.S. Census Bureau; 2022 Business Dynamics Statistics Data Tables; "BDSFSIZE - Business Dynamics Statistics: Firm Size: 1978-2022"; retrieved 21 October 2024.

Statistics illustrates consumption, production, prices, and trade of Maps and hydrographic or similar charts of all kinds, including atlases, wall maps, topographical plans and globes, printed in American Samoa from 2007 to 2024.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Gasoline (All Types) in Chicago-Naperville-Elgin, IL-IN-WI (CBSA) (CUUSA207SETB01) from H1 1984 to H2 2024 about Chicago, IL, gas, IN, WI, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

A skewed exponential power distribution, with parameters defining kurtosis and skewness, is introduced as a way to visualize Type II error in normality tests. By varying these parameters a mosaic of distributions is built, ranging from double exponential to uniform or from positive to negative exponential; the normal distribution is a particular case located in the center of the mosaic. Using a sequential color scheme, a different color is assigned to each distribution in the mosaic depending on the probability of committing a Type II error. This graph gives a visual representation of the power of the performed test. This way of representing results facilitates the comparison of the power of various tests and the influence of sample size. A script to perform this graphical representation, programmed in the R statistical software, is available online as supplementary material.