Abstract This paper presents the results of the statistical graphs’ analysis according to the curricular guidelines and its implementation in eighteen primary education mathematical textbooks in Perú, which correspond to three complete series and are from different editorials. In them, through a content analysis, we analyzed sections where graphs appeared, identifying the type of activity that arises from the graphs involved, the demanded reading level and the semiotic complexity task involved. The textbooks are partially suited to the curricular guidelines regarding the graphs presentation by educational level and the number of activities proposed by the three editorials are similar. The main activity that is required in textbooks is calculating and building. The predominance of bar graphs, a basic reading level and the representation of an univariate data distribution in the graph are observed in this study.

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.

Users can download the data set and static graphs, tables and charts regarding cancers in the United States. Background The United States Cancer Statistics is web-based report created by the Centers for Disease Control and Prevention, in partnership with the National Cancer Institute (NCI) and the North American Association of Central Cancer Registries (NAACCR). The site contains cancer incidence and cancer mortality data. Specific information includes: the top ten cancers, state vs. national comparisons, selected cancers, childhood cancer, cancers grouped by state/ region, cancers gr ouped by race/ ethnicity and brain cancers by tumor type. User Functionality Users can view static graphs, tables and charts, which can be downloaded. Within childhood cancer, users can view by year and by cancer type and age group or by cancer type and racial/ ethnic group. Otherwise, users can view data by female, male or male and female. Users may also download the entire data sets directly. Data Notes The data sources for the cancer incidence data are the CD C's National Program for Cancer Registries (NPCR) and NCI's Surveillance, Epidemiology and End Result (SEER). CDC's National Vital Statistics System (NVSS) collects the data on cancer mortality. Data is available for each year between 1999 and 2007 or for 2003- 2007 combined. The site does not specify when new data becomes available.

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)            
               ...

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

Graph and download economic data for Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in Washington-Arlington-Alexandria, DC-VA-MD-WV (CBSA) (APUS35A7471A) from Jan 1978 to Sep 2025 about DC, Washington, WV, MD, energy, VA, gas, urban, retail, price, and USA.

This graph shows the percentage of French people who have experienced discrimination based on gender, age, origin, skin color, religion, health condition, disability, pregnancy/maternity in France in 2016, distributed by gender and type of discrimination. It appears that more than 23 percent of responding women stated that they have already been discriminated because of their gender compared to 5.5 percent of responding men.

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.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Gasoline (All Types) in Philadelphia-Camden-Wilmington, PA-NJ-DE-MD (CBSA) (CUURA102SETB01) from Dec 1977 to Sep 2025 about DE, Philadelphia, MD, NJ, PA, gas, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Uzbekistan Exports of public-transport type passenger motor vehicles to Kazakhstan was US$23.36 Million during 2024, according to the United Nations COMTRADE database on international trade. Uzbekistan Exports of public-transport type passenger motor vehicles to Kazakhstan - data, historical chart and statistics - was last updated on December of 2025.

Graph and download economic data for Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in Washington, DC-MD-VA (CBSA) (APUA3157471A) from Jan 1978 to Dec 1997 about DC, Washington, MD, energy, VA, gas, urban, retail, price, and USA.

Graph and download economic data for Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in San Francisco-Oakland-Hayward, CA (CBSA) (APUS49B7471A) from Jan 1978 to Sep 2025 about San Francisco, energy, gas, urban, CA, retail, price, and USA.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Gasoline (All Types) in Size Class A (CUURA000SETB01) from Dec 1986 to Sep 2025 about gas, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

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 Sep 2025 about Dallas, gas, urban, TX, consumer, CPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in Urban Hawaii (CBSA) (APUS49F7471A) from Jan 1978 to Sep 2025 about HI, energy, gas, urban, retail, price, and USA.

1. Data Collection LocationThe Changbai Mountain deciduous Korean pine forest comprehensive observation site (central geographic coordinates: 128.0956E, 42.4030N, elevation 784 m) is located in Erdaobaihe Town, Antu County, Yanbian Korean Autonomous Prefecture, Jilin Province.2. Data Collection MethodsThe Changbai Mountain Forest Ecosystem Research Station, following the "Observation Indicators and Standards for Terrestrial Ecosystem Biology," further divides the long-term monitoring plot of 40 m × 40 m into secondary plots of 5 m × 5 m, totaling 64. For convenience and research needs, the monitoring plot is referred to as the Level I plot for tree layer observation, and the secondary plot is referred to as the Level II plot for shrub layer and herbaceous layer observation. - Tree layer observation: Investigate the diameter at breast height, height, and cover of each tree in the Level I plot. - Shrub layer observation: Mechanically sample and conduct long-term observation on 17 fixed Level II plots. Set up a 2 m × 2 m small plot in each selected Level II plot to investigate the height and cover of each shrub (clump). - Herbaceous layer observation: Conducted within the Level II plots selected for shrub layer investigation. Set up a 1 m × 1 m small plot in each selected Level II plot for inter-annual observation of the herbaceous layer. If necessary, all herbaceous plants can be removed for observation to investigate the height and cover of each herbaceous plant (clump) within the small plot.- Epiphyte observation: Investigate the category of epiphytes on each tree in the Level I plot. - Liana observation: Investigate the base diameter and length of lianas within the Level I plot.3. Data ProcessingData processing includes checking and completing original record information, data entry and verification, and data statistical analysis.The specific statistical analysis methods are as follows: - Tree layer: Based on individual tree surveys, statistics are calculated by Level II plot and species: number of individuals, average diameter, average height, and biomass calculated using models (including stem dry weight, branch dry weight, leaf dry weight, fruit (flower) dry weight, bark dry weight, aerial root dry weight, aboveground total dry weight, and underground total dry weight). Based on the results of individual tree surveys by species, statistics are calculated by Level II plot: species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight. - Shrub layer: Based on species surveys by Level II plot, statistics are calculated by plot: number of individual plants (clumps), average height, biomass calculated using models (including branch dry weight, leaf dry weight, aboveground total dry weight, and underground total dry weight), species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight. - Herbaceous layer: Based on species surveys by Level II plot, statistics are calculated by plot: number of individual plants (clumps), average height, aboveground total dry weight, species number, dominant species, average height of dominant species, density, aboveground total dry weight, and underground total dry weight (underground sampling plot 1 m × 1 m × 0.25 m). - Epiphytes: Based on the survey of epiphytes on each tree, statistics are calculated by Level II plot and species: number of individual plants (clumps). - Liana: Based on the survey within the Level I plot, statistics are calculated by Level II plot and species: number of individual plants (clumps), average base diameter, and average height.4. Database CompositionThe data set is stored in Excel format, including eight sheets. Sheet1 is for the composition of tree species in the Changbai Mountain deciduous Korean pine forest, with a total of 269 records, including indicators as shown in Table 2; Sheet2 is for the composition of shrub species, with a total of 66 records, including indicators as shown in Table 3; Sheet3 is for the composition of herbaceous species, with a total of 193 records, including indicators as shown in Table 4; Sheet4 is for the community characteristics of the tree layer, with a total of 118 records, including indicators as shown in Table 5; Sheet5 is for the community characteristics of the shrub layer, with a total of 32 records, including indicators as shown in Table 6; Sheet6 is for the community characteristics of the herbaceous layer, with a total of 32 records, including indicators as shown in Table 7; Sheet7 is for the species composition of epiphytes, with a total of 130 records, including indicators as shown in Table 8; Sheet8 is for the composition of liana species, with a total of 65 records, including indicators as shown in Table 9.5. Data Quality Control and AssessmentThe quality control of this data set follows the relevant monitoring specifications of the "Observation Indicators and Standards for Terrestrial Ecosystem Biology," with field surveys conducted by technicians with rich experience and professional skills, and the survey data is reviewed and verified by scientific researchers to ensure the scientific and accurate nature of the data.Specific measures are as follows: - During field surveys: The observation time for the species composition and community characteristics of the Changbai Mountain deciduous Korean pine forest is mid-August (the peak of plant growth). Standardized measurement tools and methods are used for data collection, such as using the same model of measuring instruments to measure tree diameter, plant height, and liana base diameter to reduce measurement errors. Plant species identification, common names, and scientific names are based on the Plant Smart database. For plant species that cannot be determined on-site, photos should be taken and specimens collected for indoor analysis and identification. Field survey data records are checked by both the investigator and the recorder to ensure the accuracy of the data. - Data entry: Paper data is transformed into electronic data, with one person entering and another verifying to ensure the accuracy of the data entry. - Quality control and assessment: Quality control methods include threshold checks (comparing monitoring data with historical data over the years, verifying data that exceeds the historical data threshold range, deleting outliers or marking explanations), consistency checks (such as different order of magnitude compared to other measurement values), etc. Quality assessment is carried out by plotting dynamic graphs based on annual or seasonal units and comparing data from the same period.

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).

Graphs are a representative type of fundamental data structures. They are capable of representing complex association relationships in diverse domains. For large-scale graph processing, the stream graphs have become efficient tools to process dynamically evolving graph data. When processing stream graphs, the subgraph counting problem is a key technique, which faces significant computational challenges due to its #P-complete nature. This work introduces StreamSC, a novel framework that efficiently estimate subgraph counting results on stream graphs through two key innovations: (i) It’s the first learning-based framework to address the subgraph counting problem focused on stream graphs; and (ii) this framework addresses the challenges from dynamic changes of the data graph caused by the insertion or deletion of edges. Experiments on 5 real-word graphs show the priority of StreamSC on accuracy and efficiency.

Graph and download economic data for Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in Pittsburgh, PA (CBSA) (APUA1047471A) from Jan 1978 to Dec 1997 about Pittsburgh, energy, PA, gas, urban, retail, price, and USA.

Graph and download economic data for Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in Los Angeles-Long Beach-Anaheim, CA (CBSA) (APUS49A7471A) from Jan 1978 to Sep 2025 about Los Angeles, energy, gas, urban, CA, retail, price, and USA.