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.

This dataset contains the Pointwise Mutual Information (PMI) values for co-occurrence pairs between different mention categories extracted from two distinct clinical datasets: MESINESP2 and the Clinical Case Reports Collection. PMI is a statistical measure used to assess the strength of association between pairs of entities by comparing their observed co-occurrence to the expected frequency under the assumption of independence.

The datasets include PMI values for each co-occurrence pair, derived from the association of professions and clinical concepts, with the aim of identifying potential occupational health risks. By sharing these datasets, we aim to support further research into the relationships between professions and clinical entities, enabling the development of more accurate and targeted occupational health risk models.

There is a separate file for each corpus, and each dataset is provided in CSV format for easy access and analysis. These files include the PMI values for co-occurrence pairs extracted from the respective corpora, making them suitable for further data analysis.

Data Structure:

• MESINESP2: mesinesp2_co-occurrence_pmi.zip
• Clinical case reports: clinical_cases_co-occurrence_pmi.zip

The repository contains a .zip file for each of the corpus, each containing a .csv file with the co-occurrences between the detected professions and clinical entities. The file has the following columns order:

• span_mention_1: Mention string (original): profession
• normalized_entity_1: Controlled vocabulary entry for this term
• mention1_category: Semantic class (i.e., NER label)
• mention1_freq: Absolute frequency of this mention entity 1
• span_mention_2: Mention string (original): entity 2 (disease, symptom, species, etc.)
• normalized_entity_2: Controlled vocabulary entry for this term
• mention2_category: Semantic class (i.e., NER label)
• mention1_freq: Absolute frequency of this mention entity 2
• co-occurrence: Number of co-occurrences
• PMID: PMID value

Notes

This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (PID2020-119266RA-I0/AEI/10.13039/501100011033).

Contact

If you have any questions or suggestions, please contact us at:

- Miguel Rodríguez Ortega (

Additional resources and corpora

If you are interested, you might want to check out these corpora and resources:

• MEDDOPROF (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)
• MESINESP-2 (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)

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.

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.

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)            
               ...

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.

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

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.

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.

Context

This knowledge graph is constructed to aid research in scholarly data analysis. It can serve as a standard benchmark dataset for several tasks, including knowledge graph embedding, link prediction, recommendation systems, and question answering about high quality papers from 20 top computer science conferences.

This has been introduced and used in the PhD thesis Multi-Relational Embedding for Knowledge Graph Representation and Analysis and TPDL'19 paper Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space.

Content

Construction protocol

Scholarly data

From the Microsoft Academic Graph dataset, we extracted high quality computer science papers published in top conferences between 1990 and 2010. The top conference list are based on the CORE ranking A* conferences. The data was cleaned by removing conferences with less than 300 publications and papers with less than 20 citations. The final list includes 20 top conferences: AAAI, AAMAS, ACL, CHI, COLT, DCC, EC, FOCS, ICCV, ICDE, ICDM, ICML, ICSE, IJCAI, NIPS, SIGGRAPH, SIGIR, SIGMOD, UAI, and WWW.

Knowledge graph

The scholarly dataset was converted to a knowledge graph by defining the entities, the relations, and constructing the triples. The knowledge graph can be seen as a labeled multi-digraph between scholarly entities, where the edge labels express there relationships between the nodes. We use 5 intrinsic entity types including Paper, Author, Affiliation, Venue, and Domain. We also use 5 intrinsic relation types between the entities including author_in_affiliation, author_write_paper, paper_in_domain, paper_cite_paper, and paper_in_venue.

Benchmark data splitting

The knowledge graph was split uniformly at random into the training, validation, and test sets. We made sure that all entities and relations in the validation and test sets also appear in the training set so that their embeddings can be learned. We also made sure that there is no data leakage and no redundant triples in these splits, thus, constitute a challenging benchmark for link prediction similar to WN18RR and FB15K-237.

Data content

File format

All files are in tab-separated-values format, compatible with other popular benchmark datasets including WN18RR and FB15K-237. For example, train.txt includes "28674CFA author_in_affiliation 075CFC38", which denotes the author with id 28674CFA works in the affiliation with id 075CFC38. The repo includes these files: - all_entity_info.txt contains id name type of all entities - all_relation_info.txt contains id of all relations - train.txt contains training triples of the form entity_1_id relation_id entity_2_id - valid.txt contains validation triples - test.txt contains test triples

Statistics

Data statistics of the KG20C knowledge graph:

Acknowledgements

For the dataset and semantic query method, please cite: - Hung Nghiep Tran and Atsuhiro Takasu. Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space. In Proceedings of International Conference on Theory and Practice of Digital Libraries (TPDL), 2019.

For the MEI knowledge graph embedding model, please cite: - Hung Nghiep Tran and Atsuhiro Takasu. Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion. In Proceedings of the European Conference on Artificial Intelligence (ECAI), 2020.

For the baseline results and extended semantic query method, please cite: - Hung Nghiep Tran. Multi-Relational Embedding for Knowledge Graph Representation and Analysis. PhD Dissertation, The Graduate University for Advanced Studies, SOKENDAI, Japan, 2020.

For the Microsoft Academic Graph dataset, please cite: - Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June (Paul) Hsu, and Kuansan Wang. An Overview of Microsoft Academic Service (MAS) and Applications. In Proceedings of the International Conference on World Wide Web (WWW), 2015.

Inspiration

We include the baseline results for two tasks on ...

The graph displays distracted drivers involved in fatal crashes in the United States during 2023, categorized by distraction type. The x-axis represents the types of distractions, while the y-axis shows the number of fatal crashes for each type. The data reveals that the most common distraction was "lost in thought" or daydreaming, with 2,087 fatal crashes, far exceeding other categories. Cellphone use was the second highest, contributing to 371 crashes, followed by distractions from outside events at 207. The lowest values include smoking-related distractions (8) and moving objects in vehicles, such as pets or insects (12). The data highlights the significant impact of cognitive and device-related distractions on road safety.

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.

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 Tampa-St. Petersburg-Clearwater, FL (CBSA) (APUS35D7471A) from Jan 2018 to Sep 2025 about Tampa, energy, gas, FL, urban, retail, price, and USA.

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).

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.

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 Fiji from 2007 to 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 Latvia from 2007 to 2024.