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 The aim of this work was to analyze the statistical graphs included in the two most frequently series of textbooks used in Costa Rica basic education. We analyze the type of graph, its semiotic complexity, and the data context, as well as the type of task, reading level required to complete the task and purpose of the graph within the task. We observed the predominance of bar graphs, third level of semiotic complexity (representing a distribution), second reading level (reading between the data), work and school context, reading and computation tasks and analysis purpose. We describe the differences in the various grades and between both editorials, as well as differences and coincidences with results of other textbook studies carried out in Spain and Chile.

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

This is the third lab in an Introductory Physical Geography/Environmental Studies course. It introduces students to different data types (qualitative vs quantitative), basic statistical analyses (correlation analysis s, t-test), and graphing techniques.

This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the International Conference on Mobile Software Engineering and Systems (MOBILESoft) in 2015.

Dataset Collection We built a dataset that consists of a random sample of Android app metadata and user reviews available on the Google Play Store on January and March 2014. Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice. The dataset D1 contains available apps in the Google Play Store in January 2014. Then, we created a new snapshot (D2) of the Google Play Store in March 2014.

The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.

For each app, we retrieved the following metadata: name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting, and the set of permission requests.

In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata: title, description, device, and version of the app. None of these fields were mandatory, thus several reviews lack some of these details. From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).

Dataset Stats Some stats about the datasets:

• D1 (Jan. 2014) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.

• D2 (Mar. 2014) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.

Additional stats about the datasets are available here.

Dataset Description To store the dataset, we created a graph database with Neo4j. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g., clusters of apps sharing similar sets of permission requests).

In particular, our dataset graph contains six types of nodes: - APP nodes containing metadata of each app, - PERMISSION nodes describing permission types, - CATEGORY nodes describing app categories, - SUBCATEGORY nodes describing app subcategories, - USER_REVIEW nodes storing user reviews. - TOPIC topics mined from user reviews (using LDA).

Furthermore, there are five types of relationships between APP nodes and each of the remaining nodes:

• USES_PERMISSION relationships between APP and PERMISSION nodes
• HAS_REVIEW between APP and USER_REVIEW nodes
• HAS_TOPIC between USER_REVIEW and TOPIC nodes
• BELONGS_TO_CATEGORY between APP and CATEGORY nodes
• BELONGS_TO_SUBCATEGORY between APP and SUBCATEGORY nodes

Dataset Files Info

Neo4j 2.0 Databases

googlePlayDB1-Jan2014_neo4j_2_0.rar

googlePlayDB2-Mar2014_neo4j_2_0.rar We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with Neo4j 2.0. In particular with the tool version 'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).

Neo4j 3.5 Databases

googlePlayDB1-Jan2014_neo4j_3_5_28.rar

googlePlayDB2-Mar2014_neo4j_3_5_28.rar Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official Neo4j Download Center. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28. The databases can be opened with the tool version: 'Neo4j Community Edition 3.5.28'. The tool can be downloaded from the official Neo4j Donwload page.

  In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the Neo4j Migration Guide.

  First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j

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.

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)            
               ...

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)

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.

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.

Graph and download economic data for Average Price: Gasoline, All Types (Cost per Gallon/3.785 Liters) in Baltimore, MD (CBSA) (APUA3177471A) from Jan 1978 to Dec 1997 about Baltimore, MD, energy, gas, urban, retail, price, and USA.

Water pollution is a major concern among Americans. In a 2025 survey, some ** percent of respondents worried a great deal about pollution in drinking water, while ** percent worried a great deal about pollution of rivers, lakes, and reservoirs.

Historical time series of headline adult (19+) further education and skills learner participation, containing breakdowns by provision type and in some cases level. Also includes some all age apprenticeship participation figures.Academic years: 2005/06 to 2023/24 full academic yearsIndicators: ParticipationFilter: Provision type, Age group, Level

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 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.

Time series data for the statistic Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100) and country Botswana. Indicator Definition:The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.The indicator "Statistical performance indicators (SPI): Pillar 4 data sources score (scale 0-100)" stands at 62.75 as of 12/31/2023. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is 17.97.The 5 year change in percent is 7.93.The Serie's long term average value is 58.13. It's latest available value, on 12/31/2023, is 7.95 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2020, to it's latest available value, on 12/31/2023, is +17.97%.The Serie's change in percent from it's maximum value, on 12/31/2022, to it's latest available value, on 12/31/2023, is 0.0%.

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...

Highly experimental and not yet reliable.

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