This is the data that was extracted from existing studies for use in this meta-analysis on reading anxiety and reading achievement. This includes 64 studies. Many of the studies had multiple effect sizes. Each row of data represents a effect size (long format).

User ID: Unique identifier for each participant.

Age: Age of the participant.

Gender: Gender of the participant (f for female, m for male).

Favorite Book Genre: The genre of books most enjoyed by the participant (e.g., Fiction, Fantasy, Science).

Weekly Reading Time (hours): Average number of hours spent reading per week.

Mood Impact: Reported impact on mood after reading (Positive, Neutral, Negative).

This data set. It was created by myself as practice for my own data analysis work. in this dataset. It can be used to analyze the connection between people's weekly reading hours and their emotional state.

This catches the old data warts and all. Not production ready.

This is a review of The UK Reading Experience Database.

Dataset Overview: This dataset contains survey responses collected from students in a college located in Satara, Maharashtra, India. The survey was conducted to gather information about students' library usage, reading habits, learning preferences, and other related factors.

Columns: The dataset consists of 29 columns representing different survey questions and responses. The columns include information such as gender, faculty, location, preferred study materials, library visit frequency, average time spent in college, preferred learning language, reading preferences, COVID-19 pandemic impact, book purchasing behavior, parents' occupation and education, and more.

Data Collection: The survey was shared with students in the college library, and their responses were collected using a Google Form. Approximately 10-15k students studying in various courses, ranging from 11th grade to master's degree, participated in the survey.

Data Format: The dataset is provided in CSV format, with each row representing a student's survey response and each column representing a specific survey question.

Data Usage: This dataset can be used to gain insights into students' library usage patterns, reading habits, and learning preferences. It can be used for exploratory data analysis, statistical analysis, and building predictive models related to student behavior, library services, or educational interventions.

Data Quality: The dataset has been cleaned and preprocessed to remove any identifiable personal information and ensure data privacy. However, it is always advisable to handle the data responsibly and in accordance with applicable data protection regulations.

Here's a column-wise description of the dataset:

gender: Gender of the student. faculty: Faculty or department of the student. Enter Your Location: Location of the student. kind of books preferred for study: Preferred type of books for studying. How Frequently do you visit library: Frequency of visiting the library. For what Purposes do you visit library: Purposes for visiting the library. Average Time spent in college: Average time spent in college. What is general Purposes: General purposes of the student. Which one is your Preferred location: Preferred location. What is your preferred time?: Preferred time for activities. Preferred language for Learning: Preferred language for learning. Preferred type for reading: Preferred type of reading material. Do you enjoy the Reading: Enjoyment of reading. Which mode of learning: Preferred mode of learning. Dose Covid Pandemic Ch: Impact of the Covid pandemic on learning. How do you study before collage: Study habits before college. How do you study after Collage: Study habits after college. Do you aware about Nati: Awareness about National Digital Library. Do you Using National di: Usage of National Digital Library. Dose Covid 19 Pandemic Affected Your Reading Habits: Impact of the Covid-19 pandemic on reading habits. Do you purchase Books from store: Book purchasing behavior from physical stores. Average Expenditure on books: Average expenditure on books. Occupation Of Father: Occupation of the student's father. Parents Education: Education level of the student's parents. Select your Faculty: Select faculty or department. Enter your Location: Enter location. Preferred Language for Learning: Preferred language for learning. Do you Using National dig: Usage of National Digital Library. Occupation of Father: Occupation of the student's father.

This study examined levels of students’ reading literacy across countries, as well as the nature of reading instruction and the relationships between reading comprehension and aspects of home and school environment. The data were collected in 1990–1991. Two target populations were included in the study: nine-year-old students and 14-year-old students in 32 countries. The international coordinating center for the Reading Literacy Study established within the Faculty of Education, University of Hamburg , Germany, worked in close cooperation with IEA, and the national centers of participating education systems. RL_II Educational measurements and tests

The PIRLS 2016 International Database is available for individuals interested in the data collected and analyzed as part of PIRLS 2016. The aim is to support and promote the use of these data by researchers, analysts, and others interested in improving education. For the PIRLS 2016 assessment, the database includes student reading achievement data as well as the student, parent, teacher, school, and curricular background data for 50 countries and 11 benchmarking entities. The ePIRLS 2016 International Database includes data from the ePIRLS 2016 assessment, with the participation of 14 countries and 2 benchmarking entities. The student, parent, teacher, and school data files are in SAS and SPSS formats.

The entire database and its supporting documents are described in the PIRLS 2016 User Guide (Foy, 2018) and its three supplements. The data can be analyzed using the downloadable IEA IDB Analyzer (version 4.0), an application developed by IEA Hamburg to facilitate the analysis of the PIRLS data.

A public use version of the datasets is available for download using the links below. A restricted use version of the PIRLS 2016 International Database is available to users who require access to variables removed from the public use version (see Chapter 4 of the User Guide). Users who require access to the restricted use version of the International Database to conduct their analyses should contact the IEA (RandA@iea-hamburg.de).

PIRLS 2006 is the second in a cycle of internationally comparative reading assessments carried out every five years. Conducted at the fourth grade, this world-wide assessment and research project is designed to measure trends in children's reading literacy achievement and collect information about the policy and practices related to learning to read and reading instruction. For example, PIRLS 2006 will provide information on the impact of the home environment on reading achievement and how parents can foster reading literacy. It also will provide extensive information about curriculum and classroom approaches to reading instruction.

Participating countries include Austria, Belgium (Fl.), Belgium (Fr.), Bulgaria, Canada, Chinese Taipei, Denmark, England, France, Georgia, Germany, Hong Kong, Hungary, Iceland, Indonesia, Iran, Israel, Italy, Kuwait, Latvia, Lithuania, Luxembourg, Macedonia, Moldova, Morocco, Netherlands, New Zealand, Norway, Poland, Qatar, Romania, Russian Federation, Scotland, Singapore, Slovak Republic, Slovenia, South Africa, Spain, Sweden, Trinidad and Tobago, and the United States.

The PIRLS 2006 User Guide for the International Database is edited by Pierre Foy and Ann M. Kennedy.

The Excel file contains the filtered data records of the map-reading study of the Research Group on Experimental Cartography at the Eötvös Loránd University (ktk.elte.hu). The data collection started in the autumn of 2015 and lasted until April 2022. The file contains three sheets: demographic_questions; correct_answers; map_reading_database. The first two sheets contain the questions asked, the answer codes, and the correct answers. The third one has 511 records, which is the result of a filtering of the original 805 fills. The filtering excluded the unfinished tests, and the ones with fill time below 2.5 minutes and above 15 minutes.

This database includes demographic data, tactile braille word/letter scores for each subject and testing session, individual grating orientation thresholds and results of the test of visual reading in regular alphabet (number of words read in one minute and percent of correct answers in the comprehension test). (XLSX)

A graphical analysis tool that finds all open reading frames in a user's sequence or in a sequence already in the database.

The data collection comprises three elements that link properties of the words that occur in books suitable for children and young people to the morpheme knowledge that readers display in reading tasks. These three elements include the following:

(a) A lexical database of the words that occur in 1200 books suitable for children and young people aged 7-16. This database comprises over 100,000 words and a range of psycholinguistic properties such as word frequency and contextual diversity. The corpus from which these words were sourced contains over 70 million words.

(b) A computational algorithm that parses these words into morphemes and provides data about their frequency of occurrence. Notably, the parser works on morphemes defined orthographically (as opposed to etymologically) and so captures what a child might learn about morphology through reading experience.

(c) Response time and accuracy data from a large-scale study of human readers that links the corpus-based metrics pertaining to morphemes to reading performance.

Each of these datasets, along with relevant pre-processing and analysis code, is available on the Open Science Framework (OSF). Each of these OSF projects also contains comprehensive documentation to facilitate reuse. Links are available as related resources.

The majority of words in English and in other languages are built by combining smaller units of meaning called morphemes (e.g. clean+ly, un+clean). Understanding how a language's morphology works is vital because it allows us to generalise; for example, we can understand 'misclean' because we know that [mis-] and [clean] function as meaningful elements. Research suggests that morpheme knowledge provides an important heuristic for vocabulary growth, and that this knowledge facilitates rapid reading comprehension in adults.

The aim of this project is to discover how we acquire abstract knowledge of affix morphemes (e.g. -ify, -ly). These units typically do not occur in isolation, so their functions must be inferred through experience with whole words (e.g. purify, falsify, simplify). Recent theories suggest that we learn these units because they provide powerful information about word meaning (e.g. -ify means 'to make [stem]'). However, these theories have been developed largely through laboratory experiments using simple miniature languages. The way that morphemes communicate meaning in real language is far more complex: we do not know what drives learning 'in the wild' or what it is that children are learning.

This project develops two research streams to quantify how children's language experience shapes morpheme knowledge.

Our first research stream offers an unprecedented attempt to quantify the nature of affix information in children's literature. We will build a large-scale children's text corpus including books suitable for ages 7 to 16, and will develop theoretically-driven metrics that capture the nature of affix information. The development of these metrics will capitalise on new computational techniques that permit us to capture how morphemes contribute to meaning in a richer and more nuanced way than has previously been possible. We will calculate these metrics across the whole corpus but also as text accumulates across material suitable for different age bands. This latter analysis is important because the vocabulary used in books across this age range is likely to change considerably (particularly for longer, morphologically-complex words), and this will influence what children can learn about individual affixes.

Our second research stream then probes how the affix regularities uncovered in the first research stream influence morphemic knowledge. This work advances the state-of-the-art because it allows us to move beyond questions of whether or not relatively small groups show morphological effects. Instead, our prediction is that morpheme knowledge should be a function of (a) an individual's linguistic experience; and (b) the statistical regularity with which specific affixes contribute to word meaning. Item-based measures will be derived from the corpus analysis; participant-based measures will be derived from a variety of measures of language and literacy.

The immediate outcome of this work will be a new theory of how language experience underpins the acquisition of morphemic knowledge. This new theory will contribute more broadly to our understanding of how language experience shapes language knowledge, and it will stimulate new thinking about why reading might be a particularly important source of language experience. This project will also produce a new children's text corpus and high-dimensional semantic representations built from that corpus. These products will be made available in user-friendly interfaces to the fullest extent possible to facilitate future research.

The data for this project was meticulously gathered from Goodreads, focusing on the curated list of books that are deemed essential reading. The data collection process was carried out in two distinct phases to ensure comprehensive and accurate capture of all relevant information.

Source:

Goodreads Listing: https://www.goodreads.com/list/show/264.Books_That_Everyone_Should_Read_At_Least_Once

Data Collection Steps:

Book URL Scraping:

• Objective: The primary goal of this step was to extract the URLs of the books listed on the Goodreads page, along with their corresponding titles. This is a crucial preliminary step that allows for subsequent detailed data collection.
• Methodology: I employed a custom-built Python script, scraper\book_url_scraper.py, designed specifically to navigate the Goodreads page and identify each book's URL. The script systematically parses the HTML structure of the listing page, extracts the URLs, and pairs them with the book titles.
• Data Storage: The collected URLs and titles were compiled into a CSV file named book_urls.csv, which is stored in the scraper folder. This CSV file acts as a reference list, containing essential links and titles needed for the next phase of data collection.

Book Details Scraping:

• Objective: This phase aimed to enrich the dataset by collecting detailed descriptions and genre classifications for each book using the URLs obtained in the previous step. This provides a deeper understanding of each book's content and category.
• Methodology: Utilizing the URLs stored in book_urls.csv, I developed and executed another Python script, scraper\book_details_scraper.py. This script accesses each URL, retrieves the book's detailed description, and identifies its genre(s). The process involves parsing the book's page to extract relevant information accurately.
• Data Storage: The extracted descriptions and genres were organized and saved into a CSV file named book_details.csv, located in the data folder. This file contains comprehensive information about each book, including its description and genre, facilitating detailed analysis and research.

Summary:

The data collection effort resulted in the comprehensive gathering of details for 6,313 books. This dataset includes essential information such as book titles, URLs, detailed descriptions, and genres. The structured approach, involving separate scripts for URL extraction and detailed data scraping, ensures that the dataset is both thorough and well-organized. The final dataset, encapsulated in book_details.csv, provides a robust foundation for further exploration, analysis, and insights into the literary works recommended on Goodreads.

In 2014, EFSA has commissioned the compilation of a database specific for the pesticide residues including active substances and their metabolites, which comprises different genotoxicity endpoints, i.e. point mutations, structural and numerical chromosome aberrations, and DNA damage.

Data collection on individual genotoxicity studies has been retrieved from regulatory toxicological reports (Draft or Renewal Assessment Reports, i.e. DARs or RARs, respectively) as provided by the Rapporteur Member State (RMS) during the pesticide peer review process at European Level. The final EFSA conclusion on the overall genotoxic potential of active substance or metabolites taking into account all available information is not included in the database.

The database contains identity and genotoxicity information on more than 290 active substances and some of their metabolites.

The database represents a practical tool to complement in-silico tools i.e. QSAR (Quantitative structure–activity relationship models), grouping and read across for prediction of the genotoxicity hazard of the pesticides residues, and it supposes to enlarge the chemical domains for their application.

Format: xls; contact: data.collection@efsa.europa.eu, pesticides.ppr@efsa.europa.eu

DISCLAIMER Without prejudice to the legal notice applicable to EFSA's website available here, the following legal notice applies to the Pesticide genotoxicity database and any documents, data or information contained therein. Users are advised to read this legal notice carefully before accessing, using or reading any document, data or information made available in this context, or making any other use of the Database. The Pesticide genotoxicity database is a compilation of chemical and genotoxicity information on active substances and some of their metabolites. The database contains the results of individual studies as initially assessed by the Rapporteur member state (RMS) and reported in the respective Draft assessment reports (DARs) or Renewal Assessment Reports (RARs). The final EFSA Conclusions on the respective active substances are available to the public on the EFSA Journal. The database includes the data that was available at the moment of compilation of the database (December 2016) and will be updated on a regular basis by including or deleting of information as a result of renewal procedure of active substances (Regulation (EU) No 1107/2009). EFSA makes no representations or warranties about the accuracy or suitability of any document, information, data provided in the Database. In case of discrepancy between the data provided in the original scientific output (DARs/RARs) and that in this database, preference shall be given to the former. This database does not disclose any commercially sensitive or otherwise confidential information. Unless otherwise stated, the owners of the data compiled in this database are the applicants under Regulation (EU) No 1107/2009, and by acceiding the Database you acknowledge that agreement for reuse of these data should be sought from them. The information provided in the Database and related materials are not intended to constitute advice of any kind or the rendering of consulting, or other professional services of any kind. Acceding the Database does not establish any contractual relationship with EFSA. Users are advised to consult with an attorney, food consultant or other professional to determine what may be best for your individual needs. By acceding the Database, you also acknowledge that the documents, data or information made available by EFSA may contain inaccuracies or errors. The content of the information provided is for your information and use only. It may be subject to change at any time and without prior notice by EFSA.

About this dataset

Context

The dataset tabulates the Reading town population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Reading town. The dataset can be utilized to understand the population distribution of Reading town by age. For example, using this dataset, we can identify the largest age group in Reading town.

Key observations

The largest age group in Reading, Massachusetts was for the group of age 35-39 years with a population of 2,006 (7.87%), according to the 2021 American Community Survey. At the same time, the smallest age group in Reading, Massachusetts was the 85+ years with a population of 482 (1.89%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Reading town is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Reading town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Reading town Population by Age. You can refer the same here

United States Avg Sale to List: All Residential: Reading, PA data was reported at 98.574 % in Jul 2020. This records an increase from the previous number of 97.544 % for Jun 2020. United States Avg Sale to List: All Residential: Reading, PA data is updated monthly, averaging 96.271 % from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 98.844 % in Apr 2020 and a record low of 93.418 % in Feb 2012. United States Avg Sale to List: All Residential: Reading, PA data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB050: Average Sales to List: by Metropolitan Areas.

Overview

This dataset contains sample synthetic data used for training a solution for reading analog pressure gauges values. We have used this during the writing of our paper and blog(s) which showcase how synthetic data can be used to train and use computer vision models. We've chosen the topic of Analog Gauge Reading Understanding as it is a common problem in many industries and exemplifies how output from multiple models can be consumed in heuristics to get a final reading.

Dataset contents

The dataset contains the following: - subset of the synthetic data used for training, we have included the two latest versions of datasets. Each contains both the images and the coco annotations for segmentation and pose estimation. - inference data for the test videos available in the Kaggle dataset. For each video there is one CSV file which contains for every frame the bbox for the (main) gauge, keypoints locations for the needle tip, gauge center, min and max scale ticks, and the predicted reading.

PIRLS provides trends and international comparisons of fourth grade students’ reading achievement and students’ competencies in relation to goals and standards for reading education. In addition to reading assessment, the PIRLS school, teacher, student and home questionnaires gather extensive information about the contextual factors at home and school which are associated with the teaching and learning of reading. PIRLS 2021 is the fifth cycle of the Progress in International Reading Literacy Study. In 2021, IEA’s PIRLS (Progress in International Reading Literacy Study), well-established as the “de facto” worldwide standard for monitoring reading comprehension achievement, will mark its 20th year. This cycle marks the transition to digital assessment. Approximately half the countries evaluated students using a state-of-the-art digital assessment with engaging and interactive reading assessment materials PIRLS 2021 offers the PIRLS assessment of literary and informational reading in a digital format, presenting reading passages and items as an engaging and visually attractive experience that motivates students and increases operational efficiency. Edition 2 of this database includes also process data. Some minor changes have been applied on August 17, 2023 to edition 1. Some minor changes have been applied on October 3, 2023 to edition 1.Edition 2:Differences to the former IDB: Non-Response Indicators were added to the ASA files: These are variables indicating whether students had missing achievement item responses (Omitted or Not Reached) for subgroups of items according to item type and PIRLS reading subdomain. ASP files were added: The PIRLS 2021 student process data files contain variables associated with students’ navigation in the digital PIRLS 2021 assessment. The PIRLS 2021 process data files include three types of process variables associated with the achievement items: Total time on item (seconds); Time on first item visit (seconds); Number of item visits (frequency) PIRLS Educational measurements and tests

Graph and download economic data for Unemployment Rate in Reading, PA (MSA) (READ742UR) from Jan 1990 to Aug 2025 about Reading, PA, unemployment, rate, and USA.

Number of programs held, by branch, during annual “Summer Read & Learn” (June through August). Numbers represent programs held for target audiences (i.e., babies, toddlers, preschoolers, kindergartners, elementary school-aged, teenagers).