This collection comprises unaltered data files downloaded from https://eddataexpress.ed.gov/download/data-library on February 6, 2025. The original access page consisted of a table with category filters, which provided links to data ZIP files containing the specified data fields. This table has been saved into tabular data formats here in the Index folder, with the original web links replaced with the matching ZIP filename only, which essentially replicates the functionality of the original web page in a downloadable format.In the website's underlying file structure, the original ZIP files were nested within folders named according to the format EID_####, apparently to avoid conflicts between files with the same name. These seeming duplications might have been due to updates or revisions that had to be made to a data file. To preserve this original order, the ZIP files were renamed by appending the EID number to their original file name. The files were not otherwise unzipped or altered in any way from their original state.At the time of download, the page at https://eddataexpress.ed.gov/download/data-library displayed the following two notices in red:"The COVID-19 pandemic disrupted the collection and reporting of data on EDE, beginning in SY 2019-20. The Department urges abundant caution when using the data and recommends reviewing the relevant data notes prior to use or interpretation. This includes data on state assessments, graduation rates, and chronic absenteeism.""WARNING: The data library functionality has stopped working temporarily for many SY2122 school files. Please go to the download tool page to download your data of interest. We apologize for the inconvenience."--------------------The "About Us" page from the ED Data Express website had this to say about its resources:Purpose of ED Data ExpressED Data Express is a website designed to improve the public's ability to access and explore high-value state- and district-level education data collected by the U.S. Department of Education. The site is designed to be interactive and to present the data in a clear, easy-to-use manner, with options to download information into Excel or to explore the data within the site's grant program dashboards. The site currently includes data from EDFacts, Consolidated State Performance Reports (CSPR), and the Department's Budget Service office. For more information about these topics, please visit the following web pages:https://www2.ed.gov/about/inits/ed/edfacts/index.html [see below for the text of the linked page]https://www2.ed.gov/about/offices/list/om/fs_po/ofo/budget-service.html [this URL was dead at the time of download]Using the SiteED Data Express includes two sections that allow users to access and view the data: (1) grant program data dashboards and (2) download functionality. The grant program data dashboards provide a snapshot of information on the funding, participation and performance of some of the grant programs administered by the U.S. Department of Education's Office of Elementary and Secondary Education. The dashboards are interactive and update depending on the program, state and school year selected. Additional information is provided through data notes as well as through the small "i" icon. The download functionality allows users to build customized tables of data and contain more data than what is available via the dashboards. The download functionality also allows users to download data notes which provide important caveats and contextual information to consider when using the data. Data Included and Frequency of UpdatesThe site currently includes funding, participation and performance data from school years 2010-11 to 2016-17 on formula grant programs administered in the Office of Elementary and Secondary Education. Additional data and data notes will be added to the site over time. Quality Control and Personally Identifiable InformationAll CSPR and EDFacts data are self-reported by each state. The U.S. Department of Education conducts a review of the data and provides feedback to states, but it is ultimately states’ responsibility to verify and certify that their data are correct. Please note that during the reporting years represented on this site, the Office of Elementary and Secondary Education in collaboration with EDFacts and SEAs have wor

Scala Higher Education Dhl Express Usa Mia Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

We study labor-market returns to vocational versus general secondary education using a regression discontinuity design created by the centralized admissions process in Finland. Admission to the vocational track increases initial annual income and this benefit persists at least through the mid-thirties, and present discount value calculations suggest that it is unlikely that life-cycle returns will turn negative through retirement. Moreover, admission to the vocational track does not increase the likelihood of working in jobs at risk of replacement by automation or offshoring. Consistent with comparative advantage, we observe larger returns for people who express a preference for vocational education.

In this project, we aim to analyze and gain insights into the performance of students based on various factors that influence their academic achievements. We have collected data related to students' demographic information, family background, and their exam scores in different subjects.

**********Key Objectives:*********

1. Performance Evaluation: Evaluate and understand the academic performance of students by analyzing their scores in various subjects.

2. Identifying Underlying Factors: Investigate factors that might contribute to variations in student performance, such as parental education, family size, and student attendance.

3. Visualizing Insights: Create data visualizations to present the findings effectively and intuitively.

Dataset Details:

• The dataset used in this analysis contains information about students, including their age, gender, parental education, lunch type, and test scores in subjects like mathematics, reading, and writing.

Analysis Highlights:

• We will perform a comprehensive analysis of the dataset, including data cleaning, exploration, and visualization to gain insights into various aspects of student performance.

• By employing statistical methods and machine learning techniques, we will determine the significant factors that affect student performance.

Why This Matters:

Understanding the factors that influence student performance is crucial for educators, policymakers, and parents. This analysis can help in making informed decisions to improve educational outcomes and provide support where it is most needed.

Acknowledgments:

We would like to express our gratitude to [mention any data sources or collaborators] for making this dataset available.

Please Note:

This project is meant for educational and analytical purposes. The dataset used is fictitious and does not represent any specific educational institution or individuals.

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The DataChildMap report investigates how Italian families with children aged 0–6 perceive and manage digital technologies in domestic and educational contexts. The study is based on a national survey (N=2000; n=780 for the 0–6 group) conducted between March and May 2023 using a CAWI (Computer-Assisted Web Interview) method with stratified random sampling. To ensure inclusivity, respondents with lower education levels were also reached via phone interviews.

The questionnaire, implemented via LimeSurvey, was structured into sections capturing: (1) family demographics, parental education, and digital skills; (2) children’s age, educational setting, and technology use; (3) household access to digital devices and connectivity; and (4) parental perceptions and concerns regarding data privacy, digital rights, and educational technologies in Early Childhood Education and Care (ECEC). Items explored the use of educational platforms, social media, smart toys, and AI-based tools, alongside attitudes toward adult supervision, institutional support, and public-private collaborations in technology provision. Likert scales and open responses were combined to capture nuanced opinions.

The survey results reveal that parents’ knowledge of ECEC digital practices is limited, with awareness shaped by education level and geography. Despite recognizing potential learning benefits, families express concerns about data privacy, profiling, and insufficient institutional communication. These findings underscore the need for policies and co-educational strategies that enhance critical digital literacy and safeguard children’s rights.

In this open data record, we present:

a) The Codebook

b) The dataset. Please consider that the city of respondent's, school details and some open responses have been eliminated for anonymisation purposes.

c) The initial report elaborated in the context of the research.

All data respect the current legislation on protection and confidentiality in the processing of personal data, in accordance with the provisions of Regulation (EU) No. 2016/ 679 of the European Parliament and of the Council of the European Union. 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuaIs about the processing of personal data and the free movement of such data and repealing Directive 95/46/EC (General Data Protection Regulation, GDPR. All data have been processed using identification codes to preserve the anonymity and confidentiality of the participants and the results, within the framework of the Belmont Report and the Code of Integrity in Research of the University of Padova (LINK)

Ethical approval was obtained upon the project funding in accordance with the institutional guidelines of the Department of Philosphy, Sociology, Pedagogy and Applied Psychology (University of Padova) for the collection of these data.

Higher Education Institutions in Poland Dataset

This repository contains a dataset of higher education institutions in Poland. The dataset comprises 131 public higher education institutions and 216 private higher education institutions in Poland. The data was collected on 24/11/2022. This dataset was compiled in response to a cybersecurity investigation of Poland's higher education institutions' websites [1]. The data is being made publicly available to promote open science principles [2].

Data

The data includes the following fields for each institution:

Id: A unique identifier assigned to each institution.

Region: The federal state in which the institution is located.

Name: The original name of the institution in Polish.

Name_EN: The international name of the institution in English.

Category: Indicates whether the institution is public or private.

Url: The website of the institution.

Methodology

The dataset was compiled using data from two primary sources:

Public Higher Education Institutions: Data was sourced from the official website of the Ministry of Education and Science of Poland [3].

Private Higher Education Institutions: Data was obtained from the RAD-on system, which is part of the Integrated Information Network on Science and Higher Education [4].

For the international names in English, the following methodology was employed:

Both Polish and English names were retained for each institution. This decision was based on the fact that some universities do not have their English versions available in official sources.

English names were primarily sourced from:

The Polish National Agency for Academic Exchange's official document [5].

The website Studies in English [6].

Official websites of the respective Higher Education Institutions.

In instances where English names were not readily available from the aforementioned sources, the GPT-3.5 model was employed to propose suitable names. These proposed names are distinctly marked in blue within the dataset file (hei_poland_en.xls).

Usage

This data is available under the Creative Commons Zero (CC0) license and can be used for academic research purposes. We encourage the sharing of knowledge and the advancement of research in this field by adhering to open science principles [2].

If you use this data in your research, please cite the source and include a link to this repository. To properly attribute this data, please use the following DOI: 10.5281/zenodo.8333573

Contribution

If you have any updates or corrections to the data, please feel free to open a pull request or contact us directly. Let's work together to keep this data accurate and up-to-date.

Acknowledgment

We would like to express our gratitude to the Ministry of Education and Science of Poland and the RAD-on system for providing the information used in this dataset.

We would like to acknowledge the support of the Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), within the project "Cybers SeC IP" (NORTE-01-0145-FEDER-000044). This study was also developed as part of the Master in Cybersecurity Program at the Polytechnic University of Viana do Castelo, Portugal.

References

Pending.

S. Bezjak, A. Clyburne-Sherin, P. Conzett, P. Fernandes, E. Görögh, K. Helbig, B. Kramer, I. Labastida, K. Niemeyer, F. Psomopoulos, T. Ross-Hellauer, R. Schneider, J. Tennant, E. Verbakel, H. Brinken, and L. Heller, Open Science Training Handbook. Zenodo, Apr. 2018. [Online]. Available: [https://doi.org/10.5281/zenodo.1212496]

Ministry of Education and Science of Poland. "Wykaz uczelni publicznych nadzorowanych przez Ministra właściwego ds. szkolnictwa wyższego - publiczne uczelnie akademickie." Nov 2022. [Online]. Available: https://www.gov.pl/web/edukacja-i-nauka/wykaz-uczelni-publicznych-nadzorowanych-przez-ministra-wlasciwego-ds-szkolnictwa-wyzszego-publiczne-uczelnie-akademickie

RAD-on System. "Dane instytucji systemu szkolnictwa wyższego i nauki." Nov 2022. [Online]. Available: https://radon.nauka.gov.pl/dane/instytucje-systemu-szkolnictwa-wyzszego-i-nauki

Polish National Agency for Academic Exchange. "List of the university-type HEIs." 2023. [Online]. Available: https://nawa.gov.pl/images/Aktualnosci/2023/Att.-2.-List-of-the-university-type-HEIs.pdf

Studies in English. [Online]. Available: www.studies-in-english.pl

Welcome to the dataset capturing a comprehensive overview of Senior Secondary Schools in the vibrant city of Delhi, India. This dataset has been meticulously web scraped to provide valuable insights into educational institutions catering to senior secondary education.

Key Features:

School Details: Uncover information about the schools, including names, locations, and affiliations. Contact Information: Gain access to contact numbers for seamless communication with the institutions.

Web Presence: Explore the online footprint of these schools with website URLs.

Rich Insights: Utilize this dataset for a nuanced understanding of the senior secondary education landscape in Delhi.

Potential Applications:

Education Planning: Ideal for policymakers and education planners looking to enhance educational infrastructure.

Parental Guidance: Parents can make informed decisions about their children's education. Research and Analysis: Researchers can delve into the trends and patterns of senior secondary education.

Instructions for Use:

Exploratory Data Analysis (EDA): Leverage data visualization tools to perform EDA and extract meaningful trends.

Geospatial Analysis: Utilize location data for mapping and geospatial analysis of school distribution. Machine Learning Applications: Explore predictive modeling or clustering to derive deeper insights.

Acknowledgments: We express our gratitude to the data source providers, making this dataset a valuable resource for educational research and analysis.

Your Feedback Matters: Feel free to contribute your findings, visualizations, or any improvements to enhance the community's understanding of senior secondary education in Delhi.

Happy Exploring!

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ABSTRACT: The purpose of this paper is to present, analyze and discuss the constitution of the field of Special Education, having the texts published by the Brazilian Journal of Special Education - RBEE- from 1992 to 2017 as its source. Through this selected source, it was possible to elaborate a trend balance regarding the subjects that produce the discourses disseminated by the journal, as well as the subjects and policies that these narratives produce and the way in which they are produced. In order to do so, among the total of published texts, those which presented a broad discussion on Special Education were selected through three axes of entry: who produced the papers, what was produced and by what means these narratives were produced. The collected data were organized through indicators that fed the database allowing both the elaboration of tables and the analysis of the main trends of this production from its launch until the year 2017, having Pierre Bourdieu's studies as theoretical reference, specifically the notions of field and language. The highlighted results of this analysis are the high incidence of productions in the Southeastern region of Brazil, coming from authors with a doctorate degree in the academic field related to education, culminating with the analysis of terms that designate the population assisted by Special Education and that express the dispute over the scientific authority of this field of investigation.

The University of Adelaide Biometry Hub, based in the School of Agriculture Food and Wine and funded by the GRDC Analytics for the Australian Grains Industry (AAGI) investment, provides statistical training workshops for researchers working in agronomic and plant sciences. The goal of these workshops (referred to internally as W0 to W3) is to educate these researchers in best practice statistical methodology for use in the design and analysis of agronomic comparative experiments, and promote consistency in statistical analyses across the industry.

To assess the benefits for workshop participants, we implemented a self-confidence survey. The survey consisted of questions aligned with the key outcomes of each workshop. Administered both before and after each session, the pre- and post-surveys shared questions related to participants' confidence in their ability to meet the workshop's key learning objectives. Respondents used a seven-point Likert scale, ranging from 'not confident' (shown as 1 in the data) to 'very confident' (7), to express their agreement or disagreement with the questions. The purpose of conducting surveys before and immediately after the workshop was to track the shift in participants' self-efficacy. Participants self-created unique, anonymised identifiers based on personal characteristics to enable confidential linking of the pre- and post-survey responses.

Data fields:
ID: User created ID
Q1 to Q12: Numerical user response to questions.

Over the last ten years, social media has become a crucial data source for businesses and researchers, providing a space where people can express their opinions and emotions. To analyze this data and classify emotions and their polarity in texts, natural language processing (NLP) techniques such as emotion analysis (EA) and sentiment analysis (SA) are employed. However, the effectiveness of these tasks using machine learning (ML) and deep learning (DL) methods depends on large labeled datasets, which are scarce in languages like Spanish. To address this challenge, researchers use data augmentation (DA) techniques to artificially expand small datasets. This study aims to investigate whether DA techniques can improve classification results using ML and DL algorithms for sentiment and emotion analysis of Spanish texts. Various text manipulation techniques were applied, including transformations, paraphrasing (back-translation), and text generation using generative adversarial networks, to small datasets such as song lyrics, social media comments, headlines from national newspapers in Chile, and survey responses from higher education students. The findings show that the Convolutional Neural Network (CNN) classifier achieved the most significant improvement, with an 18% increase using the Generative Adversarial Networks for Sentiment Text (SentiGan) on the Aggressiveness (Seriousness) dataset. Additionally, the same classifier model showed an 11% improvement using the Easy Data Augmentation (EDA) on the Gender-Based Violence dataset. The performance of the Bidirectional Encoder Representations from Transformers (BETO) also improved by 10% on the back-translation augmented version of the October 18 dataset, and by 4% on the EDA augmented version of the Teaching survey dataset. These results suggest that data augmentation techniques enhance performance by transforming text and adapting it to the specific characteristics of the dataset. Through experimentation with various augmentation techniques, this research provides valuable insights into the analysis of subjectivity in Spanish texts and offers guidance for selecting algorithms and techniques based on dataset features.