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Effects of the type of lunch meal and teaching-learning methods on CRI student engagement in afternoon classes.
<p><br> The post-lunch slump is a natural dip in energy caused not only by our circadian cycles but also as a result of digestive processes in our body after consuming lunch meals. It being a widely known concept, there are ample studies that focus on its effects on academic performance of students. While this dip in energy does affect student engagement to a large extent, there are also studies on how the teaching-learning methods are a factor affecting academic performance. However, there are no relevant studies that aim to look at both these factors in tandem, i.e. the effect of lunch along with the teaching-learning methods in the afternoon classrooms as factors influencing academic performance and student engagement. This paper aims to study this area of topic hypothesising that having a more balanced lunch along with engaging in more interactive classes would positively correlate with student engagement in afternoon classes. The study was conducted with bachelors and masters students at the CRI, University of Paris through surveys that were sent to students at the end of their class day. </p> <p>Due to lack of adequate data, the study was not able to produce significant correlations. However smaller nuances of the relationship between lunch, teaching-learning methods and student engagement were found. While engagement in classrooms did seem to be maximum when it was interactive and only 50% of students' lunch was formed of carbohydrates, no significant correlations were found to confirm the hypotheses.</p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 1: Essays, Finland.
<p><strong>Version 1.1.</strong></p> <p><strong>Updated from </strong>https://zenodo.org/record/5517595</p> <p><strong>Changes: </strong>added .csv copy of the dataset. Clarified the README below, and added name of publishing journal. No other changes.</p> <p>Added a FEDORA project README below.</p> <p> </p> <p><strong>Description of dataset:</strong></p> <p>This matrix, presented in two formats (.xlsx and .csv), contains an English-language dataset (translated from original Finnish). The data relate to a research article <em>Students’ technological images of the future: implications for science and technology education, </em>accepted to be published in European Journal of Futures Research.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Here, excerpts from students' essays (the context of which is given in the article) are given. The excerpts are the ones that have been used in analysis for the article identified above. Further details will be available in the published article.</p> <p>385 such excerpts are given, originating in 57 essays in which upper-secondary students imagine the year 2035 or 2040 and the technological environment in which they would like to live at that time. The numbering was used to group codes for the analysis: type of technology (1), effect of technology (1E), and positive/negative framing (2A-C).</p> <p>The dataset is intended for providing transparency, but it may also be used for further research. Assistance may be available from the authors at reasonable request. Please note that the dataset presented here contains redundancies and a few additional codes that were not used in the analysis. The redundant quotations from the essays were not duplicated in the analysis, but were not removed from this spreadsheet export. Apologies for any inconvenience.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the quotations are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters, as these can easily be inferred.</p> <p>The related research article gives a fuller description of the dataset and analysis.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Students_images_of_technological_futures_DATA_Zenodo_csv.csv</p> <p>Students_images_of_technological_futures_DATA_Zenodo_xlsx.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/6397196</em></p>
Biological data science courses at UMONS, Belgium: student's activity for 2019-2020
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2020-2021
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (<a href="mailto:raphael.conotte@umons.ac.be">raphael.conotte@umons.ac.be</a>) that also contributed to a part of the learnr exercises and of the inline course.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Biological data science courses at UMONS, Belgium: student's activity for 2018-2019
<p>Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.</p> <p>Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (<a href="mailto:philippe.grosjean@umons.ac.be">philippe.grosjean@umons.ac.be</a>) and Guyliann Engels (<a href="mailto:guyliann.engels@umons.ac.be">guyliann.engels@umons.ac.be</a>) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.</p> <p><strong>How to use these data?</strong></p> <p>The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (<a href="https://frictionlessdata.io/">https://frictionlessdata.io</a>). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at <a href="https://create.frictionlessdata.io/">https://create.frictionlessdata.io</a>. There is a large set of libraries and tools for different programming languages available at <a href="https://frictionlessdata.io/tooling/libraries/">https://frictionlessdata.io/tooling/libraries/</a>. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (<a href="https://www.sciviews.org/learnitdown/">https://www.sciviews.org/learnitdown/</a>).</p> <p>For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.</p>
Improving students' privacy awareness – Analysis of a pilot survey to design a VR environment for self-paced learning
<p>In this research, we measured the knowledge of students at the University of Debrecen in the field of data privacy awareness, online and password security.</p> <p><strong>Description</strong></p> <ul> <li>In the questionnaire, green-highlighted answer signs the correct answer to each question.</li> <li>Total data set contains the answers to each question and the respondent's age.</li> <li>Correct/incorrect data set contains information if the answer is correct to each question, and it also contains the respondent's age. One means the answer was correct, and zero means the answer was incorrect.</li> </ul>
Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses
<p>This dataset contains anonymised raw data and examples of accommodations made for neurodiverse students in four undergraduate courses in Computer Science and Software Engineering programmes. The dataset is published as a part of a book chapter in which we report the accommodations.</p> <p>Overall guidelines we followed, including their sources, are contained in <strong>guidelines.md.</strong></p> <p>The raw data for the two surveys is contained in the two Excel files <strong>survey1.xlsx</strong> and <strong>survey2.xlsx</strong>. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files<strong> survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, </strong>and<strong> survey2_freetext_neurotypical.txt.</strong></p> <p>The remaining files are examples of the adapted lecture slides and assignment texts. Here, files starting with WEBcourse are from a mandatory undergraduate course on web development, while files starting with SEcourse are from a mandatory undergraduate course giving an overview of Software Engineering.</p>
Student's logs and perceptions of an automated assessment tool in a software engineering MOOC specialization
<p>Our dataset contains students' perceptions and usage of an automated assessment tool (MOOCauto) for obtaining formative feedback in software engineering assignments that are part of a MOOC specialization at Universidad Politécnica de Madrid (Spain), delivered by the MiriadaX platform. The dataset has previously been used in a study to evaluate students' perceptions of the tool and to analyze their usage patterns using Growth Mixture Models <a href="https://www.computer.org/csdl/magazine/so/5555/01/10196480/1P9AhkBLYXK">(López-Pernas et al., 2023)</a>. The code of each of the assignments is available on Github: <a href="https://github.com/ging-moocs">https://github.com/ging-moocs</a>.</p> <p>Our dataset contains two files:</p> <h2>MOOCauto usage logs</h2> <p>The first file is called<strong> moocauto_logs.csv </strong>and it contains 9,108 anonymized logs of students' use of the automated assessment tool in the MOOC specialization assignments. The columns of the dataset are as follows:</p> <ul> <li><strong>MOOCid</strong>: Unique numeric identifier for the MOOC (1-4)</li> <li><strong>MOOC: </strong>Name of the MOOC: Frontend Development, Backend Development, Git & Github, Fullstack Development</li> <li><strong>AssignmentName</strong>: Name of the assignment.</li> <li><strong>AssignmentId</strong>: Unique identifier for each assignment (1-17)</li> <li><strong>user: </strong>Unique identifier of the student (it varies per assignment)</li> <li><strong>timestamp: </strong>Time in which the assessment was performed</li> <li><strong>score</strong>: Score obtained (0-10)</li> </ul> <h2>Students' perceptions of MOOCauto</h2> <p>The second file is called <strong>moocauto_questionnaire.csv</strong> and it contains 213 students' responses to the questionnaire conducted at the end of each MOOC in order to evaluate their opinion of the tool and perception on usefulness, ease of use, and other aspects related to the Technology Acceptance Model (TAM). The questions were as follows:</p> <ul> <li><strong>What is your general opinion of MOOCauto?</strong> (1 Horrible - 5 Excellent)</li> <li><strong>Indicate your level of agreement with the following statements </strong>(1 Strongly disagree - 5 Strongly agree) <ul> <li>MOOCauto has been easy to install</li> <li>MOOCauto has been easy to use</li> <li>The feedback provided by MOOCauto was easy to understand</li> <li>The feedback provided by MOOCauto was useful</li> <li>The feedback provided by MOOCauto helped me improve my assignments</li> <li>The documentation Of MOOCauto was useful</li> <li>MOOCauto has increased my motivation to work on the assignments</li> <li>I prefer the feedback from MOOCauto than from peer assessment</li> <li>I would like to have a bot like MOOCauto in other MOOCs</li> </ul> </li> <li><strong>How useful do you perceive the following features of MOOCauto?</strong> (1 Useless - 5 Very useful) <ul> <li>It works locally on my computer</li> <li>It allows to run the test suite as many times as I want</li> <li>It provides instantaneous feedback every time the test suite is executed</li> <li>It has documentation that explains its use and available options</li> </ul> </li> </ul>
German Student Responses to Probability Theory and Statistics Bachelor Course (WuS24): Evaluated with Rubrics
<p><strong>Description:</strong></p> <p>This dataset contains questions and answers from an introductory computer science bachelor course on statistics and probability theory at Hochschule Bonn-Rhein-Sieg. The dataset includes three questions and a total of 90 answers, each evaluated using binary rubrics (yes/no) associated with specific scores.</p> <p> </p> <p><strong>Dataset Components:</strong></p> <ol> <li><em>questions.csv</em>: Contains the details of the three questions. <ul> <li>Columns: <ul> <li><em>question_id</em>: Unique identifier for each question</li> <li><em>question</em>: The text of the question</li> <li><em>solution</em>: The reference answer for the question</li> <li><em>max_score</em>: The maximum score for this question</li> </ul> </li> </ul> </li> <li><em>rubrics.csv</em>: Contains the grading rubrics for each question. <ul> <li>Columns: <ul> <li><em>question_id</em>: Unique identifier for each question</li> <li><em>rubric_id</em>: Unique identifier for each rubric within a question</li> <li><em>rubric</em>: The rubric phrased as a question</li> <li><em>score</em>: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li><em>answers.csv</em>: Contains 90 student answers to the questions. <ul> <li>Columns: <ul> <li><em>answer_id</em>: Unique identifier for each answer</li> <li><em>question_id</em>: Unique identifier of the question that is answered</li> <li><em>answer</em>: The text of the student's answer</li> <li><em>score</em>: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li><em>answer_rubrics.csv</em>: Contains the evaluations of rubrics for each answer.<br> <ul> <li>Columns: <ul> <li><em>answer_id</em>: The identifier of the answer.</li> <li><em>question_id</em>: The identifier of the question.</li> <li><em>rubric_id</em>: The identifier of the rubric for that question.</li> <li><em>label</em>: Indicates if the rubric crierion is fulfilled for the specific answer (true / false).</li> </ul> </li> </ul> </li> </ol> <pre><strong><br>Working with the Dataset:</strong> The easiest way to work with this dataset is using the class `RubricsDataset` defined in the file `dataloader.py`. Example:<br><br></pre> <pre><code>from dataloader import RubricsDataset<br><br>dataset = RubricsDataset.from_directory("data")<br> dataset.get_question(1) # Get a dictionary containing info about the first question, including rubrics dataset.get_answers(1) # Get all the answers for the first question as a list. Each answer is a dictionary with answer, score, rubrics.</code></pre>
Investigating secondary students' stance on IoT driven educational activities - Dataset
<p>This data set supports the research and the results that are presented in Glaroudis, D., Iossifides, A., Spyropoulou, N., Zaharakis, I. D., “Investigating Secondary Students' Stance on IoT Driven Educational Activities”. In Kameas A, and Stathis K. (Eds) Ambient Intelligence, LNCS 11249, 2018, pp. 188-203. Springer Nature Switzerland AG. DOI: https://doi.org/10.1007/978-3-030-03062-9_15.</p>
Unistudium 2022: students groups logs on moodle
<p>This dataset contains the logs collected by <strong>Unistudium</strong>, the University of Perugia elearning platform based on moodle, a open source software for learning management systems (https://moodle.org).</p> <p>The collected logs record interactions with the platform of students attending 4 courses during the time period of one semester, from 1st September to 31st December. </p> <p>The platform support blended learning, providing teaching material to courses in presence delivered at University of Perugia. Data are completely anonymized.</p> <p>The dataset has been developed as part of the research activity of the scientific research group <strong>KITLab - Knowledge and Information Technology Laboratory</strong> (https://www.dmi.unipg.it/kitlab/). KIT Lab is member of the CLAIRE Network: The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE)</p>
Basic Needs and Student Success Survey (Pilot 2)
<p>Food insecurity among college students is a serious problem that can impact student performance in the classroom and ultimately effect student success. The <a href="https://www.csuchico.edu/chc/">Center for Healthy Communities</a> (CHC) developed the <strong>Basic Needs Student Success Survey (BNS3)</strong> and administered it to undergraduate students participating in the <a href="https://www2.calstate.edu/attend/student-services/eop">Educational Opportunity Program (EOP)</a> at three California State Universities between November 2020 and March 2021. </p> <p>The purpose of this second cross-sectional pilot study was to revise the BNS3 tool and validate student perception of the following:</p> <ol> <li>The impact of receiving <a href="https://www.csuchico.edu/calfresh/index.shtml">CalFresh</a> assistance.</li> <li>The impact of utilization of the campus food pantry on their health, nutrition, cooking confidence, time management and academic performance.</li> </ol> <p>This entry contains </p> <ol> <li>The anonymized and cleaned data set</li> <li>A codebook (data dictionary)</li> <li>The survey tool as a Qualtrics export to Word file</li> </ol>
Recreational Reading as a Leisure Activity: Perspectives from Georgian and Finnish Hearing and deaf students
<p><em>Using a mixed methods approach, this study investigates Finnish and Georgian deaf students' attitudes towards leisure reading and the relationship between their reading intentions and actual reading. </em><em>The primary interest of the study is to identify the external factors that affect reading behaviour directly or indirectly. </em></p> <p><em>An Integrative Model of Planned Behaviour is used as a theoretical framework, where intention is considered the main determinant for the behaviour. </em></p> <p><em>The study is cross-cultural, bringing the experiences of two distinct countries together. <br><br></em></p> <p><em>Reading attitude is investigated quantitatively, using a 5-point Likert Scale survey. 29 statements are broken down into three variables: enjoyment ( statements 4-13), difficulty ( statements 14-22) and social reinforcement (statements 23-29). </em></p> <p>Follow-up focus group interviews investigated the perceived external factors that affect reading behaviour negatively. interview questions were also grouped under three categories: 1) General feelings about reading (experiential attitude or affect), 2) Influences from individuals, close networks or society (perceived norms) and situational or environmental restrictions affect behaviour performance (skills, knowledge, resources, other factors). </p> <p>The interview protocol, questions survey and row data are attached as files.</p> <p>Interview transcripts are saved in Finnish and Georgian languages and can be shared only upon a reasonable request. </p>
Doctoral Students' Educational Needs in Research Data Management: Quantitative Data of Perceived Importance and Current Competencies
<p>These data sets include numerically coded answers to Likert-like scale questions concerning the importance and perceived current research data management competencies of doctoral students. Interviewees were 35 doctoral students and faculty members. Interview forms are attached. The data is connected with the research article: https://doi.org/10.2218/ijdc.v16i1.684</p>
Database of the assessment of two instructional design variables in verbal reasoning and mathematical reasoning courses from the perspective of a Peruvian pre-university center students
<p>These are the data obtained from 4 evaluations made to a sample of 630 students of a Peruvian pre-university center. First, two study variables were evaluated: teaching sequence compliance and the student's educational need according to the perspective of 315 students of the verbal reasoning course. Second, the same study variables were assessed in the remaining 315 students of the mathematical reasoning course. This information is being used in research to obtain an academic degree and later to make a publication of a scientific article.</p> <p>For the treatment of these data, inferential statistics was used through the software R version 3.4.4 (2018) The R Foundation for Statistical Computing.</p>
Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students
<p>The deposited files contain a database related to the study of the fear of COVID-19 among Polish students and a code book. It is connected with the article titled <em>Experience of COVID-19 disease and fear of the SARS-CoV-2 virus among Polish students</em></p>
Who's Who of American Returned Students 遊美同學錄 (1917): Kinship Data
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset records information on kinship ties mentioned in the biographies of 192 students. The data has been extracted automatically through natural language processing, and then manually checked and validated by the author. The dataset records a total of 184 relatives and 216 ties (97 fathers, 80 siblings, 17 uncles, 16 spouses). It is structured as a four-column edge list: </p> <ul> <li>ego: name of the biographed individual</li> <li>relative: names of relatives mentioned in the biography</li> <li>relation: nature of relation (father, uncle, sibling, spouse)</li> <li>relation2: specification on the nature of relation (for siblings and spouses)</li> </ul> <p>In addition, the second tab provides further information on the parents' occupational backgrounds (when available). The occupations have been classified into four main categories: business (merchants, entrepreneurs), intellectual (scholars, teachers, headmasters), officials, other (professionals, renteers...). </p>
Who's Who of American Returned Students 遊美同學錄 (1917): Affiliation Data (Chinese)
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset records all the institutions to which the students were affiliated in the course of their lives, including the educational institutions in which they studied in China, the United States, and other countries; the public or private organizations in which they were employed; as well as their memberships in clubs and associations. The names of organizations were retrieved automatically from the Chinese biographies using <a href="https://bookdown.enpchina.eu/rpackage/HistTextRManual.html#4_Named_Entity_Recognition">named entity recognition</a> (SpaCy model), then manually cleaned, classified, and validated by the author. </p> <p>The attached file contains three tabs for (1) the list of affiliations (data); (2) the classification of organizations (class), and (3) the description of variables (key). The dataset records a total of 2,883 affiliations, linking 401 unique individuals to 1,344 unique institutions, distributed as followed: </p> <table> <tbody> <tr> <td><strong>category</strong></td> <td><strong>n</strong></td> </tr> <tr> <td>education</td> <td>565</td> </tr> <tr> <td>association</td> <td>271</td> </tr> <tr> <td>administration</td> <td>132</td> </tr> <tr> <td>business</td> <td>110</td> </tr> <tr> <td>facility</td> <td>92</td> </tr> <tr> <td>media</td> <td>66</td> </tr> <tr> <td>government</td> <td>49</td> </tr> <tr> <td>factory</td> <td>30</td> </tr> <tr> <td>other</td> <td>22</td> </tr> <tr> <td>military</td> <td>7</td> </tr> </tbody> </table>
Who's Who of American Returned Students 遊美同學錄 (1917): Affiliation Data (English)
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset records all the institutions to which the students were affiliated in the course of their lives, including the educational institutions in which they studied in China, the United States, and other countries; the public or private organizations in which they were employed; as well as their memberships in clubs and associations. The names of organizations were retrieved automatically using <a href="https://bookdown.enpchina.eu/rpackage/HistTextRManual.html#4_Named_Entity_Recognition">named entity recognition</a> (SpaCy model), then manually cleaned, classified, and validated by the author. </p> <p>The attached file contains three tabs for (1) the list of affiliations (data), (2) the classification of institutions (class); (3) the description of variables (key). The dataset records a total of 3,431 affiliations, linking 401 unique individuals to 1,305 unique institutions, distributed as followed: </p> <table> <tbody> <tr> <td><strong>category_main</strong></td> <td><strong>n</strong></td> </tr> <tr> <td>education</td> <td>535</td> </tr> <tr> <td>association</td> <td>255</td> </tr> <tr> <td>media</td> <td>108</td> </tr> <tr> <td>administration</td> <td>92</td> </tr> <tr> <td>business</td> <td>85</td> </tr> <tr> <td>facility</td> <td>47</td> </tr> <tr> <td>executive</td> <td>43</td> </tr> <tr> <td>railway</td> <td>39</td> </tr> <tr> <td>production</td> <td>33</td> </tr> <tr> <td>banking</td> <td>15</td> </tr> <tr> <td>other</td> <td>15</td> </tr> <tr> <td>event</td> <td>13</td> </tr> <tr> <td>military</td> <td>13</td> </tr> <tr> <td>judiciary</td> <td>8</td> </tr> <tr> <td>legislative</td> <td>4</td> </tr> </tbody> </table> <p> </p> <p> </p>
Who's Who of American Returned Students 遊美同學錄 (1917): Vital Data
<p>This dataset is derived from the <em>Whoʻs Who of American Returned Students </em>遊美同學錄<em> </em>[<em>Youmei Tongxue Lu</em>] published in<em> </em>Peking [Beijing] in 1917, compiled by the Returned Students’ Information Bureau (Liumei xuesheng tongxunchu 留美學生通訊處) established at Tsinghua School in 1915. This book is crucial for documenting the early <em>liumei</em>'s experiences during the transitional period between the late Qing dynasty and the early years of the Republic (1911-). </p> <p>The dataset provides biographical information on the 401 individuals recorded in the directory, including their multiple names, gender, date and place of birth, family situation, date of arrival in the United States, date of return, source of funding, field of study. The data has been first retrieved automatically using Natural Language Processing techniques , and then manually checked and validated by the author. The information is given in both Chinese and English. </p> <p>The Excel file contains two tabs, one for the data, one for the description of variables (key). </p> <p> </p>
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