Find research datasets worth reusing

opencc-by-4.0May 2022View details →

Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys

Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.

opencc-by-4.0Jul 2022View details →

Online survey among students and teachers in the Swiss farm management course

This dataset contains survey data including the codebook for an online survey conducted in German and French in Switzerland in spring 2021. With this survey, we aimed to find out what students learn and what teachers teach in this course about digital technologies in agriculture.

opencc-by-4.0Jul 2021View details →

Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment

Dataset name: asppl-dataset.csv Version: 1.0 Dataset period: 06/07/2018- 05/25/2021 Dataset Characteristics: Multivalued Number of Instances: 4861 Number of Attributes: 33 Missing Values: Yes Area(s): Health and education  Sources:  <ul> <li> Primary: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1]; </li> <li> Secondary:  <ol> <li> Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2]; </li> <li> National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and  </li> <li> Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4]. </li> </ol> </li> </ul> Description: The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.

opencc-by-4.0Jul 2023View details →

Raw Data of the Surveys conducted in the Advanced Inorganic Lab Course in the Winter Terms 2020/2021, 2021/2022, and 2022/2023 at RWTH Aachen University

In the advanced inorganic lab course at RWTH Aachen University, the undergraduate students are asked to use the electronic laboratory notebook (ELN) Chemotion and thus become aware of and familiar with research data management (RDM) at an early stage in their studies. To map the implementation of research data management and the Chemotion ELN in the lab course, a survey was conducted in the winter terms 2020/2021, 2021/2022, and 2022/2023 to ask the students to share their experiences and criticism on these topics. In this data publication, the underlying raw data of the surveys (as received from the survey software SoSci Survey1) are available as .csv-files separated into data, values, and variables for the respective winter terms. Additionally, the evaluated data are summarized in .xlsx-files which are also part of this data publication. As the principal language of the inorganic lab course is German, the survey and it's evaluation are primarily in German language, too. For further information, please have a look at the 01_Read-me.txt file. The survey and the related results and interpretations are available as a journal publication elsewhere. Literature: 1.         Leiner, D. J. SoSci Survey (Version 3.2.12 and newer) [Computer software]. 2020. <a href="https://www.soscisurvey.de">https://www.soscisurvey.de</a> (accessed 2023-07-26).

edi48/100

Course Materials for Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730)

In today's world, understanding environmental data and making informed decisions based on it is crucial for addressing complex environmental challenges. Yale School of the Environment's Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730) course serves as an introduction to the integration of environmental data using R programming language, coupled with machine learning techniques. This dataset contains a zip file with all the data files used in this course, along with a README that has the metadata for those files.

openCC (other)Jul 2025View details →

opencc-by-4.0Sep 2020View details →

What is the clinical course of patients hospitalised for COVID-19 treatment Ireland: a retrospective cohort study in Dublin's North Inner City (the 'Mater 100')

Background: Since March 2020, Ireland has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While several cohorts from China have been described, there is little data describing the epidemiological and clinical characteristics of patients with COVID-19 in Ireland. To improve our understanding of this emerging infection we carried out a retrospective review of patient data to examine the clinical characteristics of patients admitted for COVID-19 hospital treatment. Methods: Demographic, clinical and laboratory data on the first 100 adult patients admitted to Mater Misericordiae University Hospital (MMUH) for in-patient COVID-19 treatment after onset of the outbreak in March 2020 was extracted from clinical and administrative records. Results: Fifty-eight per cent were male, 63% were Irish nationals, and median age was 45 years (interquartile range [IQR] =34-64 years). Patients had symptoms for a median of five days before diagnosis (IQR=2.5-7 days), most commonly cough (72%), fever (65%), dyspnoea (37%), fatigue (28%), myalgia (27%) and headache (24%). Of all cases, 54 had at least one pre-existing chronic illness (most commonly hypertension, diabetes mellitus or asthma). At initial assessment, the most common abnormal findings were: C-reactive protein >7.0mg/L (74%), ferritin >247μg/L (women) or >275μg/L (men) (62%), D-dimer >0.5μg/dL (62%), chest imaging (59%), NEWS Score (modified) of ≥3 (55%) and heart rate >90/min (51%). Twenty-seven required supplemental oxygen, of which 17 were admitted to the intensive care unit - 14 requiring ventilation. Forty received antiviral treatment (most commonly hydroxychloroquine or lopinavir/ritonavir). Four died, 17 were admitted to intensive care, and 74 were discharged home, with nine days the median hospital stay (IQR=6-11). Conclusion: Our findings reinforce the emerging consensus of COVID-19 as an acute life-threatening disease and highlights, the importance of laboratory (ferritin, C-reactive protein, D-dimer) and radiological parameters, in addition to clinical parameters. Further cohort studies involving larger samples followed longitudinally are a priority.

opencc-by-4.0Dec 2020View details →

Massive Open Online Courses(MOOCs) and why they fail. A case of HarvardX

Massive open online courses (MOOCs) are free online courses open for everyone to enroll. MOOCs are attractive because they attempt to level the playing field and provide opportunities for students from all walks of life and from different countries to study "together" at the comfort of their own homes and this is facilitated by leading professors from renowned universities at little to no cost. They are sold as an on-demand online education to accommodate conflicting family and work responsibilities which offer students the flexibility to study and do life. Schools like University of Maryland University College were among the first universities to accept MOOCs for credit, since then more institutions have welcomed the idea of providing online education for free. MOOCs sadly have simply not lived up to their mission to transform the educational system around the world and once again we see how a good free initiative fails. Despite of the popularity of MOOCs, employers do not regard certifications from MOOCs as valid credentials neither as an alternative to traditional educational institutions, in essence, it is pretty useless, unless you already possess a degree from an accredited traditional educational institution. We will analyze the first two years of Harvard's attempt at offering open and free courses to all, we will look at its successes, failures and the way forward. The first two years 2012, 2013 Harvard offered only 5 courses. Out of the 290,498 registrations, 49.2% of the students registered for Introduction to Computer Science, 16.9% for Justice, 13% for Health in numbers, 12.2% for Human Health and Global environmental change and finally 8.7% registered for the Ancient Greek Hero. When we break this down by gender, males outnumber females in overall registration by 106,149. Analyzing individual course registration, males still outnumber females, we see this in all the courses listed and even more significantly in the Computer science subject. The goal of MOOCs is to make world class education accessible to all persons including students from developing countries. We do see however that the United States leads the pack in student enrollments (which is a developed country) instead of the targeted countries from the global south. 78.8% of students who registered for the courseware were between ages 16 and 34, we can be reasonably confident and say that most of the students are either millennials or generation z. 68% of the students who registered for the courseware already possess either a bachelor's, master's or a doctorate degree(s). MOOCs have grown in number and popularity but failed to live up to its mission of democratizing education around the world. Out of the 290948 students who registered for the HarvardX open courseware in 2012 and 2013, only 65,261 completed the courseware representing 22.4% of total student enrollments. Out of the 65,261 who completed the courseware only 5728 received certifications, distressingly representing 2% of total student enrollments. Certification rate in general is disappointingly low but we want to see if this is true for all demographics in this dataset. We will analyze individual courses, student age, level of education as well as the country of origin to perhaps diagnose the problem of low completion and low certification. First, out of 143,266 students who registered for Introduction to Computer Science, only 1% were certified. Out of 49,293 students who registered for Justice, 4% were certified. Out of 37709 students who registered for Health in numbers, 5% were certified. Out of the 35509 students who registered for Human health and global environmental change, 2% were certified. Out of the 25171 students who registered for the Ancient Greek hero, only 1% received certifications. Secondly, about 73% of the students who completed the courseware and received certification already have a bachelor's, master's or doctorate degree(s). This shows that students accessing the courseware regard it merely as a resource to fill in gaps to supplement skills already learnt in a four year traditional institution. Additionally about 67% of those who received certifications fall within ages 16 to 30 which is the targeted age group, the only problem is that there are simple not enough certifications for students within this age group compared to the number of enrollments. Out of the 229301 students enrollments who fall within this age group, only 3822 received certifications representing 1.67% of enrollments. We can agree that MOOCs began conceptually as a great idea but fails in implementation, the simple truth is that attitudes towards initiatives labelled 'free' in certain parts of the world encourage apathy. We see that in most countries from every continent except for Europe. Secondly, some students need external discipline to stay committed and complete the courseware but because the stakes are low and there is nothing to lose, most students simply do not see the reason to do so. Thirdly, a large number of those who registered for these courses as well as those who received certifications were from developed countries, failing to democratize education for those who need it the most, students from the global south.

opencc-by-4.0Sep 2024View details →

European Education Provision on Public Procuement of Innovation: find your study course

The growth of procurement and attracting future leaders can be enhanced through the visibility of procurement education. With the goal to map procurement education, PROCEDIN surveyed universities across Europe. The project partners compiled data on <a href="https://procedin.eu/database-of-european-education-provision/">European universities that provide master’s or bachelor’s level education in procurement</a>, sustainability, and entrepreneurship. In the present database, there are 114 different universities from 27 different countries, which together offer 1679 courses. In this dataset you can find all the opportunities for training in POI in European universities and beyond.

opencc-by-4.0Feb 2022View details →

Spontaneous motor tempo over the course of a week: The role of the time of the day, chronotype, and arousal

The spontaneous motor tempo (SMT) or internal tempo describes the natural pace of predictive and emergent movements such as walking or hand clapping. One of the main research interests in the study of the spontaneous motor tempo relates to factors affecting its pace. Previous studies suggest an influence of the circadian rhythm (i.e., 24-h cycle of the biological clock), physiological arousal changes, and potentially also musical experience. This study aimed at investigating these effects in participants‘ everyday life by measuring their SMT four times a day over seven consecutive days, using an experience sampling method. The pace of the SMT was assessed with a finger-tapping paradigm in a selfdeveloped web application. Measured as the inter-tap interval, the overall mean SMT was 650 ms (SD = 253 ms). Using multi-level modelling (MLM), results show that the pace of the SMT sped up over the course of the day, and that this effect depended on the participants’ chronotype, since participants tending towards morning type were faster in the morning compared to participants tending towards evening type. During the day, the pace of the SMT of morning types stayed relatively constant, whereas it became faster for evening-type participants. Furthermore, higher arousal in participants led to a faster pace of the SMT. Musical sophistication did not influence the SMT. These results indicate that the circadian rhythm influences the internal tempo, since the pace of SMT is not only dependent on the time of the day, but also on the individual entrainment to the 24-h cycle (chronotype).

Biological data science courses at UMONS, Belgium: student's activity for 2019-2020

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. 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. How to use these data? 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>). For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.

Biological data science courses at UMONS, Belgium: student's activity for 2020-2021

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. 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. How to use these data? 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>). For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.

Biological data science courses at UMONS, Belgium: student's activity for 2018-2019

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. 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. How to use these data? 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>). For any question, send an email at <a href="mailto:sdd@sciviews.org">sdd@sciviews.org</a>.

THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE "HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM" AND ITS IMPACTS

Dataset name: asppl_dataset_v2.csv  Version: 2.0  Dataset period: 06/07/2018 - 01/14/2022 Dataset Characteristics: Multivalued  Number of Instances: 8118 Number of Attributes: 9 Missing Values: Yes Area(s): Health and education  Sources:  <ul> <li> Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);  </li> <li> Brazilian Occupational Classification (CBO) (Brasil, 2022b); </li> <li> National Registry of Health Establishments (CNES) (Brasil, 2022c);  </li> <li> Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).  </li> </ul> Description: The data contained in the asppl_dataset_v2.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021). Table 1: Description of AVASUS dataset features.  <table> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> gender  </td> <td> Gender of the course participant.  </td> <td> Categorical.  </td> <td> Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed) </td> </tr> <tr> <td> course_progress </td> <td> Percentage of completion of the course.  </td> <td> Numerical.  </td> <td> Range from 0 to 100. </td> </tr> <tr> <td> course_evaluation </td> <td> A score given to the course by the participant.  </td> <td> Numerical.  </td> <td> 0, 1, 2, 3, 4, 5 or NaN. </td> </tr> <tr> <td> evaluation_commentary </td> <td> Comment made by the participant about the course. </td> <td> Categorical.  </td> <td> Free text or NaN. </td> </tr> <tr> <td> region </td> <td> Brazilian region in which the participant resides. </td> <td> Categorical.  </td> <td> Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South).  </td> </tr> <tr> <td> CNES </td> <td> The CNES code refers to the health establishment where the participant works. </td> <td> Numerical.  </td> <td> CNES Code or NaN. </td> </tr> <tr> <td> health_care_level </td> <td> Identification of the health care network level for which the course participant works. </td> <td> Categorical. </td> <td> “ATENCAO PRIMARIA”, “MEDIA COMPLEXIDADE”,  “ALTA COMPLEXIDADE”,  and their possible combinations. (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")  </td> </tr> <tr> <td> year_enrollment </td> <td> Year in which the course participant registered. </td> <td> Numerical. </td> <td> Year (YYYY). </td> </tr> <tr> <td> CBO </td> <td> Participant occupation. </td> <td> Categorical.  </td> <td> Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”) </td> </tr> </tbody> </table>   Dataset name: prison_syphilis_and_population_brazil.csv Dataset period: 2017 - 2020 Dataset Characteristics: Multivalued  Number of Instances: 6 Number of Attributes: 13 Missing Values: No Source:  <ul> <li> National Penitentiary Department (DEPEN) (Brasil, 2022d);  </li> </ul> Description: The data contained in the prison_syphilis_and_population_brazil.csv dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. Table 2: Description of DEPEN dataset Features.  <table align="center"> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> Region </td> <td> Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil. </td> <td> Categorical.  </td> <td> Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </td> </tr> <tr> <td> syphilis_2017 </td> <td> Number of syphilis cases in the prison system in 2017. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2017 </td> <td> Normalized rate of syphilis cases in 2017. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> syphilis_2018 </td> <td> Number of syphilis cases in the prison system in 2018. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2018 </td> <td> Normalized rate of syphilis cases in 2018. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> syphilis_2019 </td> <td> Number of syphilis cases in the prison system in 2019. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2019 </td> <td> Normalized rate of syphilis cases in 2019. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> syphilis_2020 </td> <td> Number of syphilis cases in the prison system in 2020. </td> <td> Numerical.  </td> <td> Number of syphilis cases. </td> </tr> <tr> <td> syphilis_rate_2020 </td> <td> Normalized rate of syphilis cases in 2020. </td> <td> Numerical.  </td> <td> Syphilis case rate. </td> </tr> <tr> <td> pop_2017 </td> <td> Prison population in 2017. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> <tr> <td> pop_2018 </td> <td> Prison population in 2018. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> <tr> <td> pop_2019 </td> <td> Prison population in 2019. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> <tr> <td> pop_2020 </td> <td> Prison population in 2020. </td> <td> Numerical.  </td> <td> Population number. </td> </tr> </tbody> </table>   Dataset name: students_cumulative_sum.csv Dataset period: 2018 - 2020 Dataset Characteristics: Multivalued  Number of Instances: 6 Number of Attributes: 7 Missing Values: No Source:  <ul> <li> Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a); </li> <li> Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).  </li> </ul> Description: The data contained in the students_cumulative_sum.csv dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate. Table 3: Description of Students dataset Features.  <table align="center"> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> Region </td> <td> Brazilian region of the course participant. In addition, the sum of the regions, which refers to Brazil. </td> <td> Categorical.  </td> <td> Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.  </td> </tr> <tr> <td> 2018 </td> <td> Number of students enrolled in the course in 2018. </td> <td> Numerical.  </td> <td> Number of students. </td> </tr> <tr> <td> rate_2018 </td> <td> Standardized rate of students in the course in 2018. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2019 </td> <td> Sum of students enrolled in the course in 2019. </td> <td> Numerical.  </td> <td> Number of students. </td> </tr> <tr> <td> rate_2019 </td> <td> Standardized rate of students in the course in 2019. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2020 </td> <td> Sum of students enrolled in the course in 2020. </td> <td> Numerical.  </td> <td> Number of students. </td> </tr> <tr> <td> rate_2020 </td> <td> Standardized rate of students in the course in 2020. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> </tbody> </table>   Dataset name: syphilis_tests_brazil.csv Dataset period: 2017 - 2020 Dataset Characteristics: Multivalued  Number of Instances: 6 Number of Attributes: 9 Missing Values: No Source:  <ul> <li> Brazilian Ministry of Health, through the Outpatient Information System of the Brazilian Health System (SIA/SUS) (Brasil, 2022f); </li> <li> Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).  </li> </ul> Description: The data contained in the syphilis_tests_brazil.csv dataset (see Table 4) originate mainly from the Outpatient Information System of the Brazilian Health System (SIA/SUS). This dataset provides data on the number of tests for syphilis detection by region and year. In addition, it brings a rate that represents the normalized data to compare the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate. Table 4: Description of Syphilis Testes dataset Features.  <table align="center"> <tbody> <tr> <td> Attributes  </td> <td> Description  </td> <td> datatype  </td> <td> Value </td> </tr> <tr> <td> Region </td> <td> Brazilian region where tests for syphilis were performed. In addition, the sum of the regions, which refers to Brazil. </td> <td> Categorical.  </td> <td> Brazil and the Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South. </td> </tr> <tr> <td> 2017 </td> <td> The number of tests for syphilis performed in 2017. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2017 </td> <td> Syphilis testing rate in 2017. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2018 </td> <td> The number of tests for syphilis performed in 2018. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2018 </td> <td> Syphilis testing rate in 2018. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2019 </td> <td> The number of tests for syphilis performed in 2019. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2019 </td> <td> Syphilis testing rate in 2019. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> <tr> <td> 2020 </td> <td> The number of tests for syphilis performed in 2020. </td> <td> Numerical.  </td> <td> The number of tests. </td> </tr> <tr> <td> rate_2020 </td> <td> Syphilis testing rate in 2020. </td> <td> Numerical.  </td> <td> Normalized value. </td> </tr> </tbody> </table>   REFERENCES Brasil (2022a). Ambiente virtual de aprendizagem do sus - avasus. atenção à saúde da pessoa privada de liberdade Available from: <a href="https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114">https://avasus.ufrn.br/local/avasplugin/cursos/curso.php?id=114</a> . Brasil (2022b). Cbo - classificação brasileira de ocupações. Available from: <a href="http://www.mtecbo.gov.br/cbosite/pages/home.jsf">http://www.mtecbo.gov.br/cbosite/pages/home.jsf</a> . Brasil (2022c). Cnes - cadastro nacional de estabelecimentos de saúde. Available from: <a href="http://cnes.datasus.gov.br/">http://cnes.datasus.gov.br/</a> . Brasil (2022d). Departamento penitenciário nacional. levantamento nacional de informações penitenciárias. Available from: <a href="https://www.gov.br/depen/pt-br/servicos/sisdepen">https://www.gov.br/depen/pt-br/servicos/sisdepen</a> . Brasil (2022e). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados">https://www.ibge.gov.br/estatisticas/sociais/populacao/9103-estimativas-de-populacao.html?edicao=31451&t=resultados</a> . Brasil (2022f). Ministério da saúde - sistema de informações ambulatoriais do sus (sia/sus). Available from: <a href="https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/">https://datasus.saude.gov.br/acesso-a-informacao/producao-ambulatorial-sia-sus/</a> . Valentim, J., Oliveira, E. d. S. G., Valentim, R. A. d. M., Dias-Trindade, S., Dias, A. d. P., Cunha-Oliveira, A., et al. (2021). Data report: “health care of persons deprived of liberty” course from brazil’s unified health system virtual learning environment. Frontiers in Medicine 8. doi:10.3389/fmed.2021.742071.   ARTICLE: THE RELEVANCY OF MASSIVE HEALTH EDUCATION IN THE BRAZILIAN PRISON SYSTEM: THE COURSE “HEALTH CARE FOR PEOPLE DEPRIVED OF FREEDOM” AND ITS IMPACTS    AUTHORS: Janaína L. R. S. Valentim1,2, Sara Dias-Trindade2,3, Eloiza da S. G. Oliveira1,4, José A. M. Moreira2,5, Felipe Fernandes1, Manoel Honório Romão1, Philippi S. G. de Morais1, Alexandre R. Caitano1, Aline P. Dias1, Carlos A. P. Oliveira1,4,6, Karilany D. Coutinho1, Ricardo B. Ceccim7, Ricardo A. M. Valentim1   1Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil  2Univ Coimbra, Centre for Interdisciplinary Studies, Coimbra, Portugal 3Univ Coimbra, Centre for Interdisciplinary Studies, Faculty of Arts and Humanities, Coimbra, Portugal 4Multidisciplinary Institute for Human Development with Technologies, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ, Brazil 5Open University (Universidade Aberta), Department of Education and Distance Learning (DEED), Lisbon, Portugal 6International Council for Open and Distance Education, Oslo, Norway 7Postgraduate Program in Education, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil

opencc-by-4.0Oct 2022View details →

Dataset: Economical Accommodations for Neurodivergent Students in Software Engineering Education: Experiences from an Intervention in Four Undergraduate Courses

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. Overall guidelines we followed, including their sources, are contained in guidelines.md. The raw data for the two surveys is contained in the two Excel files survey1.xlsx and survey2.xlsx. Free-text answers have been aggregated by neurodiverse and neurotypical students and anonymised, and are available in the files survey1_freetext_neurodiverse.txt, survey1_freetext_neurotypical.txt, survey2_freetext_neurodiverse.txt, and survey2_freetext_neurotypical.txt. 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.

opencc-by-4.0Jun 2024View details →

German Student Responses to Probability Theory and Statistics Bachelor Course (WuS24): Evaluated with Rubrics

Description: 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.   Dataset Components: <ol> <li>questions.csv: Contains the details of the three questions. <ul> <li>Columns: <ul> <li>question_id: Unique identifier for each question</li> <li>question: The text of the question</li> <li>solution: The reference answer for the question</li> <li>max_score: The maximum score for this question</li> </ul> </li> </ul> </li> <li>rubrics.csv: Contains the grading rubrics for each question. <ul> <li>Columns: <ul> <li>question_id: Unique identifier for each question</li> <li>rubric_id: Unique identifier for each rubric within a question</li> <li>rubric: The rubric phrased as a question</li> <li>score: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li>answers.csv: Contains 90 student answers to the questions. <ul> <li>Columns: <ul> <li>answer_id: Unique identifier for each answer</li> <li>question_id: Unique identifier of the question that is answered</li> <li>answer: The text of the student's answer</li> <li>score: The score associated with fulfilling the rubric</li> </ul> </li> </ul> </li> <li>answer_rubrics.csv: Contains the evaluations of rubrics for each answer. <ul> <li>Columns: <ul> <li>answer_id: The identifier of the answer.</li> <li>question_id: The identifier of the question.</li> <li>rubric_id: The identifier of the rubric for that question.</li> <li>label: Indicates if the rubric crierion is fulfilled for the specific answer (true / false).</li> </ul> </li> </ul> </li> </ol> <pre> Working with the Dataset: The easiest way to work with this dataset is using the class `RubricsDataset` defined in the file `dataloader.py`. Example: </pre> <pre><code>from dataloader import RubricsDataset dataset = RubricsDataset.from_directory("data") 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>

opencc-by-4.0Jun 2024View details →

Geoscience undergraduate major course requirements

This data compilation contains course requirements for undergraduate geoscience majors in Earth Science departments at 50 R1 universities in the United States.  The authors of this compilation (Ken Ferrier, Eva Golos, and Marianne Haseloff at the University of Wisconsin-Madison) collected these data in March-April 2024 (n = 966 courses in total) from each department’s publicly accessible webpage.  These data are organized in four tables, which contain data on the geoscience courses and credits required inside the home department and the cognate courses and credits required outside the home department (e.g., Math, Physics, Chemistry).

opencc-by-4.0Aug 2021View details →

EAW FASTQ files for bioinformatic courses (16S rRNA genes, 2018)

Selection of 6 samples, with 6 Forward (R1) and 6 Reverse (R2) files, including primers. R1 and R2 reads are ca. 300 bp long, and were obtained from Illumina MiSeq technologies, at the FEM facility sequencing platform. The files refer to the16S rRNA gene reads obtained from the analyses carried out on the samples collected and filtered (SterivexTM 0.22 µm) in different areas and depths of Lake Garda on September, 2018 (see EAW_2018_FASTQ_16S_description.docx). Sampling and analyses were carried out in the framework of the project Eco-AlpsWater (ASP569), funded by the Interreg Alpine Space program. A bioinformatic protocol for analyzing these data using DADA2 is available in Zenodo: <pre>https://doi.org/10.5281/zenodo.5232772 </pre>