These occurrence data were encoded from the inventory works carried out by 5 students at the end of their training at the University of Abomey-Calavi, in the context of the accomplishment of their thesis

Being relatively new to the field, electromechanical actuators in aerospace applications lack the knowledge base compared to ones accumulated for the other actuator types, especially when it comes to fault detection and characterization. Lack of health monitoring data from fielded systems and prohibitive costs of carrying out real flight tests push for the need of building system models and designing affordable but realistic experimental setups. This paper presents our approach to accomplish a comprehensive test environment equipped with fault injection and data collection capabilities. Efforts also include development of multiple models for EMA operations, both in nominal and fault conditions that can be used along with measurement data to generate effective diagnostic and prognostic estimates. A detailed description has been provided about how various failure modes are inserted in the test environment and corresponding data is collected to verify the physics based models under these failure modes that have been developed in parallel. A design of experiment study has been included to outline the details of experimental data collection. Furthermore, some ideas about how experimental results can be extended to real flight environments through actual flight tests and using real flight data have been presented. Finally, the roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators is discussed.*

Data DescriptionThe CADDI dataset is designed to support research in in-class activity recognition using IMU data from low-cost sensors. It provides multimodal data capturing 19 different activities performed by 12 participants in a classroom environment, utilizing both IMU sensors from a Samsung Galaxy Watch 5 and synchronized stereo camera images. This dataset enables the development and validation of activity recognition models using sensor fusion techniques.Data Generation ProceduresThe data collection process involved recording both continuous and instantaneous activities that typically occur in a classroom setting. The activities were captured using a custom setup, which included:A Samsung Galaxy Watch 5 to collect accelerometer, gyroscope, and rotation vector data at 100Hz.A ZED stereo camera capturing 1080p images at 25-30 fps.A synchronized computer acting as a data hub, receiving IMU data and storing images in real-time.A D-Link DSR-1000AC router for wireless communication between the smartwatch and the computer.Participants were instructed to arrange their workspace as they would in a real classroom, including a laptop, notebook, pens, and a backpack. Data collection was performed under realistic conditions, ensuring that activities were captured naturally.Temporal and Spatial ScopeThe dataset contains a total of 472.03 minutes of recorded data.The IMU sensors operate at 100Hz, while the stereo camera captures images at 25-30Hz.Data was collected from 12 participants, each performing all 19 activities multiple times.The geographical scope of data collection was Alicante, Spain, under controlled indoor conditions.Dataset ComponentsThe dataset is organized into JSON and PNG files, structured hierarchically:IMU Data: Stored in JSON files, containing:Samsung Linear Acceleration Sensor (X, Y, Z values, 100Hz)LSM6DSO Gyroscope (X, Y, Z values, 100Hz)Samsung Rotation Vector (X, Y, Z, W quaternion values, 100Hz)Samsung HR Sensor (heart rate, 1Hz)OPT3007 Light Sensor (ambient light levels, 5Hz)Stereo Camera Images: High-resolution 1920×1080 PNG files from left and right cameras.Synchronization: Each IMU data record and image is timestamped for precise alignment.Data StructureThe dataset is divided into continuous and instantaneous activities:Continuous Activities (e.g., typing, writing, drawing) were recorded for 210 seconds, with the central 200 seconds retained.Instantaneous Activities (e.g., raising a hand, drinking) were repeated 20 times per participant, with data captured only during execution.The dataset is structured as:/continuous/subject_id/activity_name/ /camera_a/ → Left camera images /camera_b/ → Right camera images /sensors/ → JSON files with IMU data

/instantaneous/subject_id/activity_name/repetition_id/ /camera_a/ /camera_b/ /sensors/ Data Quality & Missing DataThe smartwatch buffers 100 readings per second before sending them, ensuring minimal data loss.Synchronization latency between the smartwatch and the computer is negligible.Not all IMU samples have corresponding images due to different recording rates.Outliers and anomalies were handled by discarding incomplete sequences at the start and end of continuous activities.Error Ranges & LimitationsSensor data may contain noise due to minor hand movements.The heart rate sensor operates at 1Hz, limiting its temporal resolution.Camera exposure settings were automatically adjusted, which may introduce slight variations in lighting.File Formats & Software CompatibilityIMU data is stored in JSON format, readable with Python’s json library.Images are in PNG format, compatible with all standard image processing tools.Recommended libraries for data analysis:Python: numpy, pandas, scikit-learn, tensorflow, pytorchVisualization: matplotlib, seabornDeep Learning: Keras, PyTorchPotential ApplicationsDevelopment of activity recognition models in educational settings.Study of student engagement based on movement patterns.Investigation of sensor fusion techniques combining visual and IMU data.This dataset represents a unique contribution to activity recognition research, providing rich multimodal data for developing robust models in real-world educational environments.CitationIf you find this project helpful for your research, please cite our work using the following bibtex entry:@misc{marquezcarpintero2025caddiinclassactivitydetection, title={CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors}, author={Luis Marquez-Carpintero and Sergio Suescun-Ferrandiz and Monica Pina-Navarro and Miguel Cazorla and Francisco Gomez-Donoso}, year={2025}, eprint={2503.02853}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2503.02853}, }

Different science fair experiences of high school (HS) and post high school (PHS) students depending upon whether or not they received help from scientists.

The “All Activity Trend” shows the number of positive cases that were interviewed (bar graph) and the percentage of those interviewed who reported each select high to moderate exposure activity types (i.e. personal care, dining out, social-related activities, work, travel, gym/fitness, sports, and faith-related events) during their exposure period (trend lines) on a weekly basis.Note: Data subject to change on a daily basis. Data are restricted to positive cases with a completed contact tracing interview. Possible exposure data are collected during the contact tracing interview as self-reported activities occurring within the 2-week period before the date of symptom onset for symptomatic individuals or the date of test sample collection for asymptomatic individuals. Data collection methods were altered starting the week of Dec 11 for gym/fitness and sports, so should not be compared to previous values.* High to Moderate Exposure Activity Types are not exhaustive and include travel, personal care, faith events, work, dining out, social events, gym/fitness, and sports.Data is updated on a weekly basis.

The American Time Use Survey (ATUS) is the Nation's first federally administered, continuous survey on time use in the United States. This multi-year data collection contains information on the amount of time (in minutes) that people spent doing various activities on a given day, including the arts activities, in the years 2003 through 2021. Data collection for the ATUS began in January 2003. Sample cases for the survey are selected monthly, and interviews are conducted continuously throughout the year. In 2021, approximately 9,000 individuals were interviewed. Estimates are released annually. ATUS sample households are chosen from the households that completed their eighth (final) interview for the Current Population Survey (CPS), the nation's monthly household labor force survey. ATUS sample households are selected to ensure that estimates will be nationally representative. One individual age 15 or over is randomly chosen from each sampled household. This "designated person" is interviewed by telephone once about his or her activities on the day before the interview--the "diary day." The ATUS Activity Coding Lexicon is a 3-tiered classification system with 17 first-tier categories. Each of the first-tier categories has two additional levels of detail. Respondents' reported activities are assigned 6-digit activity codes based on this classification system. Additionally, the study provides demographic information--including sex, age, ethnicity, race, education, employment, and children in the household. IMPORTANT: The 2020 ATUS was greatly affected by the coronavirus (COVID-19) pandemic. Data collection was suspended in 2020 from mid-March to mid-May. ATUS data files for 2020 contain all ATUS data collected in 2020--both before and after data collection was suspended. For more information, please visit BLS's ATUS page. The weighting method changed in 2020 to account for the suspension of data collection in early 2020 due to the COVID-19 pandemic. Respondents from 2020 will have missing values for the replicate weights on this data file. The Pandemic Replicate weights file for 2019-20 contains 160 replicate final weights for each ATUS final weight created using the 2020 weighting method. Chapter 7 of the ATUS User's Guide provides more information about the 2020 weighting method.

Collaboratory is a software product developed and maintained by HandsOn Connect Cloud Solutions. It is intended to help higher education institutions accurately and comprehensively track their relationships with the community through engagement and service activities. Institutions that use Collaboratory are given the option to opt-in to a data sharing initiative at the time of onboarding, which grants us permission to de-identify their data and make it publicly available for research purposes. HandsOn Connect is committed to making Collaboratory data accessible to scholars for research, toward the goal of advancing the field of community engagement and social impact.Collaboratory is not a survey, but is instead a dynamic software tool designed to facilitate comprehensive, longitudinal data collection on community engagement and public service activities conducted by faculty, staff, and students in higher education. We provide a standard questionnaire that was developed by Collaboratory’s co-founders (Janke, Medlin, and Holland) in the Institute for Community and Economic Engagement at UNC Greensboro, which continues to be closely monitored and adapted by staff at HandsOn Connect and academic colleagues. It includes descriptive characteristics (what, where, when, with whom, to what end) of activities and invites participants to periodically update their information in accordance with activity progress over time. Examples of individual questions include the focus areas addressed, populations served, on- and off-campus collaborators, connections to teaching and research, and location information, among others.The Collaboratory dataset contains data from 45 institutions beginning in March 2016 and continues to grow as more institutions adopt Collaboratory and continue to expand its use. The data represent over 6,200 published activities (and additional associated content) across our user base.Please cite this data as:Medlin, Kristin and Singh, Manmeet. Dataset on Higher Education Community Engagement and Public Service Activities, 2016-2023. Collaboratory [producer], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2023-07-07. https://doi.org/10.3886/E136322V1When you cite this data, please also include: Janke, E., Medlin, K., & Holland, B. (2021, November 9). To What End? Ten Years of Collaboratory. https://doi.org/10.31219/osf.io/a27nb

A global survey from Capgemini showed that retail companies were lagging behind consumer products enterprises in the use of data. The gap was significant in the automation of processes and in data collecting: only ** percent of retailers automated data collection, against ** percent of consumer goods companies. However, one in **** organizations in both categories reported to have implemented practices involving data engineering, machine learning, and DevOps.

The goal of this study is to measure willingness to participate in passive mobile data collection among German smartphone owners. The data come from a two-wave web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2016, 2,623 participants completed the Wave 1 questionnaire on smartphone use and skills, privacy and security concerns, and general attitudes towards survey research and research institutions. In January 2017, all respondents from Wave 1 were invited to participate in a second web survey which included vignettes that varied the levels of several dimensions of a hypothetical study using passive mobile data collection, and respondents were asked to rate their willingness to participate in such a study. A total of 1,957 respondents completed the Wave 2 questionnaire.

Wave 1

Topics: Ownership of smartphone, mobile phone, PC, tablet, and/or e-book reader; type of smartphone; frequency of smartphone use; smartphone activities (browsing, e-mails, taking photos, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, play games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, statistical office, mobile service provider, app companies, credit card companies, online retailer, and social networks); concerns regarding the disclosure of personal data by the aforementioned institutions; general privacy concern; privacy violated by banks/ credit card companies, tax authorities, government agencies, market research companies, social networks, apps, internet browsers); concern regarding data security with smartphone activities for research (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth); number of online surveys in which the respondent has participated in the last 30 days; Panel memberships other than that of mingle; previous participation in a study with downloading a research app to the smartphone (passive mobile data collection).

Wave 2

Topics: Willingness to participate in passive mobile data collection (using eight vignettes with different scenarios that varied the levels of several dimensions of a hypothetical study using passive mobile data collection. The research app collects the following data for research purposes: technical characteristics of the smartphone (e.g. phone brand, screen size), the currently used telephone network (e.g. signal strength), the current location (every 5 minutes), which apps are used and which websites are visited, number of incoming and outgoing calls and SMS messages on the smartphone); reason why the respondent wouldn´t (respectively would) participate in the research study used in the first scenario (open answer); recognition of differences between the eight scenarios; kind of recognized difference (open answer); remembered data the research app collects (recall); previous invitation for research app download; research app download.

Demography: sex; age; federal state; highest level of school education; highest level of vocational qualification.

Additionally coded was: running number; respondent ID; duration (response time in seconds); device type used to fill out the questionnaire; vignette text; vignette intro time; vignette time.

Connecting language classrooms with 21st-century skills could be the potential framework for enhancing EFL learners’ performance in writing classes. However, investigating whether project-based learning, as a new field within ELT with unique pedagogical affordances, can enhance learners’ writing skills still needs to be improved in the literature. Accordingly, this study aimed to investigate the impact of project-based learning on EFL learners’ writing performance. It sought to determine whether and to what extent project-based learning could enhance writing skills in an EFL context. The study employed a quasi-experimental design with an interrupted pre-test-post-test time series design with single group participants. Twenty-three third-year EFL undergraduate students enrolled in the Advanced Writing Skills I course were selected using a comprehensive sampling method. An essay writing test and interview were used to gather data. The participants of the study were given a series of three problem-solving essay writing tests before and after the intervention, which employed project-based essay writing instruction. In addition, to discover their attitudes toward the impacts of project-based learning and its applications on the ground, three randomly selected students were interviewed at the end of the intervention. The data collected through the tests were analyzed through a one-way repeated measure ANOVA; narration was also used to analyze the qualitative data gathered through interviews. Accordingly, the quantitative data suggested that project-based learning significantly enhances EFL learners’ writing performance. Moreover, interview data showed that students felt optimistic about the impact of project-based learning on their writing performance, idea generation, and cooperation among themselves. Therefore, project-based learning is suggested as another method in ELT writing classes because it enhances learners’ writing via idea generation, data collection, organization, cooperation, and general communication skills. As students work on worthwhile projects, its emphasis on real-world applicability and realistic activities can help them become better writers. Hence, teachers can reinforce the relationship between form and purpose by incorporating a variety of genres and collaborative writing to reflect real-world or professional situations.

The goal of our research is to identify strengths and weaknesses of high school level science fair and improvements that might enhance learning outcomes based on empirical assessment of student experiences. We use the web-based data collection program REDCap to implement anonymous and voluntary surveys about science fair experiences with two independent groups—high school students who recently competed in the Dallas Regional Science and Engineering Fair and post high school students (undergraduates, 1st year medical students, and 1st year biomedical graduate students) on STEM education tracks doing research at UT Southwestern Medical Center. Herein, we report quantitative and qualitative data showing student opinions about the value of science fair. Few students in any group thought that competitive science fair (C-SF) should be required. The most common reasons given for not requiring C-SF were no enjoyment and no interest in competing. On the other hand, student attitudes towards requiring non-competitive science fair (NC-SF) were nuanced and ranged as high as 91%, increasing with student maturation, science fair experience, and STEM track. The most common reasons given for requiring NC-SF were learning scientific thinking skills and research skills. Students opposed to requiring NC-SF most frequently mentioned no enjoyment and no interest in science. Several student comments critical of the fairness of science fair led us to determine possible differences in science fair experiences depending on whether or not students received help from scientists. Those who received help from scientists had an easier time getting their research idea, more access to articles in books and magazines, and less difficulty getting resources. We discuss the idea that two different types of science fairs—competitive science fair with a performance goal orientation and non-competitive science fair with a mastery goal orientation—might be required to promote the broad goal of educating all students about science and engineering.

Abstract (en): The National Household Education Survey (NHES) reports on the condition of education in the United States by collecting data at the household level rather than using a traditional, school-based data collection system. The surveys attempt to address many current issues in education, such as preprimary education, school safety and discipline, adult education, and activities related to citizenship. This survey included three topical survey components. The Early Childhood Program Participation (ECPP) Survey (Part 1) gathered information on the nonparental care arrangements and educational programs of preschool children, such as care by relatives, care by persons to whom they were not related, and participation in day care centers and preschool programs including Head Start. The Before- and After-School Programs and Activities (ASPA) Survey (Part 2) addressed relative and nonrelative care for school-age children during the out-of-school hours, including home schooling as well as participation in before- and/or after-school programs, activities, and self-care. The Adult Education and Lifelong Learning (AELL) Survey (Part 3) collected data such as type of program, employer support, and credential sought for participation in the following types of adult educational activities: English as a second language, adult basic education, credential programs, apprenticeships, work-related courses, and personal interest courses. Some information on work-related informal learning activities was gathered as well. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. National sample of household members in the United States. National sample of households. 2006-01-18 File UG3198.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads.2006-01-18 File QU3198.ALL.PDF was removed from any previous datasets and flagged as a study-level file, so that it will accompany all downloads. The codebooks, user guide, and data collection instrument are provided by the ICPSR as Portable Document Format (PDF) files. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site.

Abstract

The objectives in this ex-post performance evaluation target how the education sub-activity was implemented, if and how it has been sustained, and its perceived outcomes. To meet these objectives, MCC and Social Impact, Inc. (SI), outlined four evaluation questions: 1. What are the current conditions of MCC investments made for the education sub-activity? How do the conditions of MCC investments compare to non-MCC-supported sites? 2. How did the implementation process and/or post-completion maintenance contribute to current conditions of MCC investments? 3. What other factors explain both perceived school-level outcomes and the current conditions of schools? 4. What are the perceived outcomes of the investments in school infrastructure?

To answer the evaluation questions, SI supplemented existing data with two distinct but related data collection activities: first, a school conditions survey to answer Evaluation Question 1, and second, cross-case studies to answer Evaluation Questions 2, 3, and 4.

Overall findings show that on average, MCC schools are in better condition than non-MCC schools, while schools in the Southern zone are in better condition, on average, compared to those in Afram zone and Northern zone.

Qualitative data shows that differences in implementation and maintenance practices had an effect on the current condition of schools. Lack of maintenance funding and community buy-in were identified as major barriers to maintenance. Respondents also highlighted misuse of school facilities by community members (across all zones and schools), harsh weather (primarily in Afram and Northern zones, but all school types), and environment (primarily in low scoring MCC schools) adversely affected school conditions. However, PTAs and SMCs in high scoring MCC and non-MCC schools were more proactive in addressing these factors than those at low-scoring MCC schools. The perception across all zones in all study schools was that improvements in infrastructure positively affected enrollment, attendance, completion and learning.

Geographic coverage

Data was collected from schools in the three zones where MCC interventions took place: Afram Basin, Northern Region and Southern Horticulture Zone.

Analysis unit

School

Universe

All the schools that had been considered for the MCC education intervention.

Kind of data

Sample survey data [ssd]

Sampling procedure

MCC schools: All 221 schools that received MCC funding were included in the study. Non-MCC schools: All 337 remaining schools that (1) had been considered for MCC funding but didn't receive it and (2) that MiDA could provide names for.

Sampling deviation

N/A

Research instrument

Quantitative questionnaire: School Conditions Survey The school conditions survey was a systematic examination of current school infrastructure conditions against international standards, GoG building guidelines, and the MiDA maintenance manual. The enumerators scored different aspects of school infrastructure, including the condition of school grounds, classroom blocks, equipment and furniture, and toilet facilities and polytanks. Ratings of condition were made on a three-point system-poor, average, and good-and each rating was followed up with a photograph of the object being rated.

Qualitative questionnaires: Key informant interviews (KIIs), focus group discussions (FGDs), and community score cards (CSCs) were conducted with parents, students, teachers, school leaders or headmasters, district education officers, individuals responsible for operations and management, construction consultants and implementers, MiDA and MCC staff, and a representative from the Ministry of Education. Questions were asked to understand the processes that may have led to the current conditions of school infrastructure, and perceptions of key stakeholders on the relationship between the investments made and school-level outcomes such as enrollment, attendance, completion, and learning.

Cleaning operations

Data cleaning was done for the school conditions survey. This included: - consistency checks and removing duplicate entries - coding and labeling variables - checks on ratings by enumerators - corrections made to 'Don't Know' ratings where a rating could be given from the photograph

Response rate

MCC schools: All 221 schools surveyed Non-MCC schools: 192 schools out of 337 could be surveyed. This is because many of the schools in the list provided by MiDA were duplicates (already included in the MCC funded list).

Sampling error estimates

N/A

Abstract

Basic information is lacking about many pastoralist areas in the world. As a result, many services, programmes and policies do not effectively address the needs of pastoralist communities. The Government Cooperative Programme (GCP) project GCP/GLO/779/IF “Pastoralists-driven Data Management System”, was based on the idea that pastoralist associations could themselves collect, manage and share data from among their communities. This information could then be used to advocate for better targeted and pastoralist-friendly policies at local, national and international level. The project aimed at strengthening the capacities of pastoral organizations in data collection, analysis and information management, in order to facilitate evidence-based policy decision-making. It was implemented in Argentina, Chad and Mongolia, managed by the Pastoralist Knowledge Hub (PKH), and supported by the Centre de coopération internationale en recherche agronomique pour le développement (Agricultural Research Centre for International Development [CIRAD]).

In Mongolia, the project was implemented by the National Federation of Pastoralist User Groups. An innovative approach for collecting data was developed through close partnership among the stakeholders involved, and was adopted during two successive surveys. The two questionnaires for collecting data on pastoralism were discussed and adapted to the national context, through the contribution of the participants and their deep knowledge of the field. This was one of the most innovative and successful aspects of the project, i.e. the pertinence of the method, as a result of the proactive involvement of the beneficiaries. The first survey, which aimed to identify and describe the pastoralist population, gathered information on 112,957 households. The second survey, which was more in-depth and aimed to assess the pastoralist economy and its contribution to the national economies, was conducted on a sample (based on the results of the first survey) of 1,938 households. As well as demonstrating that pastoralist organizations had the potential to successfully manage data, the surveys revealed the actual contribution of pastoralism to the economy of the country. In particular, they showed that pastoralism contributed to the national economy more than studies usually indicated, as, owing to specific characteristics, such as high levels of self-consumption, pastoralists' contribution to Gross Domestic Product (GDP) was often underestimated . During the project, it emerged that pastoralism could contribute up to 12 percent to the GDP of Mongolia.

Geographic coverage

National coverage

Analysis unit

Households

Universe

Pastoralist Households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The first survey, which aimed to identify and describe the pastoralist population, gathered information on 112,957 households in Mongolia, from different aimags.

With regard to the second survey, 1,938 pastoralist households from the 18 aimags were targeted, based on statistical requirements, as advised by CIRAD. To select the sample households, the NFPUG used maps created from the Global Positioning System (GPS) data collected through the first survey. The sample was made up of four different groups/types of households, based on their animal numbers. This survey involved a smaller number of collectors, only the aimag and sum leaders were involved, and the former gave paper-based questionnaires to the latter, to gather data from after the completed interviews and enter into the Open Foris Collect server. Each collector interviewed 10-15 households, and no more than one per day in areas such as the Gobi Desert, where households lived far apart.

Sampling deviation

For the first survey, out of the 159,219 targeted households at the beginning, 112,957 interviews were completed.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

The survey was conducted in 2 rounds. For the first round, a short questionnaire was submitted to a representative of each household, addressing the following topics: i) households' socio-demographic characteristics; ii) livestock numbers and ownership; iii) land tenure and access; and iv) water access and use.

For the second round, the questionnaire focussed on the economic activity of pastoralists and their contribution to the national GDP. It covers the following topics: i) household identification ii) socio-demographic characteristics iii) livestock herd composition iv) products and final destination v) agricultural production, fishing and hunting activity vi) income and sales vii) household expenses viii) shock and adaptation strategies.

The dataset and accompanying R code provided in the R Markdown file linked to the following manuscript submitted for review: Altwegg et al. "Emerging topics and new directions in statistical ecology".Abstract of linked material:Ecological science relies on robust estimates of the abundance, diversity, and spatial distribution of individuals and species, but these quantities are notoriously difficult to observe directly. Data collected on these quantities not only reflect the ecological processes giving rise to them but also the observation process, which is often biased by factors such as uneven sampling effort or imperfect detection. Furthermore, collecting data according to standard sampling designs is often not possible. Statistical ecology as a research field specialises in developing statistical methods for analysing such complex ecological data. Here, we apply text analysis tools to the abstracts submitted to eight International Statistical Ecology Conferences between 2008 and 2022 to guide a review of recent topics in statistical ecology. Results show that estimating various aspects of demography (including survival, recruitment, abundance, density and movement) and spatial distribution remains a key area of research. The field has benefited from and embraced new data collection methods such as automated recorders and rapidly developing remote sensing techniques. How to integrate data from different sources is a central challenge that spans multiple areas of statistical ecology. The statistical ecology community strives to be inclusive. It also promotes robust data analysis strategies that underpin reproducible research and transparent conservation decisions. With the increasing pressure of human society on nature, we feel statistical ecology is becoming an ever more important research field. Files:Data_Altwegg_et_al_JSTP_2025.csvData_Altwegg_et_al_JSTP_2025.rmd

411 adolescents born in 1978 or 1979 in two socioeconomically different cities are included in the study. Collection of the material has occurred three times for each youth, at 15, 17 and 20 years of age. 103 boys and 106 girls in Uppsala, and 90 boys and 112 girls in Trollhättan participated in the study.

In Part 1 of the longitudinal study, adolescents were examined at 15, 17 and 20.5 years of age. Lifestyle factors such as nutrition, physical activity, smoking and alcohol habits were studied to later be able to determine whether these are associated with health problems in adulthood such as cardiovascular disease, obesity, osteoporosis, diabetes and cancer. The idea is also to analyze whether differences in adolescents’ socio-economic background correlates with lifestyle factors, health and development of disease in adulthood.

Serum and in some cases whole blood from 15, 17 and 20.5 years of age are stored in Uppsala Biobank, in addition to measurement data and questionnaire responses.

There is an opportunity to continue with Part 2 of the study, since the adolescents are now more than 35 years old.

Purpose:

Prospective study of adolescent health, nutrition and physical activity and its importance for future morbidity.

Data base Database that collects data on gender, age, grade at the beginning of the course, grades of the activity and satisfaction with the activity.

An experimental study using randomisation of team work groups was developed. Some student groups developed CBL activities in combination with Escape Room activities, and other student groups developed CBL activities alone. The latter can be considered a control group.

This innovative teaching project was performed by social work students at the University of Zaragoza (Spain). This degree comprises 240 ECTS credits spread out over four years. Specifically, this experimental study was created for "Social Work with Groups" , a compulsory subject taught during the second semester of the second academic year of the Social Work degree programme. It is divided into two parts: the first one is presented from a social psychology perspective, and it is made up of five course curriculum topics. The second one is taught from a social work/social services perspective, which focuses more on the specifics of the profession (four course curriculum topics). This experiment was conducted in February and March 2023, during the delivery of the social psychology part of the course. There are taught five course curriculum topics that fall within the domain of social psychology (psychology of groups). These topics are: 1) group meaning and types; 2) group growth processes, cohesion, conflict, obedience and group violence, group decision-making; 3) group structure: definition, status, roles, norms, group culture; 4) leadership and 5) group characteristics such as communication and empathy.

The participants were students enrolled in the “Social Work with Groups” course at the University of Zaragoza (Spain) during the 2022-2023 academic year. The sample size was 111 students: 56 performed CBL activities with Escape Room activities, and 55 performed CBL activities without Escape Room activities.

The variable outcome of this experimental study was academic performance, assessed by the grade obtained in the mark for CBL activities with a rating from 0 to 10, where the higher score indicated a better performance. This mark showed the number of correct concepts that were identified and extracted from the case. This score was translated to a categorical assessment going from fail (between 0 and 4.9), to pass (between 5.0 and 6.9), to merit (between 7.0 and 8.9), to outstanding (between 9.0 and 10).

Secondary outcomes

The secondary variables were: 1) quantitative and qualitative exam score (on the psychology of groups´ contents) 2) students´ satisfaction with the activity, and 3) time needed for performing the activities.

The academic performance data were collected using the exam score for the subject (psychology of groups´ contents). This exam consisted of 40 multiple-choice questions with three response options, taking the chance factor into account (so marks were deducted for wrong answers). The quantitative rating of each academic score can range between 0 and 10, with a higher score denoting a higher percentage of correct answers. The categorical holistic assessment of achievement goes from fail (between 0 and 4.9), to pass (between 5.0 and 6.9), to merit (between 7.0 and 8.9), to outstanding (between 9.0 and 10).

The data on students´ satisfaction with the activity performed were collected using a self-reporting questionnaire made up of seven statements on the course and teaching methodology used (Gómez-Poyato et al. 2020; Oliván-Blázquez et al. 2022; Olivan-Blázquez et al. 2019), which were answered on a Likert scale from 0 to 4, with 0 meaning not at all and 4 meaning to a great extent. The statements to be evaluated were as follows: the teaching methodology used has encouraged new knowledge acquisition; it has favoured deep learning; it has helped me to think more critically; it has helped me to apply theoretical content to practice; it has helped me to apply theoretical content to assessments; it has helped me to understand concepts better; I believe it is an appropriate teaching methodology. A free response section was also included so that students could express themselves openly.

The data for the time used to carry out the activities were also collected, measured in minutes used for finishing the activities.

Age, gender and university admittance mark data were also obtained in order to to determine if the student groups were in the same conditions regarding these values at the start of the analysis.

411 adolescents born in 1978 or 1979 in two socioeconomically different cities are included in the study. Collection of the material has occurred three times for each youth, at 15, 17 and 20 years of age. 103 boys and 106 girls in Uppsala, and 90 boys and 112 girls in Trollhättan participated in the study.

In Part 1 of the longitudinal study, adolescents were examined at 15, 17 and 20.5 years of age. Lifestyle factors such as nutrition, physical activity, smoking and alcohol habits were studied to later be able to determine whether these are associated with health problems in adulthood such as cardiovascular disease, obesity, osteoporosis, diabetes and cancer. The idea is also to analyze whether differences in adolescents’ socio-economic background correlates with lifestyle factors, health and development of disease in adulthood.

Serum and in some cases whole blood from 15, 17 and 20.5 years of age are stored in Uppsala Biobank, in addition to measurement data and questionnaire responses.

There is an opportunity to continue with Part 2 of the study, since the adolescents are now more than 35 years old.

Purpose:

Prospective study of adolescent health, nutrition and physical activity and its importance for future morbidity.

Abstract

Statistics South Africa (Stats SA) was commissioned by the South African Department of Labour (DoL) to conduct the first Survey of Activities of Young People in 1999. Stats SA was responsible for data collection and processing, while the analysis and report writing was the responsibility of DoL. In thethird quarter of 2010 (Q3:2010) Stats SA conducted the second Survey of Activities of Young People (SAYP) as a supplement to the Quarterly Labour Force Survey (QLFS). However differences in methodologies followed in the two surveys make comparisons difficult. SAYP is a household-based sample survey that collects data on the activities of children aged 7 to 17 years who live in South Africa. This information is gathered from respondents who are members of households living in dwellings that have been selected to take part in the QLFS and have children aged 7–17 years. The survey covers market production activities, production for own final consumption, household chores as well as activities that children engaged in at school. The reference period for some activities is the week preceding the survey interview and for others it is the past twelve months. The specific objectives of SAYP are: • To understand the extent of children’s involvement in economic activities; • To provide users with statistics on the number of working children; • To supply information for the formulation of informed policy to combat child labour within the country; and • To monitor the Child Labour Action Plan of the Department of Labour based on the findings.

Geographic coverage

The survey had national coverage

Analysis unit

Units of analysis in the study were households and individuals

Universe

The sampled population was household members in South Africa. The survey excluded all people in prison, patients in hospitals, people residing in boarding houses and hotels, and boarding schools. Any single person households were screened out in all areas before the sample was drawn. Families living in hostels were treated as households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Survey of Activities of Young People (SAYP) involved two stages. The first stage involved identifying households with children aged 7–17 years during the Quarterly Labour Force Survey (QLFS) data collection that took place in the third quarter of 2010 (Q3:2010). The second stage involved a follow-up interview with children in those households to establish what kind of activities they were involved in and several other aspects related to the activities they engaged in. In Q3:2010, all the QLFS questionnaires were checked for any children aged 7–17 years using the question on age in the first part of the QLFS questionnaire. The screening process for the SAYP was performed to ensure that only households with eligible children were revisited.

Sampling deviation

The non-response adjustment is done through the creation of adjustment classes. The adjustment classes are created using Response Homogeneity Groups (RHGs), where respondents have the same characteristics with non-respondents in the group. The response rate (which is the ratio of responses to all eligible units in the sample) is calculated within each class. The inverse of the response rate (adjustment factor) is calculated within each class, and the result is multiplied by the QLFS 2010 person's weights of the responding units to get the adjusted SAYP person weights for responding units. Children identified as ineligible for SAYP were not considered when calculating weights adjustment. In short, the weights of responding children are inflated to account for eligible children that did not respond during SAYP data collection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Phase one questionnaire covered the following topics: Living conditions of the household, including the type of dwelling, fuels used for cooking, lighting and heating,water source for domestic use, land ownership,tenure and cultivation; demographic information on members of the household, both adults and children. Questions covered the age, gender and population group of each household member, their marital status, their relationships to each other, and their levels of education; migration details; household income; school attendance of children aged 5 -17 years; information on economic and non-economic activities of children aged 5-17 years in the 12 months prior to the survey

Phase two questionnaire The second phase questionnaire was administered to the sampled sub-set of households in which at least one child was involved in some form of work in the year prior to the interview. It covered activities of children in much more detail than in phase one, and the work situation of related adults in the household. Both adults and children were asked to respond.

The data files contain data from sections of the questionnaires as follows:

PERSON: Data from Section 1, 2 and 3 of the questionnaire HHOLD : Data from Section 4 ADULT : Data from Section 5 YOUNGP: Data from Section 6, 7, 8 and 9

This data collection contains information gathered to evaluate certain activities of a number of organizations dedicated to the advancement of law-related education (LRE) in elementary, junior high, and senior high schools. The organizations whose activities were evaluated were (1) the Constitution Rights Foundation, (2) Law in a Free Society, (3) the National Street Law Institute, (4) the American Bar Association's Special Committee on Youth Education for Citizenship, (5) the Children's Legal Rights Information and Training Program, and (6) the Phi Alpha Delta Committee for Juvenile Justice. The evaluation research dealt primarily with two types of issues: (1) the degree of increase in awareness of and receptivity toward LRE among the nation's educators, juvenile justice, and other related professionals, as well as the degree of institutionalization of LRE in certain targeted states (i.e., California, Michigan, and North Carolina), and (2) the degree to which LRE could produce changes in students' knowledge of and attitudes about the law, and reduce juvenile delinquency (measured both by self-reported delinquency rates and by attitudes previously shown to be correlated with delinquent behavior). In 1981 (Part 1) and again in 1982 (Part 2), questionnaires were mailed to a sample of professionals in state educational organizations as well as to elementary and secondary school principals, juvenile justice specialists, juvenile and family court judges, police chiefs, and law school deans. Respondents were asked whether they had heard of the various projects, what they thought of LRE in terms of its impact on students and usefulness in the curriculum, whether LRE should be required, what type of publicity had contributed to their awareness of LRE, and the degree of involvement they would be willing to have in promoting or developing LRE programs. In a second component of the study, primary and secondary school students were selected for an impact evaluation of the LRE activities run by the six organizations under evaluation. Questionnaires were administered to students during academic years 1982-1983 (Part 3) and 1983-1984 (Part 4), before and after participating in LRE courses offered by the programs under evaluation. Control students (not taking LRE courses) were also used for the comparisons. The questionnaires tested the knowledge, attitudes (measuring such factors as isolation from school, delinquent peer influence, negative labeling, and attitudes toward violence), and self-reported delinquency of school children. Demographic information collected about the student respondents includes sex, age, race, grade in school, and grade-point average.