This is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data. Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set. Exceptions to this are: Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence. Data from Gateway to Research is made available under an Open Government Licence (Version 3.0). Contents Survey data & analysis: esrc_data-survey-analysis-data.zip Other data: esrc_data-other-data.zip Transcripts: esrc_data-transcripts.zip Data Management Plan: esrc_data-dmp.zip Survey data & analysis The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data. The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled. The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. A pdf copy of the survey questions is available on GitHub. The survey data has been decoupled into: survey-results-key.csv - maps a question number and the responses to the actual question values. q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16). q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23). q17-institutions.csv - the institution/location of the respondent (Q17). q18-funding.csv - funding sources within the last 5 years (Q18). Please note the code that has been used to do the analysis will not run with the decoupled survey data. Other data files included CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered. DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021. projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence. locations.csv - latitude and longitude for the institutions in the cleaned locations. subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot. topics.csv - topic classification for the ESRC projects for the 24th February data snapshot. Interview transcripts The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts: 1269794877.md 1578450175.md 1792505583.md 2964377624.md 3270614512.md 40983347262.md 4288358080.md 4561769548.md 4938919540.md 5037840428.md 5766299900.md 5996360861.md 6422621713.md 6776362537.md 7183719943.md 7227322280.md 7336263536.md 75909371872.md 7869268779.md 8031500357.md 9253010492.md Data Management Plan The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank sponsored Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect data on the conditions under which South Africans live in order to provide policymakers with the data necessary for development planning. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

The survey had national coverage

Analysis unit

Households and individuals

Universe

The survey covered all household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn for the households in ESDs.

Kind of data

Sample survey data

Mode of data collection

Face-to-face [f2f]

Research instrument

The main instrument used in the survey was a comprehensive household questionnaire. This questionnaire covered a wide range of topics but was not intended to provide exhaustive coverage of any single subject. In other words, it was an integrated questionnaire aimed at capturing different aspects of living standards. The topics covered included demographics, household services, household expenditure, educational status and expenditure, remittances and marital maintenance, land access and use, employment and income, health status and expenditure and anthropometry (children under the age of six were weighed and their heights measured). This questionnaire was available to households in two languages, namely English and Afrikaans. In addition, interviewers had in their possession a translation in the dominant African language/s of the region.

In addition to the detailed household questionnaire, a community questionnaire was administered in each cluster of the sample. The purpose of this questionnaire was to elicit information on the facilities available to the community in each cluster. Questions related primarily to the provision of education, health and recreational facilities. Furthermore there was a detailed section for the prices of a range of commodities from two retail sources in or near the cluster: a formal source such as a supermarket and a less formal one such as the "corner cafe" or a "spaza". The purpose of this latter section was to obtain a measure of regional price variation both by region and by retail source. These prices were obtained by the interviewer. For the questions relating to the provision of facilities, respondents were "prominent" members of the community such as school principals, priests and chiefs.

A literacy assessment module (LAM) was administered to two respondents in each household, (a household member 13-18 years old and a one between 18 and 50) to assess literacy levels.

Data appraisal

The data collected in clusters 217 and 218 are highly unreliable and have therefore been removed from the dataset currently available on the portal. Researchers who have downloaded the data in the past should download version 2.0 of the dataset to ensure they have the corrected data. Version 2.0 of the dataset excludes two clusters from both the 1993 and 1998 samples. During follow-up field research for the KwaZulu-Natal Income Dynamics Study (KIDS) in May 2001 it was discovered that all 39 household interviews in clusters 217 and 218 had been fabricated in both 1993 and 1998. These households have been dropped in the updated release of the data. In addition, cluster 206 is now coded as urban as this was incorrectly coded as rural in the first release of the data. Note: Weights calculated by the World Bank and provided with the original data are NOT updated to reflect these changes.

The IT Portfolio dataset contains data for all Information Technology (IT) related projects required to be oversighted or governed as part of the project oversight process (Stage Gate). The purpose of the project oversight process is to provide IT governance and oversight to agency technology projects to ensure consistent governance and management of technology initiatives within the State of Oregon. Questions about projects within this dataset should be directed to the Agency managing the project.

Abstract

In 2007-2008 a multi-topic household survey, the Timor Leste Living Standards Survey (LSS-2) was conducted in East Timor with the main objectives of developing a system of poverty monitoring and supporting poverty reduction, and to monitor human development indicators and progress toward the Millennium Development Goals. The LSS-3 extension survey was designed to re-visit one third of the households interviewed under the LSS-2 to explore different facets of household welfare and behaviour in the country, while also being able to make use of information collected in the LSS-2 survey for analytic purposes. The four new topics investigated in the extension survey are:

• Risk and Vulnerability: This section is designed to help us understand the dimensions and sources of household-level vulnerability to uninsured risks in Timor Leste, and the efficacy and welfare effects of various risk-management strategies (prevention, mitigation, coping) and mechanisms (private as well as public, formal as well as informal) households do (or do not) have access to. The work in Timor Leste is part of a program of analytic work and policy dialogue throughout the EAP region, more information on which can be found on the World Bank website.
• Land Degradation and Poverty: This section of the questionnaire is designed to identify proximate causes of deforestation through land use patterns and links with poverty; understand strengths and failures of common land resource management institutions (property rights, enforcement); understand the impact of the Siam Weed problem on household welfare.
• Justice for Poor: The Justice for the Poor/Access to Justice (J4P/A2J) module of the survey will serve mainly as an initial diagnostic for project development in the country. The topics we would be interested in covering would be Dispute Processing/Resolution; Social Legal Norms and Perceptions of Efficiency in Government (Local, Sub-District, District and National level).
• Access to Financial Services: The financial service work has the following two objectives: (i) to collect data on access to and use financial services (savings and credit), both formal and informal, and (ii) assess the quality of information on access to financial services obtained from head of households vs. from all adults - i.e. is there a bias introduced by not asking all household members, do the characteristics of the head or the household affect this (gender, age, nuclear family, urban, education levels, wealth, etc.).

Geographic coverage

National coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN FOR THE 2008 EXTENSION SURVEY

Sampling for the LSS-3 Extension survey was a sub-sample of the original LSS-“ sample. The LSS-2 field work was divided into 52 "weeks", with each week being a random subset of the total sample. The sub-sample was chosen by randomly selecting 19 weeks from the original field work schedule. Each week contained seven Primary Sampling Units (PSUs) for a total of 133 PSUs. In each PSU the teams were to interview 12 of the original 15 households, with the remaining three to serve as replacements. The total nominal sample size was thus 1596.

Additional interviews: Following the collection and initial analysis of the data, it was determined that data from one district, Manatuto, and partially from another district, Oecussi, were of insufficient quality in certain modules. Therefore, it was decided to repeat the survey in another 25 PSUs of these two districts - six in Manatuto, and 19 in Oecussi. The additional PSUs chosen were randomly selected within the two districts from the remaining non-panel PSUs in the original LSS-2 sample.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

DATA CLEANING

The LSS-3 had a significant number of responses in which the response is "other". In general, if the response clear fit into a pre-coded response category, it was recoded into that category during the cleaning and compilation process. Some responses where additional information was provided were not recoded even though they clearly fit into pre-coded categories. For example, agriculture project" would be recoded into the "agriculture" category, while "community garden" would not. Data users can either use the additional information, or re-code into categories as they see fit. Potential Data Quality Issues in 2008 Extension survey

Data appraisal

Potential Data Quality Issues in 2008 Extension survey

Agriculture: Similarly, to the individual roster of the previous section, the plots listed in the previous survey are listed on the pre-printed cover page and all changes noted. The agricultural section, similarly, to the other sections, suffers from problems with open-ended questions. This is particularly the case for the question asking what community restrictions are placed on the clearing of forest land (section 2d). The translation from the original question was vague (using the Tetun word for "boundary" for "restriction,") and therefore many of the responses relate to physical boundaries on the land, such as stone walls and tree lines. Additionally, the translation of all answers from Tetun into English is imperfect, and those wishing to use this information for analytical purposes are advised to also refer to the original Tetun. Analysts should be careful in using the data from the open ended questions because of translation problems. Also, it was noted during the training and field work that many interviewers had significant difficulties understanding definitions with some of the land management and investment questions. In general, however, all agricultural data may be used for analysis, sampling weights w3.

Finance: It should be noted that the quality of the data for the finance experiment (comparing the knowledge of the household head to that of other household members) was not sufficient for the experiment to be deemed a success. Subsequent spot-checking revealed that in many cases, interviewers asked the household head about the financial activities of various household members instead of asking them directly. Therefore, this data should only be used to measure the access to finance at the household level. The finance sections were not repeated during the additional interviews in the replacement PSUs. Sampling weights w1 should be used when doing any analysis with this data.

Shocks and Vulnerability: It was determined following the initial round of data collection that the shocks and vulnerability module had some issues with uneven interview quality. Two reasons were listed as potential causes of the data quality issues: (1) fundamental inability to adequately translate both the word and concept of a "shock" into the Timorese context, and (2) incomplete / questionable responses to the health shock questions in particular. Analysis for health shocks should drop the "questionable" households and use the "re-interview" households, sampling weights w2.

Justice for the Poor: Similar to the shocks and vulnerability module, the justice module included a long series of follow up questions if the household indicated having experienced a dispute during the recall period. Again, the number of disputes experienced by the household seemed extremely low compared to expectations. This was particularly a problem with the Manatuto district in which no disputes were recorded during the first set of TLSLS2-X interviews. Analysis for the disputes section of the justice module should drop the "questionable" households and use the "re-interview" households, sampling weights w2. The justice model also has a number of instances in which the specifications for "other" were not recorded. Every effort was made to ensure this data was as complete as possible, but gaps do remain. Also, data users should use caution when using the imputed rank variable in section 5D. The rank in terms of importance was not explicitly captured in the data entry software, and the rankings therefore had to be imputed from the order they were listed in the original data entry. Inconsistencies may exist in this variable.

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

The purpose of the ASSISTments Replication Study is to conduct a replication study of the impact of a fully developed, widely adopted intervention called ASSISTments on middle school student mathematics outcomes. ASSISTments is an online formative assessment platform that provides immediate feedback to students and supports teachers in their use of homework to improve math instruction and learning. Findings from a previous IES-funded efficacy study, conducted in Maine, indicated this intervention led to beneficial impacts on student learning outcomes in 7th grade. The current study examined the impacts of this intervention with a more diverse sample and relied on trained local math coaches (instead of the intervention developers) to provide professional development and support to teachers. Participating schools (and all 7th grade math teachers in the school) in this study were randomly assigned to either a treatment or control group. Teachers participated in the project over a two year period, the 2018-19 school year and the 2019-20 school year. The 2018-19 school year was to serve as a ramp-up year. Data used in the final analysis was collected during the second year of the study, the 2019-20 school year. The data contained in this project is primarily from the 2019-20 school year and includes student ASSISTments usage data, teacher ASSISTments usage data, student outcome data, and teacher instructional log data. Student outcome data is from the online Mathematics Readiness Test for Grade 8 developed by Math Diagnostic Test Project (MDTP). The teacher instructional log had teachers to answer questions about their daily instructional practices over the span of 5 consecutive days of instruction. They were asked to participate in 3 rounds of logs over the course of the 2019-2020 school year. Student and teacher usage data of ASSISTments were collected automatically as they used the system. The usage data was limited to treatment group only. Other data (outcome data, teacher instructional log data) were collected from both treatment and control groups.

About Dataset The dataset contains information about sales transactions, including details such as the customer's age, gender, location, and the products sold. The dataset includes data on both the cost of the product and the revenue generated from its sale, allowing for calculations of profit and profit margins. The dataset includes information on customer age and gender, which could be used to analyze purchasing behavior across different demographic groups. The dataset likely includes both numeric and categorical data, which would require different types of analysis and visualization techniques. Overall, the dataset appears to provide a comprehensive view of sales transactions, with the potential for analysis at multiple levels, including by product, customer, and location. But it does not contain any useful information or insights for decision makers. - After understanding the dataset. - I cleaned it and add some columns & calculations like (Net profit, Age Status). - Making a model in Power Pivot, calculate some measures like (Total profit, COGS, Total revenues) and Making KPIS Model. - Then asked some questions: About Distribution What are the total revenues and profits? What is the best-selling country in terms of revenue? What are the five best-selling states in terms of revenue? What are the five lowest-selling states in terms of revenues? What is the position of age in relation to revenues? About profitability What are the total revenues and profits? Monthly position in terms of revenues and profits? Months position in terms of COGS? What are the top category-selling in terms of revenues & Profit? What are the three best-selling sub-category in terms of profit? About KPIS Explain to me each salesperson's position in terms of Target

• Then Answering that questions, analysis the data and Visualize with Dashboards.

The self-assessment response data were collected through a questionnaire survey method, targeting primarily project managers/leaders (self-assessment), and the results were tested and validated by another independent group of followers (project engineers, project supervisors, team members and senior managers who worked with project managers) - Dataset 2. The follower's assessment allowed them to report leadership behavioural practices as observed, making the research more effective and the study's results verifiable. The survey participants were selected to align with the research objectives and questions, aiming to gather perception data from qualified project managers/leaders with experience, expertise, and knowledge in managing building construction projects. The questionnaire survey method was selected for this study because of the following reasons: they require less time and energy to administer, incur low cost, offer anonymity because the respondents' names are not required on the completed questionnaires, and respondents have adequate time to provide well thought out answers to the questions. Respondents were asked to provide information on various sections in the questionnaire based on the most recently completed project/s: (1) demographics, (2) project characteristics (type, size, complexity, and site structure [single/multiple], and (3) their perceptions on questions related to (1) leadership practices, (2) communication, (3) relationship management, (4) conflict management, and (5) project success. The data collected for this study are primary data, which are firsthand data collected personally by the researcher. The data analysis was conducted using the Statistical Package for Social Sciences (SPSS-29.0.2.0), involving exploratory factor analysis and multivariate/stepwise regression analysis to identify key project success factors in the construction industry.

About this dataset

Context

The dataset tabulates the population of Sale City by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Sale City across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 58.09% of total population being female. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Sale City is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Sale City total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Sale City Population by Race & Ethnicity. You can refer the same here

The MEDIATIZED EU project aims to study how the media discourses are constructed to foster or hamper the European project and how they resonate among the public by focusing on the elite-media-public triangle. The research was conducted in seven target countries: Ireland, Belgium, Estonia, Spain, Portugal, Hungary and Georgia.

This dataset is part of the integration of the MEDIATIZED EU project research data into the EU’s Open Research Data Pilot. In accordance with the Data Management Plan, public opinion survey data were deemed suitable for being openly shared through ORDP to be accessible and of use to other academic researchers in Europe and worldwide. Quantitative data derived from surveys was deemed suitable, with the only concerns being the heterogeneous nature of some of the survey questions in each target country.

The aim of the population surveys was to investigate public opinion about the media and elites in their country and the EU and how they interpret elite and media discourses on Europeanisation and European integration. The merged database allows the project participants and other researchers to compare their national research results with phenomena in other participating countries.

This dataset contains a subset of integrated survey data including those survey questions where comparative data was available. The final deliverable contains this subsection of the survey data which has been weighted and cleaned, in .SAV and .XLS formats, and provides the requisite codebook for the dataset.

For more on the MEDIATIZED EU project, visit our website at mediatized.eu or view our CORDIS profile at: https://cordis.europa.eu/project/id/101004534

This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no 101004534. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.

Since the beginning of the 1960s, Statistics Sweden, in collaboration with various research institutions, has carried out follow-up surveys in the school system. These surveys have taken place within the framework of the IS project (Individual Statistics Project) at the University of Gothenburg and the UGU project (Evaluation through follow-up of students) at the University of Teacher Education in Stockholm, which since 1990 have been merged into a research project called 'Evaluation through Follow-up'. The follow-up surveys are part of the central evaluation of the school and are based on large nationally representative samples from different cohorts of students.

Evaluation through follow-up (UGU) is one of the country's largest research databases in the field of education. UGU is part of the central evaluation of the school and is based on large nationally representative samples from different cohorts of students. The longitudinal database contains information on nationally representative samples of school pupils from ten cohorts, born between 1948 and 2004. The sampling process was based on the student's birthday for the first two and on the school class for the other cohorts.

For each cohort, data of mainly two types are collected. School administrative data is collected annually by Statistics Sweden during the time that pupils are in the general school system (primary and secondary school), for most cohorts starting in compulsory school year 3. This information is provided by the school offices and, among other things, includes characteristics of school, class, special support, study choices and grades. Information obtained has varied somewhat, e.g. due to changes in curricula. A more detailed description of this data collection can be found in reports published by Statistics Sweden and linked to datasets for each cohort.

Survey data from the pupils is collected for the first time in compulsory school year 6 (for most cohorts). Questionnaire in survey in year 6 includes questions related to self-perception and interest in learning, attitudes to school, hobbies, school motivation and future plans. For some cohorts, questionnaire data are also collected in year 3 and year 9 in compulsory school and in upper secondary school.

Furthermore, results from various intelligence tests and standartized knowledge tests are included in the data collection year 6. The intelligence tests have been identical for all cohorts (except cohort born in 1987 from which questionnaire data were first collected in year 9). The intelligence test consists of a verbal, a spatial and an inductive test, each containing 40 tasks and specially designed for the UGU project. The verbal test is a vocabulary test of the opposite type. The spatial test is a so-called ‘sheet metal folding test’ and the inductive test are made up of series of numbers. The reliability of the test, intercorrelations and connection with school grades are reported by Svensson (1971).

For the first three cohorts (1948, 1953 and 1967), the standartized knowledge tests in year 6 consist of the standard tests in Swedish, mathematics and English that up to and including the beginning of the 1980s were offered to all pupils in compulsory school year 6. For the cohort 1972, specially prepared tests in reading and mathematics were used. The test in reading consists of 27 tasks and aimed to identify students with reading difficulties. The mathematics test, which was also offered for the fifth cohort, (1977) includes 19 assignments. After a changed version of the test, caused by the previously used test being judged to be somewhat too simple, has been used for the cohort born in 1982. Results on the mathematics test are not available for the 1987 cohort. The mathematics test was not offered to the students in the cohort in 1992, as the test did not seem to fully correspond with current curriculum intentions in mathematics. For further information, see the description of the dataset for each cohort.

For several of the samples, questionnaires were also collected from the students 'parents and teachers in year 6. The teacher questionnaire contains questions about the teacher, class size and composition, the teacher's assessments of the class' knowledge level, etc., school resources, working methods and parental involvement and questions about the existence of evaluations. The questionnaire for the guardians includes questions about the child's upbringing conditions, ambitions and wishes regarding the child's education, views on the school's objectives and the parents' own educational and professional situation.

The students are followed up even after they have left primary school. Among other things, data collection is done during the time they are in high school. Then school administrative data such as e.g. choice of upper secondary school line / program and grades after completing studies. For some of the cohorts, in addition to school administrative data, questionnaire data were also collected from the students.

Project Title: Add title here

Project Team: Add contact information for research project team members

Summary: Provide a descriptive summary of the nature of your research project and its aims/focal research questions.

Relevant publications/outputs: When available, add links to the related publications/outputs from this data.

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Data collection details: 1. When was your data collected? 2. How were your participants sampled/recruited?

Sample information: How many and who are your participants? Demographic summaries are helpful additions to this section.

Research Project Materials: What materials are necessary to fully reproduce your the contents of your dataset? Include a list of all relevant materials (e.g., surveys, interview questions) with a brief description of what is included in each file that should be uploaded alongside your datasets.

List of relevant datafile(s): If your project produces data that cannot be contained in a single file, list the names of each of the files here with a brief description of what parts of your research project each file is related to.

Data codebook: What is in each column of your dataset? Provide variable names as they are encoded in your data files, verbatim question associated with each response, response options, details of any post-collection coding that has been done on the raw-response (and whether that's encoded in a separate column).

Examples available at: https://www.thearda.com/data-archive?fid=PEWMU17 https://www.thearda.com/data-archive?fid=RELLAND14

TitleUnravelling formative decision-making: How formative assessment is used to inform teachers’ actions in the classroom AbstractFormative assessment is used to make better founded decisions about the next steps to take in teaching and learning. In formative assessment teachers collect information on student learning to help them decide what next steps in teaching and learning will best suit students’ learning needs. However, how teachers come from this collection of information about student learning to a well-informed formative decision about the follow-up usually stays implicit. This multiple case study focused on bringing the invisible processes that are involved in formative decision-making to the surface. Through journal-writing and interviews with teachers from secondary education, who implemented formative assessment in their classrooms, different pathways were found in which formative decision-making occurs. Furthermore, the outcomes of the current study reveal that formative assessment is not an isolated strategy. The decisions teachers make based on formative assessment are always also embedded in and supported by knowledge and beliefs teachers already have about the context, content, learning and learners. Description of the data included This study focused on five teachers and their formative assessment plans, which were designed and would be implemented during the project. Each formative assessment plan consisted of multiple checkpoints and follow-ups. Checkpoints are the planned moments where teachers want to collect and analyze data on all student learning with regards to the learning objectives. Each checkpoint is the starting point for making a formative decision and informed follow-up. A case in this study is the process, line of events as described by the teachers, that starts with a checkpoint in the formative assessment plan and ends with the chosen follow-up. In this study eighteen cases from five formative assessment plans were included. Table 1 presents where the eighteen cases originated from: Table 1: Overview of the cases included in this study Formative assessment plan - teacher 1: 3 cases - teacher 2: 2 cases - teacher 3: 5 cases - teacher 4: 6 cases - teacher 5: 2 cases - total: 18 cases Interview transcipts - teacher 1: 3 cases - teacher 2: 1 case - teacher 3: 5 cases - teacher 4: 4 cases - teacher 5: 1 case - total: 14 cases Journal form - teacher 1: 3 cases - teacher 2: 2 cases - teacher 3: 5 cases - teacher 4: 6 cases - teacher 5: 1 case - total: 17 cases The teachers filled in journal forms for each case and were interviewed about each case. Sometimes journal forms or interview included multiple cases. The data therefore consist of: 1. 17 anonymized journal forms Parallel with the implementation of their formative assessment plan, the five teachers filled in a journal form after each checkpoint. Questions in this form were: a. Did the information you collected at this checkpoint lead you to know where the students are in their learning with regards to the intended learning goal(s)? b. What do you know now thanks to this checkpoint? c. Wat are possible explanations for the outcomes of this checkpoint? d. What are possible follow-ups that match the outcomes of the checkpoint? e. Which follow-up do you choose? f. Why is this a good/the best follow-up? 2. Anonymized Transcripts of 14 interviews with five teachers The interviews were planned after a checkpoint took place. Starting point for the interviews were the completed journal forms for each checkpoint. Based on the journal forms, the interviewer asked the teacher clarification questions about the answers in the form. For example, questions like how do you know that these are the outcomes or how do you know the reasons for these outcomes, how do you know this is the best follow-up? After clarification, three additional questions followed for all teachers: a. What other information did you use to choose a follow-up? b. How do you look back at the chosen follow-up? c. Do you feel that the components in the formative assessment plan (checkpoints and follow-up are both a part of this plan) have led to better founded/informed decisions? 1. If so, how do you know and what contributed to it? 2. If not, what is required for this?

12 months worth of sales data. The data contains hundreds of thousands of electronics store purchases broken down by order Number and it's date, product type and it's quantity, cost and purchase address

Column descriptors : Each dataset has:

Order Id: The id of the order.

Product: The type of the product bought.

Quantity Ordered: How many of the product was ordered.

Price Each: The price of a single item.

Order Date: When the product was ordered including the year,month, day, hours and minutes

Purchase Adrress: Where to deliver the order.

Questions: Question 1: What was the best month for sales? How much was earned that month?

Question 2 : Which city had the highest number of sales?

Question 3: What time should we display advertisements to maximize the likelihood of purchasses and Sales?

Question 4: What products are most often sold together?

Question 5: Which product was sold the most?

TitleComparing Practical Skills Teaching by Near-Peers and Faculty Purpose Near-peer teaching is a vital teaching resource in most medical schools, but we know little about the comparative benefits of near-peers and faculty teaching or the learning mechanisms that underlie them. This study explored near-peers’ and students’ perceptions of differences between the way near-peers and faculty teach practical skills. Methods Using qualitative methodology, the authors conducted 4 focus groups with near-peers (n=22) and 4 focus groups with students (n=26, years 3-6) at the University of Bern, Switzerland, between Sept-Dec 2022. All participants recently participated in near-peer skills training. Vignettes of typical teaching situations guided the focus group discussions. The reflexive thematic analysis was both inductive and deductive; Cognitive Apprenticeship teaching methods informed the deductive analysis. Results Near-peers were perceived to establish a safer learning climate than faculty, lowering the threshold to ask questions. Near-peer teaching was oriented toward the formal curriculum and students’ learning needs, resulting in more tailored explanations focused on exam-relevant content. Faculty oriented their teaching towards clinical practice, which helped students transition to clinical practice but could overwhelm novice students. Faculty better stimulated students to think critically about unanswered questions and how to fill their competence gaps. Conclusions Skills teaching by near-peers and faculty differed in teaching climate and orientation. Near-peers saw students as “learners,” focused on the learning climate and on students’ needs. Faculty saw students as “future physicians” and facilitated the transition from curricular learning to clinical practice. Curricular design should capitalize on the complementary benefits of near-peer and faculty skills instructors, and seek to get the best of both worlds. Explanation of all the instruments used in the data collection (including phrasing of items in surveys) Baseline Questionnaire for near-peers and students, focus group guide using vignettes Explanation of the data files: what data is stored in what file? The study contained 8 transcrips of focus groups and one questionnaire with variants for near-peers and students: Folder name -.> Description Baseline Questionnaire_Students -> Questions in Baseline questionnaire for students (in German) Baseline Questionnaire_Peers -> Questions in Baseline questionnaire for Peers (in German) Participant Information -> Contains results from Baseline questionnaire Transcripts FG 1-8 -> Transcripts of the 8 focus groups In case of qualitative data: description of the structure of the data files The Transcript files contain the original focus group interview data in German. The Participant information sheet contain demographic data of the focus group participants

This data provides the basis for the report titled

"Ready for integrated sustainable agricultural land management?

Are practitioners in archaeology and agriculture informed, willing, enabled, and motivated to change how they work with remote and near-surface sensing data to collaboratively address contemporary challenges in sustainable agricultural land management? "

Data were collected in compliance with the University of Glasgow's Research Ethics Policy (Application #100200154).

As stated in the Methods section of this report:

"The participatory survey was conducted between May 2021 and October 2022.

Location: The preponderance of stakeholders engaged with are professional practitioners or researchers based in the UK, Belgium, Italy, Cyprus, Spain and France. Sessions occurred remotely (online/phone), as well as on site, during workshops at the University of Glasgow, the Dalswinton Estate, Dumfries, and Manor Farm, Yedingham.

Participants

Selection: A sub-group of 51 high-level participants were selected from a greater network of 86 stakeholders who were engaged with during the ipaast project.

Sector: Farmers, researchers, heritage managers, geophysicists, remote sensing specialists, statisticians, soil scientists, service providers, sensor developers, and data archivists, who all deal directly, or indirectly with datasets relating to the measurement of soil and/or plant properties (physical, chemical, microbial) were represented (Table 1)

Expertise: Engagement with mid- to late- career specialists was prioritised, with many participants having over 20 years of experience and most having over 10 years of experience (including time during the PhD).

Interview method

Engagement with stakeholders was primarily through one-to-one interviews and structured workshop discussions, conducted either in person, or remotely over video conference or phone. In some instances, participants provided written input (see Table 2 summary). Follow-up interviews or written exchanges were used to clarify or continue discussions when required. A semi-structured approach to interviews and discussions was preferred, with a mix of general questions (see sample questions), as well as questions specifically tailored to the participants specialist background and experience.

Sample Questions:

• What types of sensing data do you use/collect?

• Where/how do you access/collect these data?

• What are your main aims/applications in using or collecting these data?

• How often do you access/collect, or anticipate accessing/collecting, these data to be useful to you?

• What spatial resolution is necessary for these data to be useful to you?

• What, if anything, would encourage/discourage you from sharing your data?

• What kinds of additional data types or additional information (metadata) might help you to better understand and use data which you have previously collected or received?

• What do you see as the main impacts, if any, of ecosystem service frameworks and/or recent changes to rural/environmental regulations on your work?

• What attitudes to sensing data do you see from other stakeholders in rural affairs?

Documentation: Where viable, interviews and workshop discussions were recorded and transcribed; alternatively, notes were made during engagement by either the interviewer and/or dedicated participant observers (e.g. at workshops). Where notes were used, specific quotes and summary reports were checked with the participants for accuracy. "

This project involves conducting and analyzing listening tests using modified webMUSHRA software to evaluate the perceptual accuracy of simulated acoustic environments. The code is structured into five main directories: Docs, containing ethics documents; Modified webMUSHRA Software, including testing code and configurations run with Docker for paired_comparison and subjective_eval tests; Results, storing both raw and processed data from the listening tests; Samples, providing original and convolved audio files with real and simulated Room Impulse Responses (RIRs); and Utils, featuring scripts for generating sine sweeps, convolving and clipping audio, and performing basic statistical analysis. For questions, contact b.christensen@student.tudelft.nl.

This dataset contains all milestone information entered by Federal agencies into the Permitting Dashboard. Rows represent single milestones within an individual environmental review or authorization (action). For a full description of all fields in the dataset, see the Data Dictionary. Questions specific to the dataset can be directed to the email listed below. For questions on specific projects, please use the contact information listed on the project page on the The Permitting Dashboard.

The Project Approval Boundary spatial data set provides information on the location of the project approvals granted for each mine in NSW by an approval authority (either NSW Department of Planning or local Council). This information may not align to the mine authorisation (i.e. mine title etc) granted under the Mining Act 1992. This information is created and submitted by each large mine operator to fulfill the Final Landuse and Rehabilitation Plan data submission requirements required under Schedule 8A of the Mining Regulation 2016. \r \r The collection of this spatial data is administered by the Resources Regulator in NSW who conducts reviews of the data submitted for assessment purposes. In some cases, information provided may contain inaccuracies that require adjustment following the assessment process by the Regulator. The Regulator will request data resubmission if issues are identified. \r \r Further information on the reporting requirements associated with mine rehabilitation can be found at https://www.resourcesregulator.nsw.gov.au/rehabilitation/mine-rehabilitation. \r \r Find more information about the data at https://www.seed.nsw.gov.au/project-approvals-boundary-layer\r \r Any data related questions should be directed to nswresourcesregulator@service-now.com

Storytelling with Data is organized into 2 main parts. Part I comprises four modules, and is collectively aimed at introducing students to the process of creating "data stories" using Python data science tools: Module 1: What makes a good story? Module 2: Visualizing data Module 3: Python and Jupyter notebooks as a medium for data storytelling Module 4: Data science tools Part II is project-based, and revolves around mini data science projects. For each project, one or more students choose a question and dataset to explore and turn into a data story. Each week students and groups will report on their progress with the latest iterations of their stories. Students should aim to participate in three or more projects during Part II of the course. At students' discretion, those three (or more) projects may comprise the same questions and/or datasets (e.g., whereby each story builds on the previous story), or multiple questions and/or datasets that may or may not be related. In addition, students are encouraged to build off of each others' code, projects, and questions. Projects and project groups should form organically and should remain flexible to facilitate changing goals and interests.