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)

To address Toronto's 2012 budget gap of $774 million, City Council has launched a review of all of its services and implemented a multi-year financial planning process. This data set contains the responses to the multiple- choice questions on the Core Services Review Public Consultation Feedback Form from members of the public. Approximately 13,000 responses were received (full and partial). The consultation was held between May 11 and June 17, 2011. As a public consultation, respondents chose to participate, and chose which questions to answer. This produced a self-selected sample of respondents. The majority of the responses were from City of Toronto residents. There were some responses from GTA residents. City staff reviewed the data and removed personal information and input violating city policies (for example, contravenes the City's current anti-discrimination policy or confidentiality policy). The .SAV file may be viewed with Statistics software such as SPSS or SAS.

The Supplementary Material for this article:Supplementary Table 1 | PRISMA 2009 checklist.Supplementary Table 2 | Search strategies.Supplementary Table 3 | Research information details and indicators dataset.Supplementary Table 4 | Specific information of indicators and studies under different profile.Supplementary Table 5 | Analysis results data.

In order to request access to this data please complete the data request form.* * University of Bristol staff should use this form instead. The ASK feasibility trial: a randomised controlled feasibility trial and process evaluation of a complex multicomponent intervention to improve AccesS to living-donor Kidney transplantation This trial was a two-arm, parallel group, pragmatic, individually-randomised, controlled, feasibility trial, comparing usual care with a multicomponent intervention to increase access to living-donor kidney transplantation. The trial was based at two UK hospitals: a transplanting hospital and a non-transplanting referral hospital. 62 participants were recruited. 60 participants consented to data sharing, and their trial data is available here. 2 participants did not consent to data sharing and their data is not available. This project contains: 1. The ASK feasibility trial dataset 2. The trial questionnaire 3. An example consent form 4. Trial information sheet This dataset is part of a series: ASK feasibility trial documents: https://doi.org/10.5523/bris.1u5ooi0iqmb5c26zwim8l7e8rm The ASK feasibility trial: CONSORT documents: https://doi.org/10.5523/bris.2iq6jzfkl6e1x2j1qgfbd2kkbb The ASK feasibility trial: Wellcome Open Research CONSORT checklist: https://doi.org/10.5523/bris.1m3uhbdfdrykh27iij5xck41le The ASK feasibility trial: qualitative data: https://doi.org/10.5523/bris.1qm9yblprxuj2qh3o0a2yylgg

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing. The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data. Specifically, participants will learn: 1. Primary challenges and successes when sharing quantitative and qualitative clinical research data. 2. Platforms available for open data sharing. 3. Ways to troubleshoot data sharing and publish from open data. Workshop Agenda: 1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital 2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides. Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

Objectives: In quantitative research, understanding basic parameters of the study population is key for interpretation of the results. As a result, it is typical for the first table (“Table 1”) of a research paper to include summary statistics for the study data. Our objectives are 2-fold. First, we seek to provide a simple, reproducible method for providing summary statistics for research papers in the Python programming language. Second, we seek to use the package to improve the quality of summary statistics reported in research papers.

Materials and Methods: The tableone package is developed following good practice guidelines for scientific computing and all code is made available under a permissive MIT License. A testing framework runs on a continuous integration server, helping to maintain code stability. Issues are tracked openly and public contributions are encouraged.

Results: The tableone software package automatically compiles summary statistics into publishable formats such...

This collection contains an example MINUTE-ChIP dataset to run minute pipeline on, provided as supporting material to help users understand the results of a MINUTE-ChIP experiment from raw data to a primary analysis that yields the relevant files for downstream analysis along with summarized QC indicators. Example primary non-demultiplexed FASTQ files provided here were used to generate GSM5493452-GSM5493463 (H3K27m3) and GSM5823907-GSM5823918 (Input), deposited on GEO with the minute pipeline all together under series GSE181241. For more information about MINUTE-ChIP, you can check the publication relevant to this dataset: Kumar, Banushree, et al. "Polycomb repressive complex 2 shields naïve human pluripotent cells from trophectoderm differentiation." Nature Cell Biology 24.6 (2022): 845-857. If you want more information about the minute pipeline, there is a public biorXiv and a GitHub repository and official documentation.

BackgroundCurrently, the biggest issue facing the entire world is mental health. According to the Ethiopian Ministry of Health, nearly one-fourth of the community is experiencing any of the mental illness categories. Most of the cases were treated in religious and traditional institutions, which the community most liked to be treated. However, there were very limited studies conducted to show the level of mental health literacy among traditional healers.AimsThe study aimed to assess the level of mental health literacy and its associated factors among traditional healers toward mental illness found in Northeast, Ethiopia from September 1-30/2022.MethodA mixed approach cross-sectional study design was carried out on September 130, 2022, using simple random sampling with a total sample of 343. Pretested, structured questionnaires and face-to-face interviews were utilized for data collection. The level of Mental Health Literacy (MHL) was assessed using the 35 mental health literacy (35-MHLQ) scale. The semi-structured checklist was used for the in-depth interview and the FGD for the qualitative part. Data was entered using Epi-data version 4.6 and, then exported to SPSS version 26 for analysis. The association between outcome and independent variables was analyzed with bivariate and multivariable linear regression. P-values < 0.05 were considered statistically significant. Thematic analysis was used to analyze the qualitative data, and the findings were then referenced with the findings of the quantitative data.ResultsThe findings of this study showed that the sample of traditional healers found in Dessie City scored a total mean of mental health literacy of 91.81 ± 10:53. Age (β = -0.215, 95% CI (-0.233, -0.05), p = 0.003, informal educational status (β = -5.378, 95% CI (-6.505, -0.350), p = 0.029, presence of relative with a mental disorder (β = 6.030, 95% CI (0.073, 7.428),p = 0.046, getting information on mental illness (β = 6.565, 95% CI (3.432, 8.680), p =

COVID-19 long haulers face profound psychosocial stressors (e.g., depression, anxiety, PTSD) and physical health challenges (e.g., brain fog, fatigue). This study tests the feasibility and initial impact of a digitally delivered mindful-walking (MW) intervention for improving the physical and psychosocial wellbeing of COVID-19 long haulers. We recruited 23 participants via Facebook groups, between March and November 2021, for a 4-week online MW intervention (i.e., 2 mindfulness practice sessions per week), that was delivered entirely through the study Facebook group. The intervention was assessed using mixed methods. Quantitative data were collected through brief daily evening surveys (i.e., 28 days) over the 4-week intervention period, and measured affect, cognition, mindfulness, physical activity, and MW engagement. Qualitative data were extracted from the Facebook group’s Paradata (i.e., participant feedback, engagement metrics, and all social media interactions). Multilevel modeling was employed for the statistical analysis and a pragmatic approach was used for the qualitative analysis. The participants reported a high feasibility score (mean=4.93/7, SD=1.88), which was comprised of perceived usefulness, satisfaction, and ease of use. Those who engaged in MW, on any given day, frequently reported better psychosocial moods with more positive affect (β=0.89, p<0.01), less negative affect (β=−0.83, p<0.01), higher perceived cognitive ability (β=0.52, p<0.05), and more physical activity (β=0.41, p<0.05). Additionally, participants who practiced MW more consistently during the study reported higher levels of momentary mindfulness (β=0.3 p<0.01). Participants expressed satisfaction with the intervention, reporting benefits such as better symptom management and an overall improvement in wellbeing. Despite the small sample size, the digital delivery of our MW intervention via Facebook showed high acceptability. Preliminary efficacy findings indicate improved mental wellbeing and physical activity among long haulers. Larger-scale RCTs are needed in the future to improve the robustness and applicability of findings.

This dataset provides foundational factor and portfolio return data used in empirical finance and asset pricing research. It contains: - Fama–French 3-Factor and 5-Factor models - Size (ME), Book-to-Market (B/M), Operating Profitability (OP), and Investment (Inv) portfolios - Bivariate portfolios (e.g., 2x3 Size-B/M sorts) - Industry portfolio returns All data originate from the Kenneth R. French Data Library and are based on CRSP and Compustat databases. Data are value-weighted and expressed in percentages.

Some files in this dataset contain header comments describing data sources and methodology (as shown below):

This file was created using the 202508 CRSP database.
The 1-month TBill rate data until 202405 are from Ibbotson Associates. 
Starting from 202406, the 1-month TBill rate is from ICE BofA US 1-Month Treasury Bill Index.

To correctly read such files in Python (pandas), use the comment parameter — it automatically ignores all lines starting with a specific symbol (e.g., none here, so you can skip manually):

Example 1 — Automatically detect header rows:

import pandas as pd

# Detect the first numeric line to find where data starts
file_path = "F-F_Research_Data_5_Factors_2x3.csv"

with open(file_path) as f:
  lines = f.readlines()

# Find where the header line (column names) appears
for i, line in enumerate(lines):
  if "Mkt-RF" in line:
    skip_rows = i
    break

df = pd.read_csv(file_path, skiprows=skip_rows, sep=r"\s+")
print(df.head())

Example 2 — Skip a known number of comment lines manually:

df = pd.read_csv("F-F_Research_Data_5_Factors_2x3.csv", skiprows=3, sep=r"\s+")

Example 3 — If comments are prefixed (e.g., with #):

df = pd.read_csv("F-F_Research_Data_5_Factors_2x3.csv", comment="#", sep=",")

File Structure Example

Column	Description
Mkt-RF	Market excess return
SMB	Small minus Big (size factor)
HML	High minus Low (book-to-market factor)
RMW	Robust minus Weak (profitability factor)
CMA	Conservative minus Aggressive (investment factor)
RF	Risk-free rate (1-month Treasury Bill)

Abstract

This survey is the first detailed study on the phenomena of teacher absenteeism in Indonesia obtained from two unannounced visits to 147 sample schools in October 2002 and March 2003. The study was conducted by the SMERU Research Institute and the World Bank, affiliated with the Global Development Network (GDN). Similar surveys were carried out at the same time in seven other developing countries: Bangladesh, Ecuador, India, Papua New Guinea, Peru, Uganda, and Zambia.

This research focuses on primary school teacher absence rates and their relations to individual teacher characteristics, conditions of the community and its institutions, and the education policy at various levels of authority. A teacher was considered as absent if at the time of the visit the researcher could not find the sample teacher in the school.

This survey was conducted in randomly selected 10 districts/cities in four Indonesian regions: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara.

Geographic coverage

Java-Bali, Sumatera, Kalimantan-Sulawesi and Nusa Tenggara regions

Analysis unit

• Teachers
• Schools

Kind of data

Sample survey data [ssd]

Sampling procedure

Information from Indonesian Statistics Agency (BPS) and the Ministry of Education was used as a basis to build a sample frame. The data gathered included the amount of total population, a list of villages and primary school facilities in each district/city. Due to limited time and resources, this research only focused on primary schools. In Indonesia, there are two types of primary education facilities: primary schools and primary madrasah. Primary schools are regulated by the Ministry of National Education, using the general curriculum, while primary madrasah are regulated by the Ministry of Religious Affairs, using a mixed (general and Islamic) curriculum.

A sample of districts/cities and schools (consisting of primary schools and primary madrasah) were selected using the following steps. First, Indonesia was divided into several regions based on the number of total population: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara. Indonesian provinces that were suffering from various conflicts (such as Aceh, Central Sulawesi, Maluku, North Maluku, and Papua) were removed from the sample selection process. Then, from each region, a total of five districts and cities were randomly selected, taking into account the population of each district/city.

Second, 12 schools were selected in each district/city. Before choosing sampled schools, researchers randomly selected 10 villages in each district/city to be sampled, taking into account the location of these villages (in urban or rural areas). One of the 10 villages was a backup village to anticipate the possibility of a village that was too difficult to reach. In each village sampled, researchers asked residents about the location of primary schools/madrasah (both public and private) in these villages. They started visiting schools, giving priority to public primary schools/madrasahs. To meet the number of samples in each district/city, additional samples were selected from private schools.

Third, in each school sampled, the researcher would request a list of teachers. If a school visited was considered to be large, such as schools with more than 15 teachers, then the researcher would only interview 15 teachers chosen randomly to ensure that survey quality could be maintained despite the limited time and resources. Each school was visited twice, both on an unannounced date. From the 147 primary schools/madrasah in the sample, 1,441 teachers were selected in each visit (because this is a panel study, the teacher absence data that were used were taken only from teachers that could be interviewed or whose data were obtained from both visits). If there were teachers whose information was only obtained from one of the visits, then their data was not included in the dataset panel.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• Teacher Questionnaire, First Visit
• Teacher Questionnaire, Second Visit.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

The STATA cleaning do-file and the data quality report on the dataset can also be found in external resources.

In this project, we applied our recently developed turboDDA method in the analysis of low amount of sample and iTRAQ labeled trace sample. Compared with standard data-ependent acquisition approach with dynamic exclusion, we detected improvment in spectral purity and quantification accuracy.

The trends for sustainable lifestyle and marketing motivated natural product consumption, such as natural skin care products (NSCPs). Different personal, environmental, and sociocultural factors influence purchase intention (PI) for NSCPs. However, there is a lack of evidence on the role of consumers’ ethnicity in the PI model. The present study investigated the moderated mediation role of ethnicity in the relationship between related factors, including environmental concern, subjective norms, health factor, Halal certificate, packaging design, past experience factor, price factor, and PI mediated by personal attitude. A web-based survey was utilized to capture quantitative data from a random sample of 330 multicultural consumer group participants. The results of the study indicated that consumers’ ethnicity substantially moderated the mediation effect of personal attitude in the relationships between subjective norms, health factor, Halal certificate, packaging design, past experience factor, price factor, and PI in the model. The findings contributed to understanding of the factors that influenced the PI of consumers from diverse sociocultural contexts in the market for natural products. It contributed directly to natural product marketing and industry.

The Project Information Literacy (PIL) lifelong learning survey dataset was produced as part of a two-year federally funded study on relatively recent US college graduates and their information-seeking behavior for continued learning. The goal of the survey was to collect quantitative data about the information-seeking behavior of a sample of recent graduates—the strategies, techniques, information support systems, and best practices—used to support lifelong learning in post-college life. The dataset contains responses from 1,651 respondents to a 21-item questionnaire administered between October 9, 2014 and December 15, 2014. The voluntary sample of respondents consisted of relatively recent graduates, who had completed their degrees between 2007 and 2012, from one of 10 US colleges and universities in the institutional sample. Quantitative data are included in the dataset about the learning needs of relatively recent graduates as well as the information sources they used in three arenas of their post-college lives (i.e., personal life, workplace, and the communities in which they resided). Demographic information—including age, gender, major, GPA, employment status, graduate school attendance, and geographic proximity of current residence to their alma mater—is also included in the dataset for the respondents. "Staying Smart: How Today's Graduates Continue to Learn Once They Complete College," Alison J. Head, Project Information Literacy Research Report, Seattle: University of Washington Information School (January 5, 2016), 112 pages, 6.9 MB.

Abstract

The Education Quality Improvement Programme in Tanzania (EQUIP-T) is a large, four-year Department for International Development (DFID) funded programme. It targets some of the most educationally disadvantaged regions in Tanzania to increase the quality of primary education and improve pupil learning outcomes, in particular for girls. EQUIP-T covers seven regions in Tanzania and has five components: 1) enhanced professional capacity and performance of teachers; 2) enhanced school leadership and management skills; 3) strengthened systems that support the district and regional management of education; 4) strengthened community participation and demand for accountability; and 5) strengthened learning and dissemination of results. Together, changes in these five outputs are intended to reduce constraints on pupil learning and thereby contribute to better-quality education (outcome) and ultimately improved pupil learning (impact).

The independent impact evaluation (IE) of EQUIP-T conducted by Oxford Policy Management Ltd (OPM) is a four-year study funded by DFID. It covers five of the seven programme regions (the two regions that will join EQUIP-T in a later phase are not included) and the first four EQUIP-T components (see above). The IE uses a mixed methods approach where qualitative and quantitative methods are integrated. The baseline approach consists of three main parts to allow the IE to: 1) capture the situation prior to the start of EQUIP-T so that changes can be measured during the follow-up data collection rounds; impact attributable to the programme assessed and mechanisms for programme impact explored; 2) develop an expanded programme theory of change to help inform possible programme adjustments; and 3) provide an assessment of the education situation in some of the most educationally disadvantaged regions in Tanzania to the Government and other education stakeholders.

This approach includes:

• Quantitative survey of 100 government primary schools in 17 programme treatment districts and 100 schools in eight control districts in 2014, 2016 and 2018 covering:
• Standard three pupils
• Teachers who teach standards 1-3 Kiswahili and/or mathematics;
• Teachers who teach standards 4-7 mathematics;
• Head teachers; and

• Standard two lesson observations in Kiswahili and mathematics.

• Qualitative fieldwork in nine research sites that overlap with a sub-set of the quantitative survey schools, in 2014, 2016 and 2018, consisting of key informant interviews (KIIs) and focus group discussions (FGDs) with head teachers, teachers, pupils, parents, school committee (SC) members, region, district and ward education officials and EQUIP-T programme staff; and

• A mapping of causal mechanisms, and assessment of the strength of assumptions underpinning the programme theory of change using qualitative and quantitative IE baseline data as well as national and international evidence.

The data and documentation contained in the World Bank Microdata Catalog are those from the EQUIP-T IE quantitative baseline survey conducted in 2014. For information on the qualitative research findings see OPM. 2015b. EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume II: Methods and Technical Annexes.

Geographic coverage

The survey is representative of the 17 EQUIP-T programme treatment districts. The survey is NOT representative of the eight control districts. For more details see the section on Representativeness and OPM. 2015. EQUIP-Tanzania Impact Evaluation: Final Baseline Technical Report, Volume I: Results and Discussion and OPM. 2015. EQUIP-Tanzania Impact Evaluation. Final Baseline Technical Report, Volume II: Methods and Technical Annexes.

The 17 treatment districts are:

-Dodoma Region: Bahi DC, Chamwino DC, Kongwa DC, Mpwapwa DC -Kigoma Region: Kakonko DC, Kibondo DC -Shinyanga Region: Kishapu DC, Shinyanga DC -Simiyu Region: Bariadi DC, Bariadi TC, Itilima DC, Maswa DC, Meatu DC -Tabora Region: Igunga DC, Nzega DC, Sikonge DC, Uyui DC

The 8 control districts are:

-Arusha Region: Ngorongoro DC -Mwanza Region: Misungwi DC -Pwani Region: Rufiji DC
-Rukwa Region: Nkasi DC -Ruvuma Region: Tunduru DC -Singida Region: Ikungi DC, Singida DC -Tanga Region: Kilindi DC

Analysis unit

• School
• Teacher
• Pupil
• Lesson (not sampled)

Kind of data

Sample survey data [ssd]

Sampling procedure

Because the EQUIP-T regions and districts were purposively selected (see OPM. 2015. EQUIP-Tanzania Impact Evaluation: Final Baseline Technical Report, Volume I: Results and Discussion.), the IE sampling strategy used propensity score matching (PSM) to: (i) match eligible control districts to the pre-selected and eligible EQUIP-T districts (see below), and (ii) match schools from the control districts to a sample of randomly sampled treatment schools in the treatment districts. The same schools will be surveyed for each round of the IE (panel of schools) and standard 3 pupils will be interviewed at each round of the survey (no pupil panel).

Identifying districts eligible for matching

Eligible control and treatment districts were those not participating in any other education programme or project that may confound the measurement of EQUIP-T impact. To generate the list of eligible control and treatment districts, all districts that are contaminated because of other education programmes or projects or may be affected by programme spill-over were excluded as follows:

-All districts located in Lindi and Mara regions as these are part of the EQUIP-T programme, but the impact evaluation does not cover these two regions; -Districts that will receive partial EQUIP-T programme treatment or will be subject to potential EQUIP-T programme spill-overs; -Districts that are receiving other education programmes/projects that aim to influence the same outcomes as the EQUIP-T programme and would confound measurement of EQUIP-T impact; -Districts that were part of pre-test 1 (two districts); and -Districts that were part of pre-test 2 (one district).

Sampling frame

To be able to select an appropriate sample of pupils and teachers within schools and districts, the sampling frame consisted of information at three levels:

-District level; -School level; and -Within school level.

The sampling frame data at the district and school levels was compiled from the following sources: the 2002 and 2012 Tanzania Population Censuses, Education Management Information System (EMIS) data from the Ministry of Education and Vocational Training (MoEVT) and the Prime Minister's Office for Regional and Local Government (PMO-RALG), and the UWEZO 2011 student learning assessment survey. For within school level sampling, the frames were constructed upon arrival at the selected schools and was used to sample pupils and teachers on the day of the school visit.

Sampling stages

Stage 1: Selection of control districts

Because the treatment districts were known, the first step was to find sufficiently similar control districts that could serve as the counterfactual. PSM was used to match eligible control districts to the pre-selected, eligible treatment districts using the following matching variables: Population density, proportion of male headed households, household size, number of children per household, proportion of households that speak an ethnic language at home, and district level averages for household assets, infrastructure, education spending, parental education, school remoteness, pupil learning levels and pupil drop out.

Stage 2: Selection of treatment schools

In the second stage, schools in the treatment districts were selected using stratified systematic random sampling. The schools were selected using a probability proportional to size approach, where the measure of school size was the standard two enrolment of pupils. This means that schools with more pupils had a higher probability of being selected into the sample. To obtain a representative sample of programme treatment schools, the sample was implicitly stratified along four dimensions:

-Districts; -PSLE scores for Kiswahili; -PSLE scores for mathematics; and -Total number of teachers per school.

Stage 3: Selection of control schools

As in stage one, a non-random PSM approach was used to match eligible control schools to the sample of treatment schools. The matching variables were similar to the ones used as stratification criteria: Standard two enrolment, PSLE scores for Kiswahili and mathematics, and the total number of teachers per school.

The midline and endline surveys will be conducted for the same schools as the baseline survey (a panel of schools). However, the IE will not have a panel of pupils as a pupil only attends standard three once (unless repeating). Thus, the IE will have a repeated cross-section of pupils in a panel of schools.

Stage 4: Selection of pupils and teachers within

This dataset includes all the data files that were used for the studies in my Master Thesis: "The Choice of Aspect in the Russian Modal Construction with prixodit'sja/prijtis'". The data files are numbered so that they are shown in the same order as they are presented in the thesis. They include the database and the code used for the statistical analysis. Their contents are described in the ReadMe files. The core of the work is a quantitative and empirical study on the choice of aspect by Russian native speakers in the modal construction prixodit’sja/prijtis’ + inf. The hypothesis is that in the modal construction prixodit’sja/prijtis’ + inf the aspect of the infinitive is not fully determined by grammatical context but, to some extent, open to construal. A preliminary analysis was carried out on data gathered from the Russian National Corpus (www.ruscorpora.ru). Four hundred and forty-seven examples with the verb prijtis' were annotated manually for several factors and a statistical test (CART) was run. Results demonstrated that no grammatical factor plays a big role in the use of one aspect rather than the other. Data for this study can be consulted in the files from 01 to 03 and include a ReadMe file, the database in .csv format and the code used for the statistical test. An experiment with native speakers was then carried out. A hundred and ten native speakers of Russian were surveyed and asked to evaluate the acceptability of the infinitive in examples with prixodit’sja/prijtis’ delat’/sdelat’ šag/vid/vybor. The survey presented seventeen examples from the Russian National Corpus that were submitted two times: the first time with the same aspect as in the original version, the second time with the other aspect. Participants had to evaluate each case by choosing among “Impossible”, “Acceptable” and “Excellent” ratings. They were also allowed to give their opinion about the difference between aspects in each example. A Logistic Regression with Mixed Effects was run on the answers. Data for this study can be consulted in the files from 04 to 010 and include a ReadMe file, the text and the answers of the questionnaire, the database in .csv, .txt and pdf formats and the code used for the statistical test. Results showed that prijtis’ often admits both aspects in the infinitive, while prixodit’sja is more restrictive and prefers imperfective. Overall, “Acceptable” and “Excellent” responses were higher than “Impossible” responses for both aspects, even when the aspect evaluated didn’t match with the original. Personal opinions showed that the choice of aspect often depends on the meaning the speaker wants to convey. Only in very few cases the grammatical context was considered to be a constraint on the choice.

This is the raw data behind the publication (on PeerJ Preprints):

Open access levels: a quantitative exploration using Web of Science and oaDOI data

Across the world there is growing interest in open access publishing among researchers, institutions, funders and publishers alike. It is assumed that open access levels are growing, but hitherto the exact levels and patterns of open access have been hard to determine and detailed quantitative studies are scarce. Using newly available open access status data from oaDOI in Web of Science we are now able to explore year-on-year open access levels across research fields, languages, countries, institutions, funders and topics, and try to relate the resulting patterns to disciplinary, national and institutional contexts. With data from the oaDOI API we also look at the detailed breakdown of open access by types of gold open access (pure gold, hybrid and bronze), using universities in the Netherlands as an example. There is huge diversity in open access levels on all dimensions, with unexpected levels for e.g. Portuguese as language, Astronomy & Astrophysics as research field, countries like Tanzania, Peru and Latvia, and Zika as topic. We explore methodological issues and offer suggestions to improve conditions for tracking open access status of research output. Finally, we suggest potential future applications for research and policy development. We have shared all data and code openly.

The National Child Development Study (NCDS) is a continuing longitudinal study that seeks to follow the lives of all those living in Great Britain who were born in one particular week in 1958. The aim of the study is to improve understanding of the factors affecting human development over the whole lifespan.

The NCDS has its origins in the Perinatal Mortality Survey (PMS) (the original PMS study is held at the UK Data Archive under SN 2137). This study was sponsored by the National Birthday Trust Fund and designed to examine the social and obstetric factors associated with stillbirth and death in early infancy among the 17,000 children born in England, Scotland and Wales in that one week. Selected data from the PMS form NCDS sweep 0, held alongside NCDS sweeps 1-3, under SN 5565.

Survey and Biomeasures Data (GN 33004):
To date there have been ten attempts to trace all members of the birth cohort in order to monitor their physical, educational and social development. The first three sweeps were carried out by the National Children's Bureau, in 1965, when respondents were aged 7, in 1969, aged 11, and in 1974, aged 16 (these sweeps form NCDS1-3, held together with NCDS0 under SN 5565). The fourth sweep, also carried out by the National Children's Bureau, was conducted in 1981, when respondents were aged 23 (held under SN 5566). In 1985 the NCDS moved to the Social Statistics Research Unit (SSRU) - now known as the Centre for Longitudinal Studies (CLS). The fifth sweep was carried out in 1991, when respondents were aged 33 (held under SN 5567). For the sixth sweep, conducted in 1999-2000, when respondents were aged 42 (NCDS6, held under SN 5578), fieldwork was combined with the 1999-2000 wave of the 1970 Birth Cohort Study (BCS70), which was also conducted by CLS (and held under GN 33229). The seventh sweep was conducted in 2004-2005 when the respondents were aged 46 (held under SN 5579), the eighth sweep was conducted in 2008-2009 when respondents were aged 50 (held under SN 6137), the ninth sweep was conducted in 2013 when respondents were aged 55 (held under SN 7669), and the tenth sweep was conducted in 2020-24 when the respondents were aged 60-64 (held under SN 9412).

A Secure Access version of the NCDS is available under SN 9413, containing detailed sensitive variables not available under Safeguarded access (currently only sweep 10 data). Variables include uncommon health conditions (including age at diagnosis), full employment codes and income/finance details, and specific life circumstances (e.g. pregnancy details, year/age of emigration from GB).

Four separate datasets covering responses to NCDS over all sweeps are available. National Child Development Deaths Dataset: Special Licence Access (SN 7717) covers deaths; National Child Development Study Response and Outcomes Dataset (SN 5560) covers all other responses and outcomes; National Child Development Study: Partnership Histories (SN 6940) includes data on live-in relationships; and National Child Development Study: Activity Histories (SN 6942) covers work and non-work activities. Users are advised to order these studies alongside the other waves of NCDS.

From 2002-2004, a Biomedical Survey was completed and is available under Safeguarded Licence (SN 8731) and Special Licence (SL) (SN 5594). Proteomics analyses of blood samples are available under SL SN 9254.

Linked Geographical Data (GN 33497):
A number of geographical variables are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies.

Linked Administrative Data (GN 33396):
A number of linked administrative datasets are available, under more restrictive access conditions, which can be linked to the NCDS EUL and SL access studies. These include a Deaths dataset (SN 7717) available under SL and the Linked Health Administrative Datasets (SN 8697) available under Secure Access.

Multi-omics Data and Risk Scores Data (GN 33592)
Proteomics analyses were run on the blood samples collected from NCDS participants in 2002-2004 and are available under SL SN 9254. Metabolomics analyses were conducted on respondents of sweep 10 and are available under SL SN 9411. Polygenic indices are available under SL SN 9439. Derived summary scores have been created that combine the estimated effects of many different genes on a specific trait or characteristic, such as a person's risk of Alzheimer's disease, asthma, substance abuse, or mental health disorders, for example. These scores can be combined with existing survey data to offer a more nuanced understanding of how cohort members' outcomes may be shaped.

Additional Sub-Studies (GN 33562):
In addition to the main NCDS sweeps, further studies have also been conducted on a range of subjects such as parent migration, unemployment, behavioural studies and respondent essays. The full list of NCDS studies available from the UK Data Service can be found on the NCDS series access data webpage.

How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from NCDS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Further information about the full NCDS series can be found on the Centre for Longitudinal Studies website.

The Social Participation and Identity project combined quantitative longitudinal data with a qualitative investigation of a sub-sample of the NCDS cohort when they were aged 50, presented here as a mixed-methods data collection containing both qualitative and quantitative data. This was the first attempt to interview members of a national, longitudinal cohort study in depth, with the possibility of linking such biographical narratives to structured survey data collected throughout the life course. Interviews were conducted with a sub-sample of 220 NCDS cohort members resident in Great Britain (England, Scotland and Wales). The interviews were organised into six main sections focussing on: 1) Neighbourhood and belonging; 2) Leisure activities and social participation; 3) Personal communities; 4) Life histories; 5) Identity; 6) Reflections on being part of the NCDS.

Further information:

• details of the qualitative NCDS project (and the rest of the NCDS series) can be found on the Centre for Longitudinal Studies website and Social Participation and Identity ESRC award webpage.
• A list of the UK Data Archive's NCDS holdings, both quantitative and qualitative, can be found on the NCDS key data page.

For the first and second editions of the study (2011 and 2012), the interview transcripts, interviewer observation summaries, gender identity diagrams and life trajectory diagrams for all participants were made available. For the third edition (July 2013), 179 essays collected from the subproject participants at the time of the NCDS2 wave (conducted 1969) were added to the study. See documentation for further details. (Users should note that an additional sample of transcribed essays from a wider set of NCDS2 participants is available from the Archive under SN 8313.)

This is a dataset of participants responses to a survey questionnaire that was conducted as a part of a research paper. The study aims to examine the effect of decarbonization and energy efficiency in the Egyptian context in relation to pollution reduction, indoor and outdoor air quality which should reflect on human health and well-being. It also aims to assess carbon management strategies and policies adopted or needed in Egypt.

Abstract

This study examines various dimensions of primary health care delivery in Uganda, using a baseline survey of public and private dispensaries, the most common lower level health facilities in the country.

The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.

This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.

Objectives of the study included: 1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level; 2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided; 4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit; 5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance; 6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.

Geographic coverage

The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.

Analysis unit

• local dispensary with or without maternity unit

Universe

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.

Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.

The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.

Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.

The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.

For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.

Sampling deviation

In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• District Health Team Questionnaire;
• District Facility Data Sheets;
• Uganda Health Facility Survey Questionnaire;
• Facility Data Sheets;
• Facility Patient Exit Poll Questionnaire.

The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.

At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.

The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.

At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.

Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.