Objective: Routinely collected health data, collected for administrative and clinical purposes, without specific a priori research questions, are increasingly used for observational, comparative effectiveness, health services research, and clinical trials. The rapid evolution and availability of routinely collected data for research has brought to light specific issues not addressed by existing reporting guidelines. The aim of the present project was to determine the priorities of stakeholders in order to guide the development of the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. Methods: Two modified electronic Delphi surveys were sent to stakeholders. The first determined themes deemed important to include in the RECORD statement, and was analyzed using qualitative methods. The second determined quantitative prioritization of the themes based on categorization of manuscript headings. The surveys were followed by a meeting of RECO...

Abstract

The Bangladesh Demographic and Health Survey (BDHS) is part of the worldwide Demographic and Health Surveys program, which is designed to collect data on fertility, family planning, and maternal and child health.

The BDHS is intended to serve as a source of population and health data for policymakers and the research community. In general, the objectives of the BDHS are to: - assess the overall demographic situation in Bangladesh, - assist in the evaluation of the population and health programs in Bangladesh, and - advance survey methodology.

More specifically, the objective of the BDHS is to provide up-to-date information on fertility and childhood mortality levels; nuptiality; fertility preferences; awareness, approval, and use of family planning methods; breastfeeding practices; nutrition levels; and maternal and child health. This information is intended to assist policymakers and administrators in evaluating and designing programs and strategies for improving health and family planning services in the country.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 10-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

Bangladesh is divided into six administrative divisions, 64 districts (zillas), and 490 thanas. In rural areas, thanas are divided into unions and then mauzas, a land administrative unit. Urban areas are divided into wards and then mahallas. The 1996-97 BDHS employed a nationally-representative, two-stage sample that was selected from the Integrated Multi-Purpose Master Sample (IMPS) maintained by the Bangladesh Bureau of Statistics. Each division was stratified into three groups: 1 ) statistical metropolitan areas (SMAs), 2) municipalities (other urban areas), and 3) rural areas. 3 In the rural areas, the primary sampling unit was the mauza, while in urban areas, it was the mahalla. Because the primary sampling units in the IMPS were selected with probability proportional to size from the 1991 Census frame, the units for the BDHS were sub-selected from the IMPS with equal probability so as to retain the overall probability proportional to size. A total of 316 primary sampling units were utilized for the BDHS (30 in SMAs, 42 in municipalities, and 244 in rural areas). In order to highlight changes in survey indicators over time, the 1996-97 BDHS utilized the same sample points (though not necessarily the same households) that were selected for the 1993-94 BDHS, except for 12 additional sample points in the new division of Sylhet. Fieldwork in three sample points was not possible (one in Dhaka Cantonment and two in the Chittagong Hill Tracts), so a total of 313 points were covered.

Since one objective of the BDHS is to provide separate estimates for each division as well as for urban and rural areas separately, it was necessary to increase the sampling rate for Barisal and Sylhet Divisions and for municipalities relative to the other divisions, SMAs and rural areas. Thus, the BDHS sample is not self-weighting and weighting factors have been applied to the data in this report.

Mitra and Associates conducted a household listing operation in all the sample points from 15 September to 15 December 1996. A systematic sample of 9,099 households was then selected from these lists. Every second household was selected for the men's survey, meaning that, in addition to interviewing all ever-married women age 10-49, interviewers also interviewed all currently married men age 15-59. It was expected that the sample would yield interviews with approximately 10,000 ever-married women age 10-49 and 3,000 currently married men age 15-59.

Note: See detailed in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Four types of questionnaires were used for the BDHS: a Household Questionnaire, a Women's Questionnaire, a Men' s Questionnaire and a Community Questionnaire. The contents of these questionnaires were based on the DHS Model A Questionnaire, which is designed for use in countries with relatively high levels of contraceptive use. These model questionnaires were adapted for use in Bangladesh during a series of meetings with a small Technical Task Force that consisted of representatives from NIPORT, Mitra and Associates, USAID/Bangladesh, the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), Population Council/Dhaka, and Macro International Inc (see Appendix D for a list of members). Draft questionnaires were then circulated to other interested groups and were reviewed by the BDHS Technical Review Committee (see Appendix D for list of members). The questionnaires were developed in English and then translated into and printed in Bangla (see Appendix E for final version in English).

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including his/her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. In addition, information was collected about the dwelling itself, such as the source of water, type of toilet facilities, materials used to construct the house, and ownership of various consumer goods.

The Women's Questionnaire was used to collect information from ever-married women age 10-49. These women were asked questions on the following topics: - Background characteristics (age, education, religion, etc.), - Reproductive history, - Knowledge and use of family planning methods, - Antenatal and delivery care, - Breastfeeding and weaning practices, - Vaccinations and health of children under age five, - Marriage, - Fertility preferences, - Husband's background and respondent's work, - Knowledge of AIDS, - Height and weight of children under age five and their mothers.

The Men's Questionnaire was used to interview currently married men age 15-59. It was similar to that for women except that it omitted the sections on reproductive history, antenatal and delivery care, breastfeeding, vaccinations, and height and weight. The Community Questionnaire was completed for each sample point and included questions about the existence in the community of income-generating activities and other development organizations and the availability of health and family planning services.

Response rate

A total of 9,099 households were selected for the sample, of which 8,682 were successfully interviewed. The shortfall is primarily due to dwellings that were vacant or in which the inhabitants had left for an extended period at the time they were visited by the interviewing teams. Of the 8,762 households occupied, 99 percent were successfully interviewed. In these households, 9,335 women were identified as eligible for the individual interview (i.e., ever-married and age 10-49) and interviews were completed for 9,127 or 98 percent of them. In the half of the households that were selected for inclusion in the men's survey, 3,611 eligible ever-married men age 15-59 were identified, of whom 3,346 or 93 percent were interviewed.

The principal reason for non-response among eligible women and men was the failure to find them at home despite repeated visits to the household. The refusal rate was low.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the BDHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the BDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the BDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the BDHS is the ISSA Sampling Error Module. This module used the Taylor

ABSTRACT Objective: to investigate the literature production on the design of forms for research in the health area and describe the most relevant concepts and precepts of the topic. Methods: an integrative literature review in the PubMed and Scielo databases with the key words: survey, constructing, questionnaire, formulary, development and design in various combinations, including articles published in any language in the last ten years. The survey returned 1,480 articles, and after reading and critically reviewing the abstracts according to the objective of the study, 16 articles were selected for complete reading. Information regarding aspects that were most relevant to the objective of the study was analyzed, as well as its recurrence in the selected articles. Results: the reading of the 16 articles resulted in three categories, based on the recurrence of the themes: structure, validation and sampling. Conclusion: clarity in the formulation of the questions was the most valued aspect in the structure of the instrument. As for validation, the realization of pilot tests was considered fundamental. Finally, the method of administration and adaptation of the questionnaire to target population was considered fundamental.

Abstract

To ensure the centrality of refugees’ voices in discussions about their future, as well as to inform evidence-based inter-agency responses in support of host Governments, UNHCR is leading the regular implementation of intentions surveys with refugees from Ukraine, collecting primary data on their profiles, their current situation and intentions, and the factors influencing their decision-making. The first regional intentions survey was completed and the report published in July 2022 (https://data.unhcr.org/en/documents/details/94176). This data was collected during the second round, conducted between August and September 2022. The scope was expanded to include not only countries neighbouring Ukraine but other host countries in Europe and beyond. In addition, the second round also includes a deeper analysis of the factors influencing refugees’ decisions, as well as key insights into their current socio-economic situation. The report was published in September 2022 (https://data.unhcr.org/en/documents/details/95767). A mixed methodological approach was used, combining different sampling approaches and data collection modes. Over 4,800 refugee households (2,000 from countries neighboring Ukraine and 2,800 from other host countries) were interviewed either through a phone-based survey, web-based survey or face-to-face interview. All surveys used a harmonized questionnaire. This data is an anonymous version of the original data collected and used for the primary analysis.

Geographic coverage

Europe

Analysis unit

Households

Universe

Refugees from Ukraine

Kind of data

Sample survey data [ssd]

Sampling procedure

The overall combined sample included a total of over 4,800 surveys completed using one of the three methods. All surveys used a harmonized questionnaire, which collected data on refugees’ demographic profile, including about their place of origin, conditions in current host country and detailed intentions information. For the regional analysis, weights were applied based on the most recent figures available of the number of individual refugees recorded in each country. Most results are disaggregated between countries neighbouring Ukraine (2,000 surveys) and the rest of host countries covered in the survey (2,800 surveys), for the purpose of identifying differences in intentions and current conditions. A more detailed description of the sampling and weighting approach is included in Annex 1 of the report.

Mode of data collection

Other [oth]

Availability of data and materials: The primary data were collected by field survey 2021-2022. Questionnaire and interview methods were used to collect primary data from the study areas.

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb)
It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

***Methodology***

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

***Test procedure***
Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study.
The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx)
The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

***Description of the data in this data set***

Protocol_HVD_SLR provides the structure of the protocol
Spreadsheets #1 provides the filled protocol for relevant studies.
Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories:
(1) descriptive information,
(2) approach- and research design- related information,
(3) quality-related information,
(4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet
2) Complete reference - the complete source information to refer to the study
3) Year of publication - the year in which the study was published
4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter}
5) DOI / Website- a link to the website where the study can be found
6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science
7) Availability in OA - availability of an article in the Open Access
8) Keywords - keywords of the paper as indicated by the authors
9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information
10) Objective / RQ - the research objective / aim, established research questions
11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.)
12) Contributions - the contributions of the study
13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach?
14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared?
15) Period under investigation - period (or moment) in which the study was conducted
16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)?
18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term?
20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output")
21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description)
22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles?
23) Data - what data do HVD cover?
24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

***Format of the file***
.xls, .csv (for the first spreadsheet only), .odt, .docx

***Licenses or restrictions***
CC-BY

For more info, see README.txt

Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms. Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time. Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months. Methods The dataset was collected via a combination of the following: 1. manual extraction of EHR-based data followed by entry into REDCap and then analysis and further processing in SAS 9.4; 2. Data pull of Epic EHR-based data from Clarity database using standard programming techniques, followed by processing in SAS 9.4 and merging with data from REDCap; 3. Collection of data directly from participants via telephone with entry into REDCap and further processing in SAS 9.4; 4. Collection of process measures from study team tracking records followed by entry into REDCap and further processing in SAS 9.4. One file in the dataset contains aggregate data generated following merging of Clarity data pull-origin dataset with a REDCap dataset and further manual processing. Recruitment for the randomized trial began at an epilepsy clinic visit, with EHR-embedded validated anxiety and depression instruments, followed by automated EHR-based research screening consent and eligibility assessment. Fully eligible individuals later completed telephone consent, enrollment and randomization. Thirty total participants were randomized 1:1 to EHR portal versus telephone outcome assessment, and patient-reported and process outcomes were collected at 3- and 6-months, with primary outcome 6-month retention in EHR arm(feasibility target: ≥11 participants retained). Variables in this dataset include recruitment flow diagram data, baseline participant sociodemographic and clinical characteristics, retention (successful PROM collection at 6 months), and process measures. The process measures included research staff time to collect outcomes, research staff time to collect outcomes and enter data, time from initial outcome collection reminder to outcome collection, and number of reminders sent to participants for outcome collection. PROMs were collected via the randomized method only at 3 months. At 6 months, if the criteria for retention was not met by the randomized method (failure to return outcomes by 1 week after 5 post-due date reminders for outcome collection), up to 3 additional attempts were made to collect outcomes by the alternative method, and process measures were also collected during this hybrid outcome collection method approach.

This report presents findings, discussions, conclusions and recommendations made following a survey of the Water Sanitation and Hygiene (WASH) Knowledge, Attitude and Practice (KAP) among refugees in Kakuma refugee camps and Kalobeyei settlement in Turkana West in Turkana County, Kenya. Two principle methods were used to collect primary data: household questionnaire and documents review in a summative evaluation approach.

The survey adopted a summative evaluation approach and a combination of quantitative and qualitative methods were used to obtain answers to the survey questions. The mixed approach was adopted for purposes of complementary, triangulation, and validation of responses. Primary data was collected with the use of household questionnaires, Key Informant Interviews (KIIs) and review of existing literatures and reports. The inclusive criteria of the survey allowed the enumerators to collect data from household heads or any household member of age 18 years and above who consented to participate in the survey. Qualitative methods mainly KIIs were also adopted in order to gather an in-depth understanding of the perspectives of the various primary targets of the programme. These included households, members of WASH Committees, NRC staff and staff from the implementing partner agencies. Observation method was also used during the survey to assess WASH infrastructure and human habits. The infrastructure included; water points, sanitation and hygiene facilities.

The stratified random sampling survey was carried out in in the 4 camps of Kakuma and the 3 villages of Kalobeyei. In Kakuma, all the 13 zones were included in the study for significant representation. The study population comprised all the households for the 192,352 refugees and asylum seekers registered in Kakuma camp (153,593) and Kalobeyei settlement (36,099) as per UNHCR population statistics of August 2019. The respondents were sampled household heads or family members aged 18 years and above. Although teachers who are patrons of school health clubs where institutional latrines had been done were targeted for interview, they were not reached due to school vacation.

Food insecurity is a chronic problem in the United States that annually affects over 40 million people under normal conditions. This difficult reality can dramatically worsen after disasters. Such events can disrupt both the supply and demand sides of food systems, restricting food distribution and access precisely when households are in a heightened need for food assistance. Often, retailers and food banks must react quickly to meet local needs under difficult post-disaster circumstances. Residents of Harris County and Southeast Texas experienced this problem after Hurricane Harvey made landfall on the Texas Gulf Coast in August 2017. The primary data collected by this project relate specifically to the supply side. The data attempt to identify factors that impacted the ability of suppliers to help ensure access to food, with a focus on fresh food access. Factors included impacts to people, property and products due to hurricane-related damage to infrastructure. Two types of food suppliers were the foci of this research: food aid agencies and food retailers. The research team examined food aid agencies in Southeast Texas with data collection methods that included secondary data analysis, a focus group and an online survey. The second population studied was food retailers with in-person surveys with store managers. Food retailers were randomly sampled in three Texas counties: Jefferson, Orange, and Harris. The data collection methods resulted in 32 food aid agency online survey responses and 210 completed food retail in-person surveys. Data were collected five to eight months after the event, which helped to increase the reliability and validity of the data. The time-sensitive nature of post-disaster data requires research teams to quickly organize their efforts before entering the field. The purpose of this project archive is to share the primary data collected, document methods, and to help future research teams reduce the amount of time needed for project development and reporting. This archive does not contain Personally or Business Identifiable Information.

This dataset includes bibliographic information for 501 papers that were published from 2010-April 2017 (time of search) and use online biodiversity databases for research purposes. Our overarching goal in this study is to determine how research uses of biodiversity data developed during a time of unprecedented growth of online data resources. We also determine uses with the highest number of citations, how online occurrence data are linked to other data types, and if/how data quality is addressed. Specifically, we address the following questions:

1.) What primary biodiversity databases have been cited in published research, and which

databases have been cited most often?

2.) Is the biodiversity research community citing databases appropriately, and are

the cited databases currently accessible online?

3.) What are the most common uses, general taxa addressed, and data linkages, and how

have they changed over time?

4.) What uses have the highest impact, as measured through the mean number of citations

per year?

5.) Are certain uses applied more often for plants/invertebrates/vertebrates?

6.) Are links to specific data types associated more often with particular uses?

7.) How often are major data quality issues addressed?

8.) What data quality issues tend to be addressed for the top uses?

Relevant papers for this analysis include those that use online and openly accessible primary occurrence records, or those that add data to an online database. Google Scholar (GS) provides full-text indexing, which was important to identify data sources that often appear buried in the methods section of a paper. Our search was therefore restricted to GS. All authors discussed and agreed upon representative search terms, which were relatively broad to capture a variety of databases hosting primary occurrence records. The terms included: “species occurrence” database (8,800 results), “natural history collection” database (634 results), herbarium database (16,500 results), “biodiversity database” (3,350 results), “primary biodiversity data” database (483 results), “museum collection” database (4,480 results), “digital accessible information” database (10 results), and “digital accessible knowledge” database (52 results)--note that quotations are used as part of the search terms where specific phrases are needed in whole. We downloaded all records returned by each search (or the first 500 if there were more) into a Zotero reference management database. About one third of the 2500 papers in the final dataset were relevant. Three of the authors with specialized knowledge of the field characterized relevant papers using a standardized tagging protocol based on a series of key topics of interest. We developed a list of potential tags and descriptions for each topic, including: database(s) used, database accessibility, scale of study, region of study, taxa addressed, research use of data, other data types linked to species occurrence data, data quality issues addressed, authors, institutions, and funding sources. Each tagged paper was thoroughly checked by a second tagger.

The final dataset of tagged papers allow us to quantify general areas of research made possible by the expansion of online species occurrence databases, and trends over time. Analyses of this data will be published in a separate quantitative review.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. Within the past fifteen years, policymakers across the country have increasingly supported criminal justice reforms designed to reduce the scope of mass incarceration in favor of less costly, more evidence-based approaches to preventing and responding to crime. One of the primary reform efforts is the Justice Reinvestment Initiative (JRI), a public-private partnership through which state governments work to diagnose the primary drivers of their state incarceration rates, reform their sentencing policies to send fewer nonviolent offenders to prison, and reinvest the saved money that used to go into prisons into alternatives to incarceration, instead. This mixed-methods study sought to assess public opinion about the justice reinvestment paradigm of reform and to determine whether exposure to racialized and race-neutral cues affects people's willingness to allocate money into criminal justice institutions versus community-based social services in order to reduce and prevent crime.

Abstract

The Jordan Population and Family Health Survey (JPFHS) is part of the worldwide Demographic and Health Surveys Program, which is designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 2012 Jordan Population and Family Health Survey (JPFHS) is to provide reliable estimates of demographic parameters, such as fertility, mortality, family planning, and fertility preferences, as well as maternal and child health and nutrition, that can be used by program managers and policymakers to evaluate and improve existing programs. The JPFHS data will be useful to researchers and scholars interested in analyzing demographic trends in Jordan, as well as those conducting comparative, regional, or cross-national studies.

Geographic coverage

National coverage

Analysis unit

• Household
• Women age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The 2012 JPFHS sample was designed to produce reliable estimates of major survey variables for the country as a whole, urban and rural areas, each of the 12 governorates, and for the two special domains: the Badia areas and people living in refugee camps. To facilitate comparisons with previous surveys, the sample was also designed to produce estimates for the three regions (North, Central, and South). The grouping of the governorates into regions is as follows: the North consists of Irbid, Jarash, Ajloun, and Mafraq governorates; the Central region consists of Amman, Madaba, Balqa, and Zarqa governorates; and the South region consists of Karak, Tafiela, Ma'an, and Aqaba governorates.

The 2012 JPFHS sample was selected from the 2004 Jordan Population and Housing Census sampling frame. The frame excludes the population living in remote areas (most of whom are nomads), as well as those living in collective housing units such as hotels, hospitals, work camps, prisons, and the like. For the 2004 census, the country was subdivided into convenient area units called census blocks. For the purposes of the household surveys, the census blocks were regrouped to form a general statistical unit of moderate size (30 households or more), called a "cluster", which is widely used in surveys as a primary sampling unit (PSU).

Stratification was achieved by first separating each governorate into urban and rural areas and then, within each urban and rural area, by Badia areas, refugee camps, and other. A two-stage sampling procedure was employed. In the first stage, 806 clusters were selected with probability proportional to the cluster size, that is, the number of residential households counted in the 2004 census. A household listing operation was then carried out in all of the selected clusters, and the resulting lists of households served as the sampling frame for the selection of households in the second stage. In the second stage of selection, a fixed number of 20 households was selected in each cluster with an equal probability systematic selection. A subsample of two-thirds of the selected households was identified for anthropometry measurements.

Refer to Appendix A in the final report (Jordan Population and Family Health Survey 2012) for details of sampling weights calculation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012 JPFHS used two questionnaires, namely the Household Questionnaire and the Woman’s Questionnaire (see Appendix D). The Household Questionnaire was used to list all usual members of the sampled households, and visitors who slept in the household the night before the interview, and to obtain information on each household member’s age, sex, educational attainment, relationship to the head of the household, and marital status. In addition, questions were included on the socioeconomic characteristics of the household, such as source of water, sanitation facilities, and the availability of durable goods. Moreover, the questionnaire included questions about child discipline. The Household Questionnaire was also used to identify women who were eligible for the individual interview (ever-married women age 15-49 years). In addition, all women age 15-49 and children under age 5 living in the subsample of households were eligible for height and weight measurement and anemia testing.

The Woman’s Questionnaire was administered to ever-married women age 15-49 and collected information on the following topics: • Respondent’s background characteristics • Birth history • Knowledge, attitudes, and practice of family planning and exposure to family planning messages • Maternal health (antenatal, delivery, and postnatal care) • Immunization and health of children under age 5 • Breastfeeding and infant feeding practices • Marriage and husband’s background characteristics • Fertility preferences • Respondent’s employment • Knowledge of AIDS and sexually transmitted infections (STIs) • Other health issues specific to women • Early childhood development • Domestic violence

In addition, information on births, pregnancies, and contraceptive use and discontinuation during the five years prior to the survey was collected using a monthly calendar.

The Household and Woman’s Questionnaires were based on the model questionnaires developed by the MEASURE DHS program. Additions and modifications to the model questionnaires were made in order to provide detailed information specific to Jordan. The questionnaires were then translated into Arabic.

Anthropometric data were collected during the 2012 JPFHS in a subsample of two-thirds of the selected households in each cluster. All women age 15-49 and children age 0-4 in these households were measured for height using Shorr height boards and for weight using electronic Seca scales. In addition, a drop of capillary blood was taken from these women and children in the field to measure their hemoglobin level using the HemoCue system. Hemoglobin testing was used to estimate the prevalence of anemia.

Cleaning operations

Fieldwork and data processing activities overlapped. Data processing began two weeks after the start of the fieldwork. After field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman, where they were registered and stored. Special teams were formed to carry out office editing and coding of the openended questions.

Data entry and verification started after two weeks of office data processing. The process of data entry, including 100 percent reentry, editing, and cleaning, was done by using PCs and the CSPro (Census and Survey Processing) computer package, developed specially for such surveys. The CSPro program allows data to be edited while being entered. Data processing operations were completed by early January 2013. A data processing specialist from ICF International made a trip to Jordan in February 2013 to follow up on data editing and cleaning and to work on the tabulation of results for the survey preliminary report, which was published in March 2013. The tabulations for this report were completed in April 2013.

Response rate

In all, 16,120 households were selected for the survey and, of these, 15,722 were found to be occupied households. Of these households, 15,190 (97 percent) were successfully interviewed.

In the households interviewed, 11,673 ever-married women age 15-49 were identified and interviews were completed with 11,352 women, or 97 percent of all eligible women.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2012 Jordan Population and Family Health Survey (JPFHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2012 JPFHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2012 JPFHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulae. The computer

Abstract

The sample design of the Production Methods and the Environment module survey is based on the sample of the current Survey of Agricultural Holdings, so firstly given the design of the current Survey. The main purpose of the Survey of Agricultural Holdings as well as Production Methods and the Environment module is to produce official indicators in line with agricultural sector. The survey allows the compilation of statistics on crops and animal husbandry, of which information annual and permanent crops, sown area, average yield of annual crops, farming practices and their linkages with the natural environment, crop and livestock production methods, access to and use of information services, infrastructure and communal resources and etc. Statistical tables are accessible through the following link: https://www.geostat.ge/en/modules/categories/196/agriculture. Production Methods and the Environment Module is part of main Survey of Agricultural Holdings. One round of the main survey (reference year) includes 5 inquiries: The Inception interview is carried out using the inception questionnaire during the period of January-February of the reference year. During this interview the sampled holdings are identified and situation existing at the holding as of first January is recorded. I, II and III quarter interviews are conducted by means of quarterly questionnaire at the beginning of the following month of the corresponding quarter of the reference year. Based on these surveys, the information about agricultural activities during the corresponding quarter is collected. The final interview is conducted by means of final questionnaire in January of the following year of the reference year. During this interview, the information about agricultural activities at the holding during IV quarter of the reference year and the summery information about agricultural activities at the holding during the whole reference year (from 1 January to 31 December of the previous year) are collected. During all five interviews, the same agricultural holdings (about 12000) are interviewed which are selected by a two-stage stratified cluster random sampling procedure out of about 642 000 agricultural holdings operated in Georgia. On the first stage, clusters (settlements) are selected. On the second stage, holdings are selected within the selected clusters. The survey completely covers the territory of Georgia, excluding the occupied territories of Autonomous Republic of Abkhazia and Tskhinvali region. Each year a new sample is selected based on a rotational design (on a 3-year basis). In particular, every year approximately 4000 holdings out of the 12000 sampled holdings are replaced by new holdings. Sampled holdings participate in the survey for 3 years. Large agricultural holdings are sampled every year with complete coverage. The statistical unit of the survey is the agricultural holding (family holdings and agricultural enterprises) - which is defined as an economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size. Agricultural activities are conducted under the supervision of a holder (in case of households - a member of household, in case of agricultural enterprises - director or authorized person), who is responsible for making decisions and takes all economic risks and expenses related to agricultural activities. More than 270 interviewers participate in the survey fieldwork. For the Data collection, computer-assisted personal interviewing method (CAPI) is used in the family holdings. In case of agricultural enterprises, the authorized persons of the enterprises (respondent) fill the electronic (online) questionnaires by themselves (CAWI). Coordination of the interviewers and the primary control of the collected data during the field is carried out by coordinators. Their working area covers several municipalities. The function of the coordinators also includes consultation for agricultural enterprises on methodological and technical issues related to the survey. Production Methods and Environment module field work was carried out from May 5th to May 20th of 2022. 200 field staff participated in the survey, 22 of which were field supervisors. In total 5,880 agricultural holdings were selected for the PME survey. Such are the extra-large farms that are continuously participating in the survey and the third rotation farms that have been participating in the survey since 2019. Currently 943 extra-large farms and 3,899 third rotation farms are participating in the survey. Therefore, we have a total of 4,842 farm data for the last three years. The rest of the holdings will be selected from the first rotation clusters where interviews have been conducted for two years. In particular, using simple random sampling approximately 30% of the working clusters of the first rotation are selected in each stratum. This will give us about 1,038 farms. A total of about 5,880 farms will be selected.

Geographic coverage

Entire country (Georgia), excluding occupied regions (Abkhazia and Tskhinvali region)

Analysis unit

Agricultural holding – economic unit of agricultural production under single management comprising all livestock kept and all land used wholly or partly for agricultural production purposes, without regard to title, legal form or size in which agricultural activities are conducted under the supervision of a holder, who is responsible for making decisions and takes all economic risks and expenses related to agricultural activities.

Universe

Survey sampling frame includes about 642 000 agriculture holdings (households and agricultural enterprises) operated in country. The Agricultural Census 2014 is the main source of the sample frame. Sampling frame is updated on a permanent basis in according to the results of survey of agricultural holdings, business register and different administrative sources.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design of the Production Methods and the Environment module survey is based on the sample of the current Survey of Agricultural Holdings, so firstly given the design of the current Survey. • Main Source of the sample frame since 2016 - Agricultural Census 2014; • Sample frame contained 642 000 holding - sample size 12 000 (1.9%); • Sample Design: two-stage stratified cluster random sampling; - First stage - selection of cluster (Settlement); - Second stage - Selection of holdings within the selected clusters; • Each year a new sample is selected based on a rotational design; - Every year 1/3 of holdings (4 000) selected a year before are replaced (Sampled holdings participate in the survey during 3 years); • Extremely large agricultural holdings are sampled every year with complete coverage; • Additional Sources for updating sample frame: Sample Survey of Agricultural Holdings, Statistical Business Register, Administrative data existing in MEPA (large agricultural holdings); Sampling error of main indicators do not exceed 5% for a country level and 10% for a regional level; The sample design of the Production Methods and the Environment module survey: • Sample Design:Two-stage cluster sampling was used for the survey. -Sample is formed separately in each stratum. At first, clusters are selected in every stratum, and then holdings from selected clusters are selected for survey. -Extra-large holdings will be in the sample by probability 1. That is, all clusters of extra-large holdings and all extra-large holdings from these clusters fall into sample. -Primary sampling unit in the rest of the strata is the cluster. The same number of holdings will be interviewed in all the selected clusters of a stratum. Specifically, in small holding strata, 12 holdings will be interviewed in each selected cluster. This number is 8 for medium-sized strata and 4 for large strata. -In each stratum the number of clusters that have to be selected is calculated by dividing the number of holdings to be selected in the stratum by the number of holdings to be interviewed in each cluster of the stratum. -In each stratum selection of clusters is done by the PPS method (Probability Proportionally to Size). -The selection of holdings in each selected cluster is made using a random systematic sample. • Rotational design: Survey has a panel design. Holdings, which will get into the sample, will stay there for three years. After this, they will be substituted by holdings from the same stratum. -The database lists 943 extra-large holdings. All of them will constantly participate in the survey. Their rotation group number will be "0". Of the remaining holdings each of them will belong to one of the three rotation groups. Holdings selected from the same cluster will fall in the same rotation group. Each rotation group will have more or less the same number of holdings. Each rotation group represents an independent random sample. -When holdings change by rotation , holding from the sample will be substituted by the new one from the same cluster. If the cluster does not have enough holdings to make the full rotation, then the cluster is deemed exhausted and is substituted by a randomly selected cluster from the same stratum. -Newly introduced holdings will belong to the same rotation group which its predecessor belonged to

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Detailed information on structure, and sections of questionnaires used in the survey of agricultural holdings available in following link:

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Abstract

To ensure the centrality of IDP’ voices in discussions about their future, as well as to inform evidence-based inter-agency responses in support of the Government of Ukraine, UNHCR is leads the regular implementation of intentions surveys with IDPs and refugees from Ukraine, collecting primary data on their profiles, their current situation and intentions, and the factors influencing their decision-making.

The first round of intentions surveys among IDPs in Ukraine was completed and the report published in February 2023 (https://data.unhcr.org/en/documents/details/99164). This data was collected during the second round, conducted in May 2023, in parallel to the fourth round of the refugees' intentions surveys. A joint report was published in July 2023 (https://reporting.unhcr.org/ukraine-lives-hold-intentions-and-perspectives-refugees-and-idps).

This data is an anonymous version of the original data collected and used for the primary analysis.

Geographic coverage

Europe

Analysis unit

Households

Universe

Internally displaced persons (IDPs) in Ukraine

Kind of data

Sample survey data [ssd]

Sampling procedure

A structured questionnaire was used to interview around 4,000 IDP households through a phone-based survey. Stratified random sampling was used to select the households from UNHCR's registration database (proGres). See further details on the methodology in the report.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Abstract

1.1 Preambule

This study was funded by Google.org. The study began in 2008 and will end in 2011. Field work was done between May and July 2009 for the first round and February and March 2010 for the second round. The purpose of this field report is (1) to document how the data was collected; (2) to act as a reference to those who will be writing scientific papers, processing, and analyzing the data; and (30 consolidate the findings for purposes of sharing with key stakeholders including teachers and Ministry of Education. The report has five sections: Section 1 presents the study background. Section two presents data collection issues. Section three outlines the district and individual school reports. Section four captures the challenges experienced. Section five outlines the lessons learnt and recommendations for future classroom-based studies.

1.2 Purpose of the study

The purpose of this study was to examine the teaching process and generate information relevant to objective policy advice on the quality of teaching and learning. The intention is that by sharing the evidence generated by this study with policy makers, it is hoped that it will lead to the improvement of the quality of teaching in primary schools in Kenya. It sought to understand whether classroom interactions, including how aspects such as 'Opportunity to Learn' explain learning achievement.

1.3 Research questions guiding the study

The following are the main research questions guiding the study. However, the data collected is rich on teaching practice information and will make it possible to answer several other research questions.

a). What are the differences and similarities in teaching practice among teachers in high and low performance schools?

b). Does the observed teaching practice explain student achievement?

c). Do teacher attributes explain student's learning achievement?

d). What policy recommendations on teaching practices can improve the quality of teaching in primary education?

Based on the guiding research questions, the following research papers have been conceptualized and are being finalized for publication as publicly available and accessible APHRC Working Papers.

a) Do teachers who have a good understanding of maths demonstrate better teaching practice in the classrooms?

b) Does teaching practice explain differences in learner achievement in low and high performing schools?

c) Social relations as predictors of achievement in maths in Kenya primary schools.

Other questions that the data may help to answer

a) Do opportunities to learn (measured by teacher absenteeism, curriculum completion, and bullying and class size) explain learning gains.

b) To what extent do student characteristics, classroom sitting arrangements and classroom participation explain learning gains?

c) Assess whether female and male teachers differ in mathematics teaching and content knowledge, and whether this is reflected in pupils' mathematics performance.

Geographic coverage

Six districts in Kenya: Embu, Nairobi, Gucha, Garissa, Muranga and Baringo and 12 schools in each district

Analysis unit

Pupils

Schools

Universe

Grade 6 pupils in the selected schools, the headteacher and Math, English and Science Teachers

Sampling procedure

The target was districts that had consistently perfomed at the bottom, middle and top for 5 consective years. The selection of the best and poor performing districts and schools, the Kenya Certificate of Primary Education (KCPE) results of the last five years available were used to rank districts (nationally) and schools (at district level). School performance in national examinations (a proxy indicator for student achievement) in Kenya varies by geographical and ecological regions of the country. Based on the distribution of school mean scores in a district, schools were categorized as low performing and high performing schools in any given year.

Specifically, six districts in Kenya, two that have consistently been ranked in the bottom 10% of the KCPE examinations over the past 4 years, two that have been consistently ranked within the middle 20% and another two that have consistently been ranked in the top 10% over the same period were selected for the study. A total of 72 schools, 12 in each of the six districts were randomly selected for the study. The schools selected for the study included six that had consistently been ranked in the bottom 20%, and six that had consistently been ranked in the top 20%. A further selection criterion for the schools ensured a mix of rural, peri-urban and urban schools in the sample. While taking a national representation in to account, the sample size was influenced by resource availability.

In the selected schools, grade six pupils were included. In case of multi-streams one grade was randomly selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

Survey instruments:

· Head teacher questionnaire: This instrument solicited information on school management, staffing, enrolment and parental participation in school affairs, among others.

· Teacher questionnaire: This solicited for information on biodata, qualification and training, discipline and syllabus coverage. The questionnaire was administered to grade six Maths, English and Science teachers.

· Learner questionnaire: The questionnaire solicited information on social economic background of the grade six learners and the school environment. This questionnaire was administered to grade six pupils in the selected schools.

Assessment tools:

· Mathematics teacher assessment tool, for grade six math teachers.

· Learner mathematics assessment tool, for pupils in the selected grade six streams.

Classroom observation and checklist tools:

· Classroom observation checklist: The checklist solicited information on availability of relevant textbooks, teacher and student made teaching and learning materials, other teaching resources, enrolment, learner absenteeism and lesson preparation.

· Opportunity to Learn (OTL) form: This form collected information from grade six exercise books that a learner used between January and November 2009. The information collected included date when the lesson was taught, and the main topic and subtopic as defined in grade six subject syllabus. In the absence of a main topic or subtopic, some contents of the lesson were recorded. These were later to be matched with main topic and subtopic from the s

Cleaning operations

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding

b) During data entry

c) Structure checking and completeness

d) Secondary editing

Response rate

Total of 72 schools, all the head teachers interviwed, 2436 pupils, 213 teachers

Data sources and search strategy: An extensive review was undertaken to describe and analyse findings from a wide range of published literature, including both peer-reviewed and grey sources.

Our inclusion criteria were: - Sample: Key stakeholders, particularly policy-makers and programme managers working in various sectors such as health, education, social development and so forth, who make high-level decisions in all types of disability policy or programming

• Phenomenon of interest: Papers that focus on how key stakeholders, who make high-level decisions, went about making a decision or a set of decisions about disability policy or programming

• Design: All types of research methodologies used to collect primary research evidence exploring disability and decision-making in LMICs

• Evaluation: Paper’s that focused on decision-making on disability-related matters–sources of information policy-makers and programme managers use to make decisions, other influences on their decision-making, and what processes are used to make decisions

• Research type: grey literature reports; peer-reviewed studies; qualitative, quantitative, or mixed-method studies exploring disability and decision-making that reported primary research evidence exploring disability and decision-making in LMICs

Our exclusion criteria were as follows: - Any papers that focused solely on HICs, were unrelated to disability, or did not concern high-level decision-making (e.g. respondents with disabilities describing access issues, limitations on their participation, difficulties at home, income problems, etc.) were excluded.

• Literature published before the year 1990 was not included. This is because we understand evidence-based decision-making to have gained recognition among medical professionals around this time.

We firstly conducted thorough searches of various databases of peer-reviewed material. The selection of databases were based on the senior author’s expert insight and extensive experience in disability and in review methods. These databases included Cumulative Index to Nursing and Allied Health Literature (CINAHL), Education Resources Information Center (ERIC), Scopus, Web of Science Social Sciences Citation Index, Medical Literature Analysis and Retrieval System Online (MEDLINE(R)), Excerpta Medica Database (Embase) Classic+Embase, PsycINFO, Cochrane, and Commonwealth Agricultural Bureaux (CAB) Global Health. Next, we conducted thorough online searches to gather relevant grey literature using the websites of the following large agencies and organisations: United Nations Educational, Scientific and Cultural Organization (UNESCO), World Bank, International Labour Organisation (ILO), World Health Organization (WHO), United Nations Children’s Fund (UNICEF), SightSavers, Christian Blind Mission (CBM), International, Disability Alliance, United Nations High Commissioner for Refugees (UNHCR), Humanity and Inclusion, Inclusion International, Global Policy Forum, Save the Children, World Vision International, International Rescue Committee (IRC) and Catholic Relief Services (CRS), as well as supplementary Google Scholar searches. To improve the reporting and methodological quality of this review, we used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and employed a comprehensive search strategy to mitigate for bias.

In the hand search of the grey literature, we selected relevant articles based on our inclusion and exclusion criteria. For our grey literature and Google Scholar hand search, the first 3 tabs/pages were searched using search strings. This is because search results yielded past the first page 3 became less specific and did not satisfy our inclusion criteria. All searches were conducted between the 27th of August 2023 and the 14th of December 2023.

Study selection process: During study selection, the authors used Rayyan.ai, a software platform specifically developed to facilitate collaborative evidence reviews in teams. Working in two pairs, we carefully assessed and selected literature based on our predetermined inclusion criteria, double screening all titles and abstracts. At the end of the full text article-screening phase, the reviewers reported a disagreement rate of 33%. These disagreements were resolved by the senior author.

Data extraction: All relevant information from each record was compiled in a spreadsheet. Information was extracted based on our research questions and included various details such as the authors’ names and publication year, publication title, study design, types of literature, category of participants, level or ambit of decision-making, sectors and topics covered, impairment or health condition of focus, setting, data collection methods, sample size, aims, and a summarised study findings section. In addition, we prepared a more specific study findings section(s) that considered sources of evidence used, barriers and facilitators to using these sources of evidence, and other influences on disability-inclusive decision-making. Three reviewers assembled the data into a spreadsheet which was used to double-extract all articles, where the third reviewer (second author) extracted 100% of the articles. Any disagreements or uncertainties were resolved during team meetings, also involving the fourth (senior) author.

Data analysis: We conducted data analysis that included numeric and qualitative content analyses. Qualitative content analysis was used to create codes and synthesise non-numerical data. Codes were created from inductively developed, condensed units of meaning using steps as described by Erlingsson and Brysiewicz (2017).

Numeric analysis focused on quantitatively summarising five sections: the aims of papers, sources of evidence used in decision-making, influences on decision-making aside from evidence, as well as the barriers to and facilitators of using the aforementioned sources of evidence in decision-making. To ensure methodological rigour, analysis was completed by three of the authors, who developed analytical codes for the five sections mentioned above and documented emerging patterns. The final codes were validated by a fourth member of the research team, with additional discussions on codes held until consensus was reached.

Data was derived from the following sources: - Braun AMB. Barriers to inclusive education in Tanzania’s policy environment: national policy actors’ perspectives. Compare. 2022;52(1):110–28. - Brydges C, Munro LT. The policy transfer of community-based rehabilitation in Gulu, Uganda. Disabil Soc. 2020 Oct 21;35(10):1596–617. - Chibaya G, Naidoo D, Govender P. Exploring the implementation of the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) in Namibia. Perspectives of policymakers and implementers. South African Journal of Occupational Therapy. 2022;52(1).
- Cleaver S, Hunt M, Bond V, Lencucha R. Disability Focal Point Persons and Policy Implementation Across Sectors: A Qualitative Examination of Stakeholder Perspectives in Zambia. Front Public Health. 2020 Sep 15;8. - Grimes P, dela Cruz. A. Mapping of Disability-Inclusive Education Practices in South Asia [Internet]. Kathmandu; 2021. Available from: www.unicef.org/rosa/
- Heidari A, Arab M, Damari B. A policy analysis of the national phenylketonuria screening program in Iran. BMC Health Serv Res. 2021 Dec 1;21(1). - International Disability Alliance. The Case Study on the Engagement of Organizations of Persons with Disabilities (DPOs) in Voluntary National Reviews [Internet]. 2017 [cited 2024 May 20]. Available from: https://www.internationaldisabilityalliance.org/sites/default/files/global_report_on_the_participation_of_organisations_of_persons_with_disabilities_dpos_in_vnr_processes.docx - Jerwanska V, Kebbie I, Magnusson L. Coordination of health and rehabilitation services for person with disabilities in Sierra Leone–a stakeholders’ perspective. Disabil Rehabil. 2023;45(11):1796–804. - Liechtenstein C. Still left behind? Growing up as a child with a disability in Somalia Disability inclusion: stakeholder, perception, and implementation of in Save the Children’s projects in Somalia.
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Abstract: MODIS Collection 6.1 yearly gap-filled Gross Primary Production (GPP) and Net Primary Production (NPP) data on the MODIS sinusoidal grid are taken from the netCDF files produced at ICDC, for which the bit-encoded quality information given in the HDF-files was already decoded, and re-gridded to build a global map of grid-cell mean GPP and NPP and their variances on a global equirectangular climate modeling grid (CMG). Only those GPP or NPP values are used where i) the cloud flag indicates either clear sky or assumed clear sky, where the MODLAND quality is good and where the confidence flag suggests best quality or good quality data. The confidence flag is provided as a grid-cell mean rounded value with fractions of the five original flags being provided for convenience. Cloud conditions are included in form of the primary cloud flag and the fraction this primary cloud flag occupies among the valid 500 m sinusoidal grid grid cells. Two separate layers of the number of valid grid cells of the 500 m sinusoidal grid are given, one is for the geophysical data and one is for the flags.

TableOfContents: grid cell mean Gross_Primary_Production (GPP); grid cell mean Net_Primary_Production (NPP); GPP standard deviation over grid cell; NPP standard deviation over grid cell; number of valid used GPP or NPP values per grid cell; number of valid used confidence and quality flag values per grid cell; grid cell mean confidence flag; fraction of confidence flag 0 in grid cell; fraction of confidence flag 1 in grid cell; fraction of confidence flag 2 in grid cell; fraction of confidence flag 3 in grid cell; fraction of confidence flag 4 in grid cell; primary cloud flag; primary cloud flag fraction

Technical Info: dimension: 720 columns x 360 rows x unlimited; temporalExtent_startDate: 2001-01-01; temporalExtent_endDate: 2023-12-31; temporalResolution: Yearly; spatialResolution: 0.5; spatialResolutionUnit: degrees; horizontalResolutionXdirection: 0.5; horizontalResolutionXdirectionUnit: degrees; horizontalResolutionYdirection: 0.5; horizontalResolutionYdirectionUnit: degrees; verticalResolution: none; verticalResolutionUnit: none; verticalStart: none; verticalEnd: none; instrumentName: MODerate Resolution Spectroradiometer (MODIS); instrumentType: visible_to_infrared_spectroradiometer; instrumentLocation: Earth Observation Satellite (EOS) Terra; instrumentProvider: NOAA/NASA

Methods: [1] Running, S. W., and M. Zhao, Users Guide Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm, (For Collection 6), Version 4.0, January 2, 2019; [2] Running, S. W., R. R. Nemani, F. A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, A continuous satellite-derived measure of global terrestrial primary production. Bioscience, 54(6), 547-560, 2004; [3] Running, S. W., A measurable planetary boundary layer for the biosphere. Science, 337(6101), 1458-1459, 2012; [4] Zhao, M., F. A. Heinsch, R. R. Nemani, and S. W. Running, Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95(2), 164-176, 2005

Units: Units for all variables (see TableOfContents): kg C m-2; kg C m-2; kg C m-2; kg C m-2; 1; 1; 1; percent; percent; percent; percent; percent; 1; percent

geoLocations: westBoundLongitude: -180.0 degrees East; eastBoundLongitude: 180.0 degrees East; southBoundLatitude: -90.0 degrees North; northBoundLatitude: 90.0 degrees North; geoLocationPlace: global on land

Size: (files are packed into one zip-archive)

• 2001-2023: 1 file per year, each approximately 12975000 bytes

Format: netCDF

DataSources:

Original data on sinusoidal grid tiles in hdf-format: https://doi.org/10.5067/MODIS/MOD17A3HGF.061 (last accessed: 2024-06-03), see also https://lpdaac.usgs.gov/products/mod17a3hgfv061/ (last accessed: 2024-06-03)

Data on sinusoidal grid tiles in netCDF format: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-07-09) or https://doi.org/10.25592/uhhfdm.14633 (last accessed: 2024-07-09).

Contact: stefan.kern (at) uni-hamburg.de

Web page: https://www.cen.uni-hamburg.de/en/icdc/data/land/modis-primaryproduction.html (last accessed: 2024-07-09)

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebookv2.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydatav2.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydatav2.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 95

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.