In this project, we aimed to increase what is known about the negative effects of maternal depression and anxiety disorders (MDAD) on the mental health outcomes of children. Mental health is a topical area of research that is receiving increasing attention in the media and is one of five ESRC strategic priorities for investment. The main aim of the project was to help develop an understanding of how mental depression and anxiety disorders are transmitted from one generation to the next and ultimately help to design interventions better able to reduce the consequences of maternal mental health for children. We have used data from QResearch, a large consolidated database derived from anonymized health records from general practices in England matched with hospital administrative data, the Hospital Episode Statistics (HES). Further information is available under Related Resources.

Problems relating to Maternal Depression and Anxiety Disorders (MDAD) are common and are known to affect child health and development. In the UK, the cost of perinatal mental health problems has been estimated at £8.1 billion for each birth cohort of children, and 72 percent of this cost is related to the direct impact on the children.

The overarching aim of our proposed research is to examine the effect of MDAD on child health outcomes, with a special focus on the role that MDAD plays in the development of child depression and anxiety disorders (CDAD) in adolescence. In particular, this research will provide robust empirical evidence to understand how depression and anxiety disorders are transmitted from one generation to the next and to help design interventions aimed at reducing the negative consequences of poor maternal mental health for children.

To achieve this aim, we will address the following research questions:

1) Are the negative effects of MDAD on children exclusively explained by genetic transmission and family background characteristics? Or are these negative effects also explained by changes in the child's home environment? If the transmission of mental and anxiety disorders is explained exclusively by genetic traits and family background characteristics, then interventions targeted at reducing the negative effect of MDAD on maternal behaviour, e.g. through cognitive behavioural therapy, would be ineffective. On the contrary, evidence on significant effects of MDAD after controlling for genetic and family background characteristics would suggest that MDAD can lead to changes in the child home environment, e.g. changes in maternal behaviour, harsher parenting style and lower time investments in the child, with negative consequences on children.

2) Do school policies and health practices have a role in attenuating the negative effect of maternal depression on children? We will answer this research question by focusing on whether starting school earlier harms or protects children who are exposed to MDAD, and on whether an early diagnosis of maternal depression can attenuate the negative effects suffered by children.

We will develop and use state-of-the-art estimation methods in combination with a novel administrative dataset covering general practices and hospitals created by merging two population-based health databases from England - namely QResearch and Hospital Episode Statistics. Using this merged database, we will create a longitudinal household dataset that will allow us to study the mental health of mothers and their children at different stages of the children's lives up to adolescence.

We are a multi-disciplinary team from the Universities of Oxford and York, consisting of experts in applied econometric methods, child and maternal mental health, psychology, general practice, and on the data that we plan to utilise.

We will translate our research findings into advice for policy-makers to help them design new interventions aimed at achieving better outcomes for patients suffering from maternal mental health issues and their children. Our research will also have an impact on health practitioners, psychologists, academics and charities working with mothers and children. We will produce papers aimed at academics as well as non-technical outputs to engage with policy-makers and a non-academic audience. Furthermore, by sharing and explaining our data and estimation methods to academics, we will build capacity for further research based on large health datasets.

The final central element of the project will be to build the capacity of early career researchers to undertake and lead large interdisciplinary projects.

Abstract (en): The National Center for Early Development and Learning (NCEDL) combined the data of two major studies in order to understand variations among state-funded pre-kindergarten (pre-k) programs and in turn, how these variations relate to child outcomes at the end of pre-k and in kindergarten. The Multi-State Study of Pre-Kindergarten and the State-Wide Early Education Programs (SWEEP) Study provide detailed information on pre-kindergarten teachers, children, and classrooms in 11 states. By combining data from both studies, information is available from 721 classrooms and 2,982 pre-kindergarten children in these 11 states. Pre-kindergarten data collection for the Multi-State Study of Pre-Kindergarten took place during the 2001-2002 school year in six states: California, Georgia, Illinois, Kentucky, New York, and Ohio. These states were selected from among states that had committed significant resources to pre-k initiatives. States were selected to maximize diversity with regard to geography, program settings (public school or community setting), program intensity (full-day vs. part-day), and educational requirements for teachers. In each state, a stratified random sample of 40 centers/schools was selected from the list of all the school/centers or programs (both contractors and subcontractors) provided to the researchers by each state's department of education. In total, 238 sites participated in the fall and two additional sites joined the study in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2002, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Pre-kindergarten data collection for the SWEEP Study took place during the 2003-2004 school year in five states: Massachusetts, New Jersey, Texas, Washington, and Wisconsin. These states were selected to complement the states already in the Multi-State Study of Pre-K by including programs with significantly different funding models or modes of service delivery. In each of the five states, 100 randomly selected state-funded pre-kindergarten sites were recruited for participation in the study from a list of all sites provided by the state. In total, 465 sites participated in the fall. Two sites declined to continue participation in the spring, resulting in 463 sites participating in the spring. Participating teachers helped the data collectors recruit children into the study by sending recruitment packets home with all children enrolled in the classroom. On the first day of data collection, the data collectors determined which of the children were eligible to participate. Eligible children were those who (1) would be old enough for kindergarten in the fall of 2004, (2) did not have an Individualized Education Plan, according to the teacher, and (3) spoke English or Spanish well enough to understand simple instructions, according to the teacher. Demographic information collected across both studies includes race, teacher gender, child gender, family income, mother's education level, and teacher education level. The researchers also created a variable for both the child-level data and the class-level data which allows secondary users to subset cases according to either the Multi-State or SWEEP study. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed recodes and/or calculated derived variables.. Response Rates: Multi-State: Of the 40 sites per state, 78 percent of eligible sites agreed to participate (fall of pre-k, n = 238). For fall of pre-k (n = 238), 94 percent of the one classroom per site selected agreed to participate. For fall (n = 940) and spring (n = 960) of pre-k, 61 percent of the parents of eligible children consented.; SWEEP: Of the 10...

We create a synthetic administrative dataset to be used in the development of the R package for calculating quality indicators for administrative data (see: https://github.com/sook-tusk/qualadmin) that mimic the properties of a real administrative dataset according to specifications by the ONS. Taking over 1 million records from a synthetic 1991 UK census dataset, we deleted records, moved records to a different geography and duplicated records to a different geography according to pre-specified proportions for each broad ethnic group (White, Non-white) and gender (males, females). The final size of the synthetic administrative data was 1033664 individuals.

National Statistical Institutes (NSIs) are directing resources into advancing the use of administrative data in official statistics systems. This is a top priority for the UK Office for National Statistics (ONS) as they are undergoing transformations in their statistical systems to make more use of administrative data for future censuses and population statistics. Administrative data are defined as secondary data sources since they are produced by other agencies as a result of an event or a transaction relating to administrative procedures of organisations, public administrations and government agencies. Nevertheless, they have the potential to become important data sources for the production of official statistics by significantly reducing the cost and burden of response and improving the efficiency of such systems. Embedding administrative data in statistical systems is not without costs and it is vital to understand where potential errors may arise. The Total Administrative Data Error Framework sets out all possible sources of error when using administrative data as statistical data, depending on whether it is a single data source or integrated with other data sources such as survey data. For a single administrative data, one of the main sources of error is coverage and representation to the target population of interest. This is particularly relevant when administrative data is delivered over time, such as tax data for maintaining the Business Register. For sub-project 1 of this research project, we develop quality indicators that allow the statistical agency to assess if the administrative data is representative to the target population and which sub-groups may be missing or over-covered. This is essential for producing unbiased estimates from administrative data. Another priority at statistical agencies is to produce a statistical register for population characteristic estimates, such as employment statistics, from multiple sources of administrative and survey data. Using administrative data to build a spine, survey data can be integrated using record linkage and statistical matching approaches on a set of common matching variables. This will be the topic for sub-project 2, which will be split into several topics of research. The first topic is whether adding statistical predictions and correlation structures improves the linkage and data integration. The second topic is to research a mass imputation framework for imputing missing target variables in the statistical register where the missing data may be due to multiple underlying mechanisms. Therefore, the third topic will aim to improve the mass imputation framework to mitigate against possible measurement errors, for example by adding benchmarks and other constraints into the approaches. On completion of a statistical register, estimates for key target variables at local areas can easily be aggregated. However, it is essential to also measure the precision of these estimates through mean square errors and this will be the fourth topic of the sub-project. Finally, this new way of producing official statistics is compared to the more common method of incorporating administrative data through survey weights and model-based estimation approaches. In other words, we evaluate whether it is better 'to weight' or 'to impute' for population characteristic estimates - a key question under investigation by survey statisticians in the last decade.

In order to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by cities and local communities in the developing countries, the SHLC team conducted a major household survey followed by a neighbourhood focus group interview in seven Asian and African countries from late 2021 to early 2022. In each country the study includes two case study cities: one large city and one smaller regional cities. Within each case study cities, neighbourhoods were identified and categorised into five income and wealth bands: the rich, upper middle income, middle income, lower middle and low income neighbourhoods.

A household survey was carried out face to face by trained interviewers with a random adult member of the household. The 20 page common questionnaire was designed and adopted by all teams, which cover topics of housing, residence, living conditions, migration, education, health, neighbourhood infrastructure, facilities, governance and relations, income and employments, gender equality and impacts from Covid-19. The sample was distributed in the city to representative the five neighbourhood types. The survey was completed in 13 of the 14 case study cities (fieldwork in Chongqing in China was delayed by the Covid-19 lockdowns and implemented in August 2023). The target sample for each city was 1000; the total sample in the database (SPSS and STATA) include 14245 households.

The survey was followed by focus group interviews. A carefully designed and agreed common interview guide was used by all team. The target was to have one focus group for one neighbourhood in each income band in each city. A total of 74 focus group interviews were conducted (Fieldwork in Datong and Chongqing in China was delayed). The transcripts are the qualitative data shared here.

The Centre for Sustainable, Healthy and Learning Cities and Neighbourhoods (SHLC) was funded by UKRI Global Challenge Research Fund (GCRF) from 2017 to 2023. Its main aim was to grow research capability to meet the challenges faced by developing countries (Grow). SHLC, led by University of Glasgow, was set up as an international collaborative research centre to address urban challenges across communities in Africa and Asia. Its work contributed to three UN 2030 Sustainable Development Goals: 11 - Make cities and human settlements sustainable; 3 - Ensure healthy lives for all; 4 - Ensure inclusive and equitable quality education for all. SHLC brought together the expertise of urban studies, education, health, geography, planning and data science from nine institutions in eight countries. Its international partners included: Ifakara Health Institute (Tanzania), Khulna University (Bangladesh), Nankai University (China), National Institute of Urban Affairs (India), The Human Sciences Research Council and University of Witwatersrand (South Africa), The University of the Philippines and The University of Rwanda. SHLC working programme had two streams of work and eight specific task packages. Stream one included four Capacity Strengthening Packages which involved the training of over 100 researchers and enhancing the associated academic networks. Steam two work consisted of four Research Task Packages. The co-designed research programme adopted a common research framework in all seven countries (14 case study cities), aiming to bring a thorough and comprehensive understanding of social, economic and environmental sustainability challenges faced by these cities and local communities. Apart from policy reviews, secondary data analysis, the project employed two major primary data collection methods – household questionnaire survey and neighbourhood focus groups. The team have overcome many challenges brought by the Covid-19 pandemics and completed the household survey in 13 cities with a total sample size of 14245, which covered five different types of neighbourhoods ranging from the rich to the poor. The team also completed 74 neighbourhood focus group interviews. Data collection was carried out from late 2021 to early 2022. Huge resources and researchers’ time were dedicated to coordinate, collect, translate, clean and merge these quantitative and qualitative data.

Regression analysis of mean height for age and stunting prevalence (HAZ

(Link to Metadata) FacilitiesSchools_PTSCHOOL is designed to provide point locations of every Vermont School along with the established school ID (PSID) for geographic analysis of school statistical data. The 2020 update was conducted without the benefit of Agency of Education (AOE) primary and secondary school registry information as an interim solution for potential short term pandemic related support. All features are now coincident with E911 sites. Once the dust settles a copy of the registry should be used in an additional update effort to ensure completeness. Independent schools were added from the "Directory of Vermont Approved and Recognized Independent Schools, Approved Tutorials and Distance Learning Schools, Other Educational Programs, and State-Operated Facilities". Originally created in 1997 by Vermont's Regional Planning Commissions, each school name and location was verified by each School Supervisory Union and merged into a single coverage by VCGI. The next update came in 2003 with VCGI using an updated list of schools from the VT Department of Education that was subsequentially validated in 2004 by the DOE. Skipping forward to 2014, the DOE updated the representations of Vermont's public K12 school locations for school year 2014 and VCGI split the layer into two distinct feature classes: 1) VT School Locations - K-12; and 2) VT School Locations - Post-Secondary.

BackgroundAn understanding of the resources which engineering students use to write their academic papers provides information about student behaviour as well as the effectiveness of information literacy programs designed for engineering students. One of the most informative sources of information which can be used to determine the nature of the material that students use is the bibliography at the end of the students’ papers. While reference list analysis has been utilised in other disciplines, few studies have focussed on engineering students or used the results to improve the effectiveness of information literacy programs. Gadd, Baldwin and Norris (2010) found that civil engineering students undertaking a finalyear research project cited journal articles more than other types of material, followed by books and reports, with web sites ranked fourth. Several studies, however, have shown that in their first year at least, most students prefer to use Internet search engines (Ellis & Salisbury, 2004; Wilkes & Gurney, 2009).PURPOSEThe aim of this study was to find out exactly what resources undergraduate students studying civil engineering at La Trobe University were using, and in particular, the extent to which students were utilising the scholarly resources paid for by the library. A secondary purpose of the research was to ascertain whether information literacy sessions delivered to those students had any influence on the resources used, and to investigate ways in which the information literacy component of the unit can be improved to encourage students to make better use of the resources purchased by the Library to support their research.DESIGN/METHODThe study examined student bibliographies for three civil engineering group projects at the Bendigo Campus of La Trobe University over a two-year period, including two first-year units (CIV1EP – Engineering Practice) and one-second year unit (CIV2GR – Engineering Group Research). All units included a mandatory library session at the start of the project where student groups were required to meet with the relevant faculty librarian for guidance. In each case, the Faculty Librarian highlighted specific resources relevant to the topic, including books, e-books, video recordings, websites and internet documents. The students were also shown tips for searching the Library catalogue, Google Scholar, LibSearch (the LTU Library’s research and discovery tool) and ProQuest Central. Subject-specific databases for civil engineering and science were also referred to. After the final reports for each project had been submitted and assessed, the Faculty Librarian contacted the lecturer responsible for the unit, requesting copies of the student bibliographies for each group. References for each bibliography were then entered into EndNote. The Faculty Librarian grouped them according to various facets, including the name of the unit and the group within the unit; the material type of the item being referenced; and whether the item required a Library subscription to access it. A total of 58 references were collated for the 2010 CIV1EP unit; 237 references for the 2010 CIV2GR unit; and 225 references for the 2011 CIV1EP unit.INTERIM FINDINGSThe initial findings showed that student bibliographies for the three group projects were primarily made up of freely available internet resources which required no library subscription. For the 2010 CIV1EP unit, all 58 resources used were freely available on the Internet. For the 2011 CIV1EP unit, 28 of the 225 resources used (12.44%) required a Library subscription or purchase for access, while the second-year students (CIV2GR) used a greater variety of resources, with 71 of the 237 resources used (29.96%) requiring a Library subscription or purchase for access. The results suggest that the library sessions had little or no influence on the 2010 CIV1EP group, but the sessions may have assisted students in the 2011 CIV1EP and 2010 CIV2GR groups to find books, journal articles and conference papers, which were all represented in their bibliographiesFURTHER RESEARCHThe next step in the research is to investigate ways to increase the representation of scholarly references (found by resources other than Google) in student bibliographies. It is anticipated that such a change would lead to an overall improvement in the quality of the student papers. One way of achieving this would be to make it mandatory for students to include a specified number of journal articles, conference papers, or scholarly books in their bibliographies. It is also anticipated that embedding La Trobe University’s Inquiry/Research Quiz (IRQ) using a constructively aligned approach will further enhance the students’ research skills and increase their ability to find suitable scholarly material which relates to their topic. This has already been done successfully (Salisbury, Yager, & Kirkman, 2012)CONCLUSIONS & CHALLENGESThe study shows that most students rely heavily on the free Internet for information. Students don’t naturally use Library databases or scholarly resources such as Google Scholar to find information, without encouragement from their teachers, tutors and/or librarians. It is acknowledged that the use of scholarly resources doesn’t automatically lead to a high quality paper. Resources must be used appropriately and students also need to have the skills to identify and synthesise key findings in the existing literature and relate these to their own paper. Ideally, students should be able to see the benefit of using scholarly resources in their papers, and continue to seek these out even when it’s not a specific assessment requirement, though it can’t be assumed that this will be the outcome.REFERENCESEllis, J., & Salisbury, F. (2004). Information literacy milestones: building upon the prior knowledge of first-year students. Australian Library Journal, 53(4), 383-396.Gadd, E., Baldwin, A., & Norris, M. (2010). The citation behaviour of civil engineering students. Journal of Information Literacy, 4(2), 37-49.Salisbury, F., Yager, Z., & Kirkman, L. (2012). Embedding Inquiry/Research: Moving from a minimalist model to constructive alignment. Paper presented at the 15th International First Year in Higher Education Conference, Brisbane. Retrieved from http://www.fyhe.com.au/past_papers/papers12/Papers/11A.pdfWilkes, J., & Gurney, L. J. (2009). Perceptions and applications of information literacy by first year applied science students. Australian Academic & Research Libraries, 40(3), 159-171.

International students’ mental health has become an increasing concern in recent years, as more students leave their country for better education. They experience a wide range of challenges while studying abroad that have an impact on their psychological well-being. These challenges can include language obstacles, cultural differences, homesickness, financial issues and other elements that could severely impact the mental health of international students. Given the limited research on the demographic, cultural, and psychosocial variables that influence international students’ mental health, and the scarcity of studies on the use of machine learning algorithms in this area, this study aimed to analyse data to understand the demographic, cultural factors, and psychosocial factors that impact mental health of international students. Additionally, this paper aimed to build a machine learning-based model for predicting depression among international students in the United Kingdom. This study utilized both primary data gathered through an online survey questionnaire targeted at international students and secondary data was sourced from the ’A Dataset of Students’ Mental Health and Help-Seeking Behaviors in a Multicultural Environment,’ focusing exclusively on international student data within this dataset. We conducted data analysis on the primary data and constructed models using the secondary data for predicting depression among international students. The secondary dataset is divided into training (70%) and testing (30%) sets for analysis, employing four machine learning models: Logistic Regression, Decision Tree, Random Forest, and K Nearest Neighbor. To assess each algorithm’s performance, we considered metrics such as Accuracy, Sensitivity, Specificity, Precision and AU-ROC curve. This study identifies significant demographic variables (e.g., loan status, gender, age, marital status) and psychosocial factors (financial difficulties, academic stress, homesickness, loneliness) contributing to international students’ mental health. Among the machine learning models, the Random Forest model demonstrated the highest accuracy, achieving an 80% accuracy rate in predicting depression.

Genomics England are striving to improve the clinical data provided for its researchers. We understand the value of accurate and granular clinical data, especially in the context of cancer.

In order to deliver this, we are planning a series of pilot datasets, aiming to incorporate additional clinical data provided by Public Health England cancer registry (NCRAS). Genomics England will aim to deliver cancer specific datasets, with the initial focus being on providing a broad pathological understanding. This will aim to incorporate data points such as molecular mutations and resection margins in pathology reports. The focus will then incorporate radiological imaging reports and finally focus on live/ up-to-date clinical data. In addition, we are also including the date each participant was last seen alive (data provided up to October 2020) and dates and causes of death to aid with outcomes.

It must be stressed that this work is a development process, and we are working in unison with NCRAS to progress this. Whilst we do not possess the extensive experience and resource of Public Health England, we are developing a natural language based algorithm for focused data extraction. NCRAS have a dedicated team to curating clinical data and the gold standard remains the NCRAS curated tables. However, for this dataset to improve and move forward, Genomics England are keen for feedback and for you to highlight areas for improvement.

You will note subtle differences to the structure of the table compared to the curated NCRAS tables and thus additional data dictionaries have been provided. Genomics England hopes to continue developing this uncurated live dataset with feedback and look forward to hearing your thoughts. Please reach out to us with related thoughts and suggestions via the Genomics England Service Desk, including "cancer_specific_datasets_pilot" in the title of your enquiry.

With the addition of the new pathology_reports dataset introduced in v16, the aml_path_reports and testes_path_reports datasets have been deprecated in v17.

PurposeThe aim of this study was to investigate the mediating effects of health literacy on the relationship between frailty and health-related quality of life (HRQOL) among community-dwelling older adults.MethodsThis study used the Korean Frailty and Aging Cohort Database (KFACD) for secondary data analysis. We selected data from 1,631 people without missing main variable values for analysis. Frailty was determined based on the modified Fried’s phenotype [MFP], and HRQOL was measured using the Korean version of the 5-level EuroQol questionnaire (EQ-5D-5L). Health literacy was assessed using the questions on the Behavioral Risk Factor Surveillance System (BRFSS) used by the U.S. Center for Disease Control and Prevention. To examine the mediating role of health literacy in the relationship between frailty and HRQOL, Baron & Kenny’s three-step mediating effect verification method was utilized.ResultsThe participants had a mean frailty score of 1.37±1.02, health literacy score of 8.56±2.59, and HRQOL score of 0.84±0.10. Frailty was negatively correlated with health literacy (r = -0.27, p < .001) and HRQOL (r = -0.32, p < .001), while health literacy was positively correlated with HRQOL (r = 0.34, p < .001). We observed that health literacy played a partial mediating role in the relationship between frailty and HRQOL.ConclusionTo increase older adults’ HRQOL, measures that directly prevent and manage frailty as well as interventions that target the enhancement of health literacy are needed.

This collection comprises unaltered data files downloaded from https://eddataexpress.ed.gov/download/data-library on February 6, 2025. The original access page consisted of a table with category filters, which provided links to data ZIP files containing the specified data fields. This table has been saved into tabular data formats here in the Index folder, with the original web links replaced with the matching ZIP filename only, which essentially replicates the functionality of the original web page in a downloadable format.In the website's underlying file structure, the original ZIP files were nested within folders named according to the format EID_####, apparently to avoid conflicts between files with the same name. These seeming duplications might have been due to updates or revisions that had to be made to a data file. To preserve this original order, the ZIP files were renamed by appending the EID number to their original file name. The files were not otherwise unzipped or altered in any way from their original state.At the time of download, the page at https://eddataexpress.ed.gov/download/data-library displayed the following two notices in red:"The COVID-19 pandemic disrupted the collection and reporting of data on EDE, beginning in SY 2019-20. The Department urges abundant caution when using the data and recommends reviewing the relevant data notes prior to use or interpretation. This includes data on state assessments, graduation rates, and chronic absenteeism.""WARNING: The data library functionality has stopped working temporarily for many SY2122 school files. Please go to the download tool page to download your data of interest. We apologize for the inconvenience."--------------------The "About Us" page from the ED Data Express website had this to say about its resources:Purpose of ED Data ExpressED Data Express is a website designed to improve the public's ability to access and explore high-value state- and district-level education data collected by the U.S. Department of Education. The site is designed to be interactive and to present the data in a clear, easy-to-use manner, with options to download information into Excel or to explore the data within the site's grant program dashboards. The site currently includes data from EDFacts, Consolidated State Performance Reports (CSPR), and the Department's Budget Service office. For more information about these topics, please visit the following web pages:https://www2.ed.gov/about/inits/ed/edfacts/index.html [see below for the text of the linked page]https://www2.ed.gov/about/offices/list/om/fs_po/ofo/budget-service.html [this URL was dead at the time of download]Using the SiteED Data Express includes two sections that allow users to access and view the data: (1) grant program data dashboards and (2) download functionality. The grant program data dashboards provide a snapshot of information on the funding, participation and performance of some of the grant programs administered by the U.S. Department of Education's Office of Elementary and Secondary Education. The dashboards are interactive and update depending on the program, state and school year selected. Additional information is provided through data notes as well as through the small "i" icon. The download functionality allows users to build customized tables of data and contain more data than what is available via the dashboards. The download functionality also allows users to download data notes which provide important caveats and contextual information to consider when using the data. Data Included and Frequency of UpdatesThe site currently includes funding, participation and performance data from school years 2010-11 to 2016-17 on formula grant programs administered in the Office of Elementary and Secondary Education. Additional data and data notes will be added to the site over time. Quality Control and Personally Identifiable InformationAll CSPR and EDFacts data are self-reported by each state. The U.S. Department of Education conducts a review of the data and provides feedback to states, but it is ultimately states’ responsibility to verify and certify that their data are correct. Please note that during the reporting years represented on this site, the Office of Elementary and Secondary Education in collaboration with EDFacts and SEAs have wor

This dataset results from the enrichment of the following game: * Added value indicators for vocational schools, published on 14 September 2013 by the Ministry of National Education on the data.gouv.fr platform: https://www.data.gouv.fr/fr/dataset/indicateurs-de-valeur-ajoutee-des-lycees-d-enseignement-professionnel-00000000 statistical data: the value-added indicators of high schools make it possible to evaluate the specific action of each high school. They are based on the results of the baccalaureate students and their educational background in the institution. Professional high schools, public and private under contract, are concerned. It is by no means a ranking but a cross-view of the three indicators and the corresponding “added values”.* Compared to the latter game, the statistics are modeled in the form of data cubes (Datacube).

Regression analysis of mean haemoglobin and the prevalence of anaemia at baseline and end-line in children aged 6–23 months.

Abstract

The 2010 NEDS is similar to the 2004 Nigeria DHS EdData Survey (NDES) in that it was designed to provide information on education for children age 4–16, focusing on factors influencing household decisions about children’s schooling. The survey gathers information on adult educational attainment, children’s characteristics and rates of school attendance, absenteeism among primary school pupils and secondary school students, household expenditures on schooling and other contributions to schooling, and parents’/guardians’ perceptions of schooling, among other topics.The 2010 NEDS was linked to the 2008 Nigeria Demographic and Health Survey (NDHS) in order to collect additional education data on a subset of the households (those with children age 2–14) surveyed in the 2008 Nigeria DHS survey. The 2008 NDHS, for which data collection was carried out from June to October 2008, was the fourth DHS conducted in Nigeria (previous surveys were implemented in 1990, 1999, and 2003).

The goal of the 2010 NEDS was to follow up with a subset of approximately 30,000 households from the 2008 NDHS survey. However, the 2008 NDHS sample shows that of the 34,070 households interviewed, only 20,823 had eligible children age 2–14. To make statistically significant observations at the State level, 1,700 children per State and the Federal Capital Territory (FCT) were needed. It was estimated that an additional 7,300 households would be required to meet the total number of eligible children needed. To bring the sample size up to the required target, additional households were screened and added to the overall sample. However, these households did not have the NDHS questionnaire administered. Thus, the two surveys were statistically linked to create some data used to produce the results presented in this report, but for some households, data were imputed or not included.

Geographic coverage

National

Analysis unit

Households Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The eligible households for the 2010 NEDS are the same as those households in the 2008 NDHS sample for which interviews were completed and in which there is at least one child age 2-14, inclusive. In the 2008 NDHS, 34,070 households were successfully interviewed, and the goal here was to perform a follow-up NEDS on a subset of approximately 30,000 households. However, records from the 2008 NDHS sample showed that only 20,823 had children age 4-16. Therefore, to bring the sample size up to the required number of children, additional households were screened from the NDHS clusters.

The first step was to use the NDHS data to determine eligibility based on the presence of a child age 2-14. Second, based on a series of precision and power calculations, RTI determined that the final sample size should yield approximately 790 households per State to allow statistical significance for reporting at the State level, resulting in a total completed sample size of 790 × 37 = 29,230. This calculation was driven by desired estimates of precision, analytic goals, and available resources. To achieve the target number of households with completed interviews, we increased the final number of desired interviews to accommodate expected attrition factors such as unlocatable addresses, eligibility issues, and non-response or refusal. Third, to reach the target sample size, we selected additional samples from households that had been listed by NDHS but had not been sampled and visited for interviews. The final number of households with completed interviews was 26,934 slightly lower than the original target, but sufficient to yield interview data for 71,567 children, well above the targeted number of 1,700 children per State.

Mode of data collection

Face-to-face [f2f]

Research instrument

The four questionnaires used in the 2004 Nigeria DHS EdData Survey (NDES)— 1. Household Questionnaire 2. Parent/Guardian Questionnaire 3. Eligible Child Questionnaire 4. Independent Child Questionnaire—formed the basis for the 2010 NEDS questionnaires. These are all available in Appendix D of the survey report available under External Resources.

More than 90 percent of the questionnaires remained the same; for cases where there was a clear justification or a need for a change in item formulation or a specific requirement for additional items, these were updated accordingly. A one day workshop was convened with the NEDS Implementation Team and the NDES Advisory Committee to review the instruments and identify any needed revisions, additions, or deletions. Efforts were made to collect data to ease integration of the 2010 NEDS data into the FMOE’s national education management information system. Instrument issues that were identified as being problematic in the 2004 NDES as well as items identified as potentially confusing or difficult were proposed for revision. Issues that USAID, DFID, FMOE, and other stakeholders identified as being essential but not included in the 2004 NDES questionnaires were proposed for incorporation into the 2010 NEDS instruments, with USAID serving as the final arbiter regarding questionnaire revisions and content.

General revisions accepted into the questionnaires included the following: - A separation of all questions related to secondary education into junior secondary and senior secondary to reflect the UBE policy - Administration of school-based questions for children identified as attending pre-school - Inclusion of questions on disabilities of children and parents - Additional questions on Islamic schooling - Revision to the literacy question administration to assess English literacy for children attending school - Some additional questions on delivery of UBE under the financial questions section

Upon completion of revisions to the English-language questionnaires, the instruments were translated and adapted by local translators into three languages—Hausa, Igbo, and Yoruba—and then back-translated into English to ensure accuracy of the translation. After the questionnaires were finalized, training materials used in the 2004 NDES and developed by Macro International, which included training guides, data collection manuals, and field observation materials, were reviewed. The materials were updated to reflect changes in the questionnaires. In addition, the procedures as described in the manuals and guides were carefully reviewed. Adjustments were made, where needed, based on experience on large-scale survey and lessons learned from the 2004 NDES and the 2008 NDHS, to ensure the highest quality data capture.

Cleaning operations

Data processing for the 2010 NEDS occurred concurrently with data collection. Completed questionnaires were retrieved by the field coordinators/trainers and delivered to NPC in standard envelops, labeled with the sample identification, team, and State name. The shipment also contained a written summary of any issues detected during the data collection process. The questionnaire administrators logged the receipt of the questionnaires, acknowledged the list of issues, and acted upon them if required. The editors performed an initial check on the questionnaires, performed any coding of open-ended questions (with possible assistance from the data entry operators), and left them available to be assigned to the data entry operators. The data entry operators entered the data into the system, with the support of the editors for erroneous or unclear data.

Experienced data entry personnel were recruited from those who have performed data entry activities for NPC on previous studies. The data entry teams composed a data entry coordinator, supervisor and operators. Data entry coordinators oversaw the entire data entry process from programming and training to final data cleaning, made assignments, tracked progress, and ensured the quality and timeliness of the data entry process. Data entry supervisors were on hand at all times to ensure that proper procedures were followed and to help editors resolve any uncovered inconsistencies. The supervisors controlled incoming questionnaires, assigned batches of questionnaires to the data entry operators, and managed their progress. Approximately 30 clerks were recruited and trained as data entry operators to enter all completed questionnaires and to perform the secondary entry for data verification. Editors worked with the data entry operators to review information flagged as “erroneous” or “dubious” in the data entry process and provided follow up and resolution for those anomalies.

The data entry program developed for the 2004 NDES was revised to reflect the revisions in the 2010 NEDS questionnaire. The electronic data entry and reporting system ensured internal consistency and inconsistency checks.

Response rate

A very high overall response rate of 97.9 percent was achieved with interviews completed in 26,934 households out of a total of 27,512 occupied households from the original sample of 28,624 households. The response rates did not vary significantly by urban–rural (98.5 percent versus 97.6 percent, respectively). The response rates for parent/guardians and children were even higher, and the rate for independent children was slightly lower than the overall sample rate, 97.4 percent. In all these cases, the urban/rural differences were negligible.

Sampling error estimates

Estimates derived 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

These interview data are part of the project "Looking for data: information seeking behaviour of survey data users", a study of secondary data users’ information-seeking behaviour. The overall goal of this study was to create evidence of actual information practices of users of one particular retrieval system for social science data in order to inform the development of research data infrastructures that facilitate data sharing. In the project, data were collected based on a mixed methods design. The research design included a qualitative study in the form of expert interviews and – building on the results found therein – a quantitative web survey of secondary survey data users. For the qualitative study, expert interviews with six reference persons of a large social science data archive have been conducted. They were interviewed in their role as intermediaries who provide guidance for secondary users of survey data. The knowledge from their reference work was expected to provide a condensed view of goals, practices, and problems of people who are looking for survey data. The anonymized transcripts of these interviews are provided here. They can be reviewed or reused upon request. The survey dataset from the quantitative study of secondary survey data users is downloadable through this data archive after registration. The core result of the Looking for data study is that community involvement plays a pivotal role in survey data seeking. The analyses show that survey data communities are an important determinant in survey data users' information seeking behaviour and that community involvement facilitates data seeking and has the capacity of reducing problems or barriers. The qualitative part of the study was designed and conducted using constructivist grounded theory methodology as introduced by Kathy Charmaz (2014). In line with grounded theory methodology, the interviews did not follow a fixed set of questions, but were conducted based on a guide that included areas of exploration with tentative questions. This interview guide can be obtained together with the transcript. For the Looking for data project, the data were coded and scrutinized by constant comparison, as proposed by grounded theory methodology. This analysis resulted in core categories that make up the "theory of problem-solving by community involvement". This theory was exemplified in the quantitative part of the study. For this exemplification, the following hypotheses were drawn from the qualitative study: (1) The data seeking hypotheses: (1a) When looking for data, information seeking through personal contact is used more often than impersonal ways of information seeking. (1b) Ways of information seeking (personal or impersonal) differ with experience. (2) The experience hypotheses: (2a) Experience is positively correlated with having ambitious goals. (2b) Experience is positively correlated with having more advanced requirements for data. (2c) Experience is positively correlated with having more specific problems with data. (3) The community involvement hypothesis: Experience is positively correlated with community involvement. (4) The problem solving hypothesis: Community involvement is positively correlated with problem solving strategies that require personal interactions.

This research used data mining approaches to better understand factors affecting the formation of secondary organic aerosol (SOA). Although numerous laboratory and computational studies have been completed on SOA formation, it is still challenging to determine factors that most influence SOA formation. Experimental data were based on previous work described by Offenberg et al. (2017), where volume concentrations of SOA were measured in 139 laboratory experiments involving the oxidation of single hydrocarbons under different operating conditions. Three different data mining methods were used, including nearest neighbor, decision tree, and pattern mining. Both decision tree and pattern mining approaches identified similar chemical and experimental conditions that were important to SOA formation. Among these important factors included the number of methyl groups, the number of rings and the presence of dinitrogen pentoxide (N2O5).

This dataset is associated with the following publication: Olson, D., J. Offenberg, M. Lewandowski, T. Kleindienst, K. Docherty, M. Jaoui, J.D. Krug, and T. Riedel. Data mining approaches to understanding the formation of secondary organic aerosol. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, USA, 252: 118345, (2021).

The original ICT skill test data including Finnish basic (grades 7-9/9, aged 12-16) and secondary education (aged 15-22) students' background knowledge and usage habit survey results and digital skills test results. Data has been collected in Finland within years 2014 and 2015 from 41 secondary (grades 7–9/9) and upper secondary level schools (study years 1–3/3). Altogether, 3,159 students were tested; 52% were male students, and 48% were female students. The age of the students ranged from 12 through 22 and their mean age was 15.9. Further, 40% (N = 1,261) came from the basic education (lower secondary level), and 60% (N = 1,898) came from secondary education (upper secondary level). Of those upper secondary level students who participated in this study, 54% came from general upper secondary schools, while 46% came from vocational upper secondary schools.

This dataset presents the results from a global survey designed to investigate how individuals involved in research discover and reuse secondary data. The data consist of 1677 complete responses received from individuals in 105 countries. The data are provided in two files: one for researchers and one for those working in research support. The README file provides extensive guidance on using the data files and the associated descriptions of the variables.

The data are maintained by the USGS (https://www.pwrc.usgs.gov/bbs/RawData/) and provide information on the trends and status of North American bird populations reported as population abundance indices. This research effort analyzed Louisiana breeding bird survey data (total species and total population) for 1990 and 2014. This dataset is not publicly accessible because: It is secondary data maintained by USGS. It can be accessed through the following means: https://www.pwrc.usgs.gov/bbs/RawData/. Format: Electronic text files. This dataset is associated with the following publication: Eason, T., W. Chuang, S. Sundstrom, and H. Cabezas. An information theory-based approach to assessing spatial patterns in complex systems. Entropy. MDPI AG, Basel, SWITZERLAND, 21(2): 182, (2019).

This document contains simulated sequences used to estimate divergence times with primary and secondary calibrations.