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

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

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

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

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

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

Abstract

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

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

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

Geographic coverage

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

Analysis unit

• Teachers
• Schools

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

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

Cleaning operations

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

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

Prior to statistical analysis of mass spectrometry (MS) data, quality control (QC) of the identified biomolecule peak intensities is imperative for reducing process-based sources of variation and extreme biological outliers. Without this step, statistical results can be biased. Additionally, liquid chromatography–MS proteomics data present inherent challenges due to large amounts of missing data that require special consideration during statistical analysis. While a number of R packages exist to address these challenges individually, there is no single R package that addresses all of them. We present pmartR, an open-source R package, for QC (filtering and normalization), exploratory data analysis (EDA), visualization, and statistical analysis robust to missing data. Example analysis using proteomics data from a mouse study comparing smoke exposure to control demonstrates the core functionality of the package and highlights the capabilities for handling missing data. In particular, using a combined quantitative and qualitative statistical test, 19 proteins whose statistical significance would have been missed by a quantitative test alone were identified. The pmartR package provides a single software tool for QC, EDA, and statistical comparisons of MS data that is robust to missing data and includes numerous visualization capabilities.

Abstract copyright UK Data Service and data collection copyright owner.

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

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

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

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

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

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

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

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

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

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

The Social Participation and Identity project combined quantitative longitudinal data with a qualitative investigation of a sub-sample of the NCDS cohort when they were aged 50, presented here as a mixed-methods data collection containing both qualitative and quantitative data. This was the first attempt to interview members of a national, longitudinal cohort study in depth, with the possibility of linking such biographical narratives to structured survey data collected throughout the life course. Interviews were conducted with a sub-sample of 220 NCDS cohort members resident in Great Britain (England, Scotland and Wales). The...

Historical quantitative benthos grab samples from the Southern Baltic Sea, collected by German researchers. The dataset was digitised at the Christian-Albrechts-University Kiel; Leibniz Institute of Marine Sciences; Marine Ecology Division; Benthos Ecology section.

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

This data package includes a quantitative analysis of non-coral communities at sites along a water quality gradient off of Aua, American Samoa in 2022. These datasets were funded by the NOAA Coral Reef Conservation Program (CRCP) Project Number 31303 to study effects of land-based sources of pollution (LBSP) in Aua, American Samoa. In September 2022 Ecosystem Sciences Division (ESD) scientists of the Pacific Islands Fisheries Science Center (PIFSC) flew into American Samoa to survey 18 sites along a water quality gradient off of Aua. Three datasets are provided of the taxa species and counts of benthic foraminifera, benthic microalgae, and macroinvertebrates from samples collected between 9-28 September 2022. Samples were preserved in the field, and brought back to the NOAA Inouye Regional Center (IRC) and analyzed via microscopy. To analyze benthic foraminifera abundance, sediment samples were collected using a small sediment corer (60 ml syringe with the tip removed and a stopper placed there instead). Only the top 3 cm were retained. Under the microscope, benthic foraminifera were picked out of the sediment and identified. To analyze benthic microalgae, microscope slides were deployed on the seafloor at each site for 2 to 3 weeks. The benthic microalgae that settled was fixed and preserved in Lugol's solution and analyzed. Microalgae included diatoms (pennate and centric), dinoflagellates, chlorophyta, and cyanobacteria. To analyze macroinvertebrates: plastic scouring pads were deployed and attached to the substratum with zip-ties for 2 to 3 weeks. The scouring pads were removed after the settlement period, all plastic zip-ties and additional waste were removed from the reef. Macroinvertebrates that settled on the scouring pad were placed into sampling jars and fixed and preserved with 4% formalin, and subsequently analyzed under the microscope. These quantitative non-coral community surveys were one of several surveys conducted at the same sites across Aua reef in September 2022. Other surveys described and archived separately include surveys of water quality, CTD casts, coral demography, benthic imagery/benthic cover, and coral demography. These can be accessed under the 'Related Items' section of the InPort metadata record.

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

Independent T-tests of key variables by exposure to healthcare barriers among the quantitative sample (N = 151).

Project Overview This study used a community-based participatory approach to identify and investigate the needs of people experiencing homelessness in Dublin, Ireland. The project had several stages: A systematic review on health disparities amongst people experiencing homelessness in the Republic of Ireland; Observation and interviews with homeless attendees of a community health clinic; and Interviews with community experts (CEs) conducted from September 2022 to March 2023 on ongoing work and gaps in the research/health service response. This data deposit stems from stage 3, the community expert interview aspect of this project. Stage 1 of the project has been published (Ingram et al., 2023.) and associated data are available here. De-identified field note data from stage 2 of the project are planned for sharing upon completion of analysis, in January 2024. Data and Data Collection Overview A purposive, criterion-i sampling strategy (Palinkas et al., 2015) – where selected interviewees meet a predetermined criterion of importance – was used to identify professionals working in homeless health and/or addiction services in Dublin, stratified by occupation type. Potential CEs were identified through an internet search of homeless health and addiction services in Dublin. Interviewed CEs were invited to recommend colleagues they felt would have relevant perspectives on community health needs, expanding the sample via snowball strategy. Interview questions were based on World Health Organization Community Health Needs Assessment guidelines (Rowe at al., 2001). Semi-structured interviews were conducted between September 2022 and March 2023 utilising ZOOM™, the phone, or in person according to participant preference. Carolyn Ingram, who has formal qualitative research training, served as the interviewer. CEs were presented with an information sheet and gave audio recorded, informed oral consent – considered appropriate for remote research conducted with non-vulnerable adult participants – in the full knowledge that interviews would be audio recorded, transcribed, and de-identified, as approved by the researchers’ institutional Human Research Ethics Committee (LS-E-125-Ingram-Perrotta-Exemption). Interviewees also gave permission for de-identified transcripts to be shared in a qualitative data archive. Shared Data Organization 16 de-identified transcripts from the CE interviews are being published. Three participants from the total sample (N=19) did not consent to data archival. The transcript from each interviewee is named based on the type of work the interviewee performs, with individuals in the same type of work being differentiated by numbers. The full set of professional categories is as follows: Addiction Services Government Homeless Health Services Hospital Psychotherapist Researcher Social Care Any changes or removal of words or phrases for de-identification purposes are flagged by including [brackets] and italics. The documentation files included in this data project are the consent form and the interview guide used for the study, this data narrative and an administrative README file. References Ingram C, Buggy C, Elabbasy D, Perrotta C. (2023) “Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map.” Journal of Public Health (Berl). https://doi.org/10.1007/s10389-023-01934-0 Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. (2015) “Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.” Administration and Policy in Mental Health. Sep;42(5):533–44. https://doi.org/10.1007/s10488-013-0528-y Rowe A, McClelland A, Billingham K, Carey L. (2001) “Community health needs assessment: an introductory guide for the family health nurse in Europe” [Internet]. World Health Organization. Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108440

BackgroundHuman Papilloma Virus (HPV) vaccination can prevent more than 90% of cancers caused by HPV. Although this vaccination is recommended and provided at no cost to all adolescent girls aged 9 to19 years in Uganda, its uptake remains low. We sought to determine the uptake of, and factors associated with HPV vaccination among adolescent girls living with HIV in Uganda.MethodsWe conducted an explanatory sequential mixed methods study, among adolescent girls living with HIV, attending HIV care at the Mulago ISS HIV clinic in Kampala, Uganda. We administered a structured questionnaire to elicit data on HPV vaccination and its covariates to a systematic random sample of 264 adolescent girls with HIV. A participant who had received all the three recommended HPV vaccine doses was classified as fully vaccinated. We then conducted four focus group discussions among adolescent girls living with HIV (n = 32), eight in-depth interviews among their parents and five Key informant interviews among their healthcare providers. We conducted descriptive statistics and logistic regression analyses for the quantitative data before thematic analysis for the qualitative data.ResultOf 264 adolescent girls, 31% (83/264) had at least one HPV vaccine dose; 22% (59/264) two doses, while 8.0% (21/264) were fully vaccinated (received three doses). While most participants received their first and second doses (48% (40/83)) and 57.6% (34/59), respectively) from school, the largest number of participants (47.1% (12/21)) received their third dose at community outreaches. Participants who received counseling from community members were three times more likely to get fully vaccinated compared to those who did not receive counseling (aOR 3.28, Cl:1.07–10.08, P = 0.038). From the qualitative follow-up, three major themes were identified: (1): Limited information about HPV vaccination, which gave room for misconceptions and doubts about the vaccine; (2) Parental influence on adolescent decisions was strong despite parents having limited knowledge about HPV vaccination and (3) Inadequacy of HPV vaccination services at the hospital and in the schools.ConclusionFull HPV vaccination was low among adolescent girls living with HIV. Counseling of the adolescents by community members, alongside HPV vaccination community outreaches, provided a platform for vaccination. There should be strategies to provide adequate information about HPV vaccine to health workers, parents, and the adolescents. In addition to schools, community-based initiatives, including outreaches and lay-health workers can be utilized to improve HPV vaccine uptake among girls with HIV.

This dataset contains sample output data for TELL. The sample dataset includes four years of sample future data (2039, 2059, 2079, and 2099) that comes from IM3's future WRF runs under the RCP 8.5 climate scenario with SSP5 population forcing. Note that the GCAM-USA output used in this simulation is sample data only. As such the quantitative results from this set of sample output should not be considered valid.

Large-scale quantitative analyses of biological systems are often performed with few replicate experiments, leading to multiple nonidentical data sets due to missing values. For example, mass spectrometry driven proteomics experiments are frequently performed with few biological or technical replicates due to sample-scarcity or due to duty-cycle or sensitivity constraints, or limited capacity of the available instrumentation, leading to incomplete results where detection of significant feature changes becomes a challenge. This problem is further exacerbated for the detection of significant changes on the peptide level, for example, in phospho-proteomics experiments. In order to assess the extent of this problem and the implications for large-scale proteome analysis, we investigated and optimized the performance of three statistical approaches by using simulated and experimental data sets with varying numbers of missing values. We applied three tools, including standard t test, moderated t test, also known as limma, and rank products for the detection of significantly changing features in simulated and experimental proteomics data sets with missing values. The rank product method was improved to work with data sets containing missing values. Extensive analysis of simulated and experimental data sets revealed that the performance of the statistical analysis tools depended on simple properties of the data sets. High-confidence results were obtained by using the limma and rank products methods for analyses of triplicate data sets that exhibited more than 1000 features and more than 50% missing values. The maximum number of differentially represented features was identified by using limma and rank products methods in a complementary manner. We therefore recommend combined usage of these methods as a novel and optimal way to detect significantly changing features in these data sets. This approach is suitable for large quantitative data sets from stable isotope labeling and mass spectrometry experiments and should be applicable to large data sets of any type. An R script that implements the improved rank products algorithm and the combined analysis is available.

This dataset contains information about the development of a paper analytical device for the detection of the microcystin toxin. Water samples were collected weekly from Rostherne Mere and Tatton Park Lake, Cheshire between 20/07/2022 and 12/10/2022. Samples were analysed for the presence of microcystin-producing genes and released microcystin. A paper analytical device incorporating a protein phosphatase inhibition assay was also used to monitor microcystin levels. Preliminary user evaluation of the paper analytical devices and associated mobile photo applications is also provided. The work was supported by the Natural Environment Research Council (Grant NE/X011607/1).

Abstract copyright UK Data Service and data collection copyright owner. This ESRC funded research study aimed to collect national level data on student attitudes toward quantitative methods which would provide reliable and valid description of student perceptions of and attitudes towards quantitative methods in sociology and political science. The survey consisted of an online self-completion questionnaire which was administered in English and Welsh to higher education institutions (HEIs) students in their second or third year of a three-year undergraduate degree. A random sample of 34 institutions offering major/single honours degrees in sociology or politics were selected. The sampling units were in both old and new universities and university colleges. Further information about the study can be found on the ESRC Award web page. Main Topics: The questionnaire focused on student attitudes toward studying quantitative methods, with additional questions on achieved grades and attitudes toward teaching and learning. The questionnaire also asked students about their A-level (or equivalent) choices and choice of sociology at degree level, their views of sociology’s status and whether they saw it as closer to the arts/humanities or science/mathematics. One-stage stratified or systematic random sample Self-completion Email survey

Quantitative evidence synthesis through meta-analysis is central to evidence-based medicine. For well-documented reasons, the meta-analysis of individual patient data (IPD) is held in higher regard than aggregate data. With access to IPD, the analysis is not restricted to a ‘two-stage’ approach (combining estimates and standard errors) but can estimate parameters of interest by fitting a single model to all of the data; a so-called ‘one-stage’ analysis. There has been debate about the merits of one- and two-stage analysis. Arguments for one-stage analysis have typically noted that a wider range of models can be fitted and overall estimates may be more precise. The two-stage side has emphasised that the models that can be fitted in two-stages are sufficient to answer the relevant questions, with less scope less scope for mistakes because there are fewer modelling choices to be made in the two-stage approach. Considering Gaussian data, we consider the statistical arguments for flexibility and precision in the small-sample settings. Regarding flexibility, several of the models that can be fitted only in one stage may not be of serious interest to most meta-analysis practitioners. Regarding precision, we consider fixed- and random-effects meta-analysis, and see that, for a model making certain assumptions, the number of stages used to fit this model is irrelevant; the precision will be approximately equal. Meta-analysts should choose modelling assumptions carefully. Sometimes relevant models can only be fitted in one stage. Otherwise, meta-analysts are free to use whichever procedure is most convenient to fit the identified model.

Algorithms for Quantitative Pedology (AQP) is a collection of code, ideas, documentation, and examples wrapped-up into several R packages. The theory behind much of the code can be found in Beaudette, D., Roudier, P., & O'Geen, A. (2013). Algorithms for quantitative pedology: A toolkit for soil scientists. Computers & Geosciences, 52, 258-268. doi: 10.1016/j.cageo.2012.10.020. The AQP package was designed to support data-driven approaches to common soils-related tasks such as visualization, aggregation, and classification of soil profile collections. To contribute code, documentation, bug reports, etc. contact Dylan at dylan [dot] beaudette [at] usda [dot] gov. AQP is a collaborative effort, funded in part by the Kearney Foundation of Soil Science (2009-2011) and USDA-NRCS (2011-current). The AQP suite of R packages are used to generate figures for SoilWeb, Series Extent Explorer, and Soil Data Explorer. Soil data presented were derived from the 100+ year efforts of the National Cooperative Soil Survey, c/o USDA-NRCS. Resources in this dataset:Resource Title: aqp: Algorithms for Quantitative Pedology (CRAN). File Name: Web Page, url: https://CRAN.R-project.org/package=aqp The Algorithms for Quantitative Pedology (AQP) project was started in 2009 to organize a loosely-related set of concepts and source code on the topic of soil profile visualization, aggregation, and classification into this package (aqp). Over the past 8 years, the project has grown into a suite of related R packages that enhance and simplify the quantitative analysis of soil profile data. Central to the AQP project is a new vocabulary of specialized functions and data structures that can accommodate the inherent complexity of soil profile information; freeing the scientist to focus on ideas rather than boilerplate data processing tasks . These functions and data structures have been extensively tested and documented, applied to projects involving hundreds of thousands of soil profiles, and deeply integrated into widely used tools such as SoilWeb https://casoilresource.lawr.ucdavis.edu/soilweb-apps/. Components of the AQP project (aqp, soilDB, sharpshootR, soilReports packages) serve an important role in routine data analysis within the USDA-NRCS Soil Science Division. The AQP suite of R packages offer a convenient platform for bridging the gap between pedometric theory and practice.