This dataset contains aggregate data concerning the number of child placement episodes that ended with a legal discharge from DCF care, and for whom the end of a 12 month observation period (starting with their legal discharge date) terminated during the SFY. These figures are broken out by the DCF Region and Office responsible for the child's care, by their Race/Ethnicity, and by whether another placement episode for that child began within 12 months of their discharge from care or not. It would be appropriate to roll up the data from all variables across multiple time periods, as they represent specific events in the lives of these children. These data form the basis of measurement for the Juan F. Consent Decree Exit Plan Outcome #11: Permanency Maintained (No Re-Entry), although those figures are reported to the DCF Court Monitor on a quarterly rather than annual schedule.

This dataset contains aggregate data concerning the number of children with substantiated abuse/neglect reports, and for whom the end of a 6 month observation period (starting with either the date the substantiated report had been accepted, or the specific incident date if one was provided) terminated during the SFY. These figures are broken out by the DCF Region and Office responsible for the child's care, by their Age Group, and by whether another report of substantiated abuse/neglect occurred within 12 months of the first substantiation or not. It would be appropriate to roll up the data from all variables across multiple time periods, as they represent specific events in the lives of these children. These data form the basis of measurement for the Juan F. Consent Decree Exit Plan Outcome #7: Safety Maintained (No Repeat Maltreatment), although those figures are reported to the DCF Court Monitor on a quarterly rather than annual schedule.

The number of charges and number of children and young people (aged 10 - 16 years) with charges finalised in any court (including the Youth, District and High Court), by offence type (ANZSOC division), charge outcome, court, most serious order, age, gender and ethnicity.

Project name:

Perception of vowel features (formant structure, pitch) in children with autism spectrum disorders and typically developing children (MEG/ERF study).

Related publications:

• Peer-reviewed publication: Fadeev, K. A., Romero Reyes, I. V., Goiaeva, D. E., Obukhova, T. S., Ovsiannikova, T. M., Prokofyev, A. O., Rytikova, A. M., Novikov, A. Y., Kozunov, V. V., Stroganova, T. A., & Orekhova, E. V. (2024). Attenuated processing of vowels in the left temporal cortex predicts speech-in-noise perception deficit in children with autism. Journal of Neurodevelopmental Disorders, 16(1), 1–24. https://doi.org/10.1186/s11689-024-09585-2
• Preprint in bioRxiv: Fadeev, K. A., Romero Reyes, I. V., Goiaeva, D. E., Obukhova, T. S., Ovsiannikova, T. M., Prokofyev, A. O., Rytikova, A. M., Novikov, A. Y., Kozunov, V. V., Stroganova, T. A., & Orekhova, E. V. (2024). Attenuated Processing of Vowels in the Left Hemisphere Predicts Speech-in-Noise Perception Deficit in Children with Autism. https://doi.org/10.1101/2024.06.24.600191

Year(s) that the project ran:

2017-2024.

Brief overview:

This dataset was obtained by the team at the Center for Neurocognitive Research (MEG Center) of Moscow State University of Psychology and Education as part of a study on vowel perception and their properties in children (Fadeev et al., 2024, preprint). It includes MEG recordings from 35 children with autism spectrum disorders and 39 typically developing children.

Experimental procedure:

The participants were instructed to watch a silent video (movie/cartoon) of their choice and to ignore the auditory stimuli. The stimuli were delivered binaurally via plastic ear tubes inserted into the ear canals. The tubes were fixed to the MEG helmet to minimize possible noise from contact with the subject’s clothing. The intensity was set at a sound pressure level of 90 dB SPL. The experiment consisted of three blocks of 360 trials, each block lasting around 9 minutes with short breaks between blocks.

Stimuli:

The experimental paradigm used in this study is identical to that described in Orekhova et al. (2023). We used four types of synthetic vowel-like stimuli previously employed by Uppenkamp et al. (2006, 2011) and downloaded from http://medi.uni-oldenburg.de/members/stefan/phonology_1/. You can also find a copy of the sound files in the stimuli directory on this dataset page. Five strong vowels were used: - /a/ (caw, dawn), - /e/ (ate, bait), - /i/ (beat, peel), - /o/ (coat, wrote) and - /u/ (boot, pool).

A total of 270 stimuli from each of the four classes were presented, with three stimulus variants equally represented within each class (N = 90). All stimuli were presented in random order. Each stimulus lasted 812 ms, including rise/fall times of 10 ms each. The interstimulus intervals (ISI) were randomly chosen from a range of 500 to 800 ms.

Short names of stimuli (used in code, filenames, and directory names): - dv - periodic vowels - rv - non-periodic vowels - mp - periodic non-vowels - mr - non-periodic non-vowels

Additional data acquired:

The following tests were also administered to the study participants: - Pure tone air conduction audiometry; - Russian Child Language Assessment Battery (Lopukhina et al., 2019); - Words-in-Noise (WiN) test (Fadeev et al., 2023); - Social Responsiveness Scale for children (Constantino, 2013); - Social Communication Questionnaire (SCQ-Lifetime) (Berument et al., 1999), - KABC-II, Mental Processing Index (MPI) as an IQ equivalent (Kaufman & Kaufman, 2004).

Dataset content:

MEG data

sub-LABEL/meg/...meg.fif -- 3 runs (in some cases, the number of runs may differ due to the subjects' features). MEG data were recorded using Elekta VectorView Neuromag 306-channel MEG detector array (Helsinki, Finland) with 0.1 - 330 Hz inbuilt filters and 1000 Hz sample frequency.

MRI data

sub-LABEL/anat/ -- T1-weighted images MRI for subject.

Freesurfer

derivatives/freesurfer/ - outputs of running the FreeSurfer pipeline recon-all on the MRI data with no additional command line options (only defaults were used): $ recon-all -i sub-Z201_T1w.nii.gz -s Z201 -all After the recon-all call, there were further FreeSurfer calls from the MNE API: $ mne make_scalp_surfaces -s Z201 --force $ mne watershed_bem -s Z201

Code

The code of the project was developed and tested for Ubuntu OS (20-22 x64 version) and has the following structure (directory names provide explanations of their contents):

code
├── analysis
│     ├── 0-preprocessing_for_clustering
│     │     ├── ...
│     ├── 1-tfce_clustering
│     │     ├── ...
│     ├── 2-clustering_results_analytics
│     │     ├── ...
│     └── modules
│       ├── clustering.py
│       └── data_ops.py
├── envs
│     ├── envs_for_between_groups_clustering_in_auditory_cortex_with_morphological_sign_flip.json
│     ├── envs_for_interaction_clustering_in_auditory_cortex_with_morphological_sign_flip.json
│     └── envs_for_within_groups_clustering_in_auditory_cortex_with_morphological_sign_flip.json
├── preprocessing
│     ├── 00-maxwell_filtering.py
│     ├── ...
│     └── 10-make_stc.py
├── README
├── requirements_for_ubuntu_2x.txt
└── requirements_for_windows_1x.txt

Please read code/README.md file for more detail instructions.

Requirements for Code Usage (MNE-Python & Additional Python Libraries)"

1. For installation, we recommend the Anaconda distribution. Find the installation guide here: Anaconda Installation Guide.

2. After you have a working version of Python 3, simply install a new virtual environment via this command in the Ubuntu terminal or Anaconda Prompt for Windows OS:

conda create --name=mne1.4 --channel=conda-forge python=3.10 mne=1.4.1 numpy=1.23.1 spyder pyvista pyvistaqt pingouin scipy mne-bids pandas openpyxl autoreject

Or use the requirements_xxxxxx.txt files located in code directory: - For Ubuntu OS (version 20 and above), please use requirements_for_ubuntu_2x.txt. - For Windows OS (version 10 and above), please use requirements_for_windows_1x.txt.

To do this, you can run the following command in the Ubuntu terminal or Anaconda Prompt for Windows OS:

$ conda create --name mne1.4 --file requirements_filename

1. Activate the created environment: $ conda activate mne1.4

2. Start your favorite IDE with this environment. For example, to start Spyder IDE, use this command after activating the environment: $ spyder

3. To start processing data, go to the directory with the downloaded data and open the Python scripts of interest. (If you are using Spyder IDE, use the "Files" pane located in the upper right corner of the workspace.)

Clustering results directories

All output files from the scripts of the code directory are saved in the derivatives directory.

Clustering Versions mnemonics (phrases used in python scripts names and directory names): - v13: TFCE clustering based on groups ⨯ conditions difference response - v14: TFCE clustering based on between-conditions difference response - v15: TFCE clustering based on between groups evoked response

The derivatives of project has the following structure (directory names provide explanations of their contents):

derivatives/preprocessing/
├── fsaverage_labels_of_analytics
│     ├── auditory_cortex_region-lh.label
│     └── auditory_cortex_region-rh.label
└── fsaverage_stcs_after_morph_flip_in_labels_of_analytics
  ├── subjects_info_for_morphological_sign_flipped_data_1000Hz
  └── subjects_stc_for_morphological_sign_flipped_data_1000Hz

derivatives/analysis/
├── 20240607_74subj_v13_500Hz_5000_perm_DV-MR_TD_vs_ASD_0-800_msec_tfce_interaction_in_auditory_cortex_morph_flip_with_5e-02_clusters_p_thresh
├── 20240608_74subj_v13_500Hz_5000_perm_MP-MR_TD_vs_ASD_0-800_msec_tfce_interaction_in_auditory_cortex_morph_flip_with_5e-02_clusters_p_thresh
├── 20240608_74subj_v13_500Hz_5000_perm_RV-MR_TD_vs_ASD_0-800_msec_tfce_interaction_in_auditory_cortex_morph_flip_with_5e-02_clusters_p_thresh
├── 20240608_74subj_v14_500Hz_5000perm_ASD_DV-MR_0-800_msec_tfce_1samp_within_groups_in_auditory_cortex_morph_flip_5e-02_clusters_p_thresh
├── 20240608_74subj_v14_500Hz_5000perm_ASD_MP-MR_0-800_msec_tfce_1samp_within_groups_in_auditory_cortex_morph_flip_5e-02_clusters_p_thresh
├── 20240608_74subj_v14_500Hz_5000perm_ASD_RV-MR_0-800_msec_tfce_1samp_within_groups_in_auditory_cortex_morph_flip_5e-02_clusters_p_thresh
├── 20240608_74subj_v14_500Hz_5000perm_TD_DV-MR_0-800_msec_tfce_1samp_within_groups_in_auditory_cortex_morph_flip_5e-02_clusters_p_thresh
├── 20240608_74subj_v14_500Hz_5000perm_TD_MP-MR_0-800_msec_tfce_1samp_within_groups_in_auditory_cortex_morph_flip_5e-02_clusters_p_thresh
├── 20240608_74subj_v14_500Hz_5000perm_TD_RV-MR_0-800_msec_tfce_1samp_within_groups_in_auditory_cortex_morph_flip_5e-02_clusters_p_thresh
├── 20240608_74subj_v15_500Hz_5000_perm_DV_TD_vs_ASD_in_0-800_msec_tfce_between_groups_in_auditory_cortex_morph_flip_with_5e-02_cluster_p_threshold
├── 20240608_74subj_v15_500Hz_5000_perm_MP_TD_vs_ASD_in_0-800_msec_tfce_between_groups_in_auditory_cortex_morph_flip_with_5e-02_cluster_p_threshold
└── 20240608_74subj_v15_500Hz_5000_perm_RV_TD_vs_ASD_in_0-800_msec_tfce_between_groups_in_auditory_cortex_morph_flip_with_5e-02_cluster_p_threshold

Data user

The WIC Infant and Toddler Feeding Practices Study–2 (WIC ITFPS-2) (also known as the “Feeding My Baby Study”) is a national, longitudinal study that captures data on caregivers and their children who participated in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) around the time of the child’s birth. The study addresses a series of research questions regarding feeding practices, the effect of WIC services on those practices, and the health and nutrition outcomes of children on WIC. Additionally, the study assesses changes in behaviors and trends that may have occurred over the past 20 years by comparing findings to the WIC Infant Feeding Practices Study–1 (WIC IFPS-1), the last major study of the diets of infants on WIC. This longitudinal cohort study has generated a series of reports. These datasets include data from caregivers and their children during the prenatal period and during the children’s first five years of life (child ages 1 to 60 months). A full description of the study design and data collection methods can be found in Chapter 1 of the Second Year Report (https://www.fns.usda.gov/wic/wic-infant-and-toddler-feeding-practices-st...). A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Processing methods and equipment used Data in this dataset were primarily collected via telephone interview with caregivers. Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. The study team cleaned the raw data to ensure the data were as correct, complete, and consistent as possible. Study date(s) and duration Data collection occurred between 2013 and 2019. Study spatial scale (size of replicates and spatial scale of study area) Respondents were primarily the caregivers of children who received WIC services around the time of the child’s birth. Data were collected from 80 WIC sites across 27 State agencies. Level of true replication Unknown Sampling precision (within-replicate sampling or pseudoreplication) This dataset includes sampling weights that can be applied to produce national estimates. A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Level of subsampling (number and repeat or within-replicate sampling) A full description of the sampling and weighting procedures can be found in Appendix B-1 of the Fourth Year Report (https://fns-prod.azureedge.net/sites/default/files/resource-files/WIC-IT...). Study design (before–after, control–impacts, time series, before–after-control–impacts) Longitudinal cohort study. Description of any data manipulation, modeling, or statistical analysis undertaken Each entry in the dataset contains caregiver-level responses to telephone interviews. Also available in the dataset are children’s length/height and weight data, which were objectively collected while at the WIC clinic or during visits with healthcare providers. In addition, the file contains derived variables used for analytic purposes. The file also includes weights created to produce national estimates. The dataset does not include any personally-identifiable information for the study children and/or for individuals who completed the telephone interviews. Description of any gaps in the data or other limiting factors Please refer to the series of annual WIC ITFPS-2 reports (https://www.fns.usda.gov/wic/infant-and-toddler-feeding-practices-study-2-fourth-year-report) for detailed explanations of the study’s limitations. Outcome measurement methods and equipment used The majority of outcomes were measured via telephone interviews with children’s caregivers. Dietary intake was assessed using the USDA Automated Multiple Pass Method (https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-h...). Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. Resources in this dataset:Resource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data CSV. File Name: itfps2_enrollto60m_publicuse.csvResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data Codebook. File Name: ITFPS2_EnrollTo60m_PUF_Codebook.pdfResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Enroll to 60 Months Public Use Data SAS SPSS STATA R Data. File Name: ITFP@_Year5_Enroll60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Enroll to 60 Months Public Use Data SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data CSV. File Name: ampm_1to60_ana_publicuse.csvResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Tot to 60 Months Public Use Data Codebook. File Name: AMPM_1to60_Tot Codebook.pdfResource Description: ITFP2 Year 5 Tot to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data Codebook. File Name: AMPM_1to60_Ana Codebook.pdfResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Ana to 60 Months Public Use Data SAS SPSS STATA R Data. File Name: ITFP@_Year5_Ana_60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Ana to 60 Months Public Use Data SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Tot to 60 Months Public Use Data CSV. File Name: ampm_1to60_tot_publicuse.csvResource Description: ITFP2 Year 5 Tot to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Tot to 60 Months Public Use SAS SPSS STATA R Data. File Name: ITFP@_Year5_Tot_60_SAS_SPSS_STATA_R.zipResource Description: ITFP2 Year 5 Tot to 60 Months Public Use SAS SPSS STATA R DataResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use Data CSV. File Name: ampm_foodgroup_1to60m_publicuse.csvResource Description: ITFP2 Year 5 Food Group to 60 Months Public Use Data CSVResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use Data Codebook. File Name: AMPM_FoodGroup_1to60m_Codebook.pdfResource Description: ITFP2 Year 5 Food Group to 60 Months Public Use Data CodebookResource Title: ITFP2 Year 5 Food Group to 60 Months Public Use SAS SPSS STATA R Data. File Name: ITFP@_Year5_Foodgroup_60_SAS_SPSS_STATA_R.zipResource Title: WIC Infant and Toddler Feeding Practices Study-2 Data File Training Manual. File Name: WIC_ITFPS-2_DataFileTrainingManual.pdf

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

SAP2022T4T3NUTS - Number of Families with Children. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Number of Families with Children...

Interpretation of Electrocardiograms (ECG) is increasingly complemented by algorithms. These algorithms are based on large datasets. This ECG database consists of children and adults with congenital heart defects (CHD) including many arrhythmia annotations. This dataset, notable for its manual annotations and inclusion of intracardiac electrograms alongside traditional 12-lead ECGs, offers 1075.85 minutes of recordings (in total 113924 annotated beats) that capture various cardiac rhythms and arrhythmias such as supraventricular tachycardia and ventricular tachycardia. The data were meticulously collected from patients undergoing electrophysiological studies, with subsequent annotations by expert reviewers using the LightWAVE® software. The significance of this database lies in its focus on pediatric arrhythmias and arrhythmias of patients with congenital heart defect areas currently underrepresented in existing datasets, which predominantly feature adult pathologies. This resource aims to enhance algorithmic development for ECG interpretation, leveraging machine learning to improve diagnosis and treatment outcomes in these sensitive groups. The dataset not only serves as a critical tool for developing precision medicine but also sets a precedent for future expansions to include a broader spectrum of congenital heart defects conditions, thereby supporting the evolution of cardiac care through advanced computational techniques.

Analysis of ‘Discharged Children With Permanency Maintained 12 Months: Annual Trend By Gender’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/485e7b37-5134-4347-8e6e-2706f721a00e on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains aggregate data concerning the number of child placement episodes that ended with a legal discharge from DCF care, and for whom the end of a 12 month observation period (starting with their legal discharge date) terminated during the SFY. These figures are broken out by the DCF Region and Office responsible for the child's care, by their Gender, and by whether another placement episode for that child began within 12 months of their discharge from care or not. It would be appropriate to roll up the data from all variables across multiple time periods, as they represent specific events in the lives of these children. These data form the basis of measurement for the Juan F. Consent Decree Exit Plan Outcome #11: Permanency Maintained (No Re-Entry), although those figures are reported to the DCF Court Monitor on a quarterly rather than annual schedule.

--- Original source retains full ownership of the source dataset ---

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). This indicator is based on caregiver report. A child is considered to have weekly fast food consumption if they eat any food, including meals and snacks, from a fast food restaurant, such as McDonald’s, Taco Bell, KFC, or another similar type of place at least 1 time per week.Fast food consumption is associated with increased intake of calories, fat, and sodium, as well as with poor diet quality in children and adolescents. Poor diet has contributed to our current obesity epidemic and is a major risk factor for heart disease, diabetes, cancer, and many other chronic health conditions.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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 Parent Migration research project had four aims:

• an investigation of how children fare in educational attainments and behavioural adjustment, following change in family circumstances. This was done for children of the NCDS, making innovative use of multivariate multilevel modelling
• to see which children were likely to be resilient and which vulnerable to which type of change in their social environment
• to provide a systematic statistical backdrop on the population aged 5-16 in 1991, with the child as the unit of observation, taking evidence from the ONS Longitudinal Study (not held at the UKDA). This should show how far the sample of second generation NCDS children were representative of all children in experiencing change in who they live with and where, relating such histories to indicators of their living standards
• an enhancement of the NCDS5 dataset (see SN 5567) by coding and organizing data on migration, making a resource for other researchers. The methods used in the project were entirely quantitative, combining both description and analysis of secondary sources, the ONS Longitudinal Study and the second generation sample of NCDS cohort members and their children aged 5-18

This dataset contains information on the 'address history' of a sub-sample (2,657 members) of the NCDS who provided a mother/child questionnaire in 1991. One third of NCDS members identified to have at least one child old enough to complete maths and reading tests were requested to take part in the survey. Initial work was carried out by the NCDS User Support team as part of the 'Changing Home' project to code postal town data from NCDS5.

For the second edition (August 2008), the serial number has been replaced with a new one, variable Ncdsid. This change has been made for all datasets in the NCDS series. Further information may be found in the ‘CLS Confidentiality and Data Security Review’, included in the documentation.

Abstract copyright UK Data Service and data collection copyright owner.The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables. The Active Lives Children and Young People Survey, 2019-2020 began as the usual school-based survey (i.e. completed at school as part of lessons). From 20 March 2020, schools, colleges and nurseries were closed in the UK due to the COVID-19 pandemic and remained closed until 1 June 2020, when there was a phased reopening for reception, and Years 1 and 6. The Active Lives survey fieldwork in Spring term finished two weeks early before the end of term, in line with the school closures. Due to the closure of schools, the survey had to be adapted for at home completion. The adaptions involved minor questionnaire changes (e.g. to ensure the wording was appropriate for both the new lockdown situation and to account for the new survey completion method at home) and communication changes. For further details on the changes, please see the accompanying technical report. The circumstances and adaptations resulted in a delay to survey fieldwork re-starting. This means that the data does not cover the full lockdown period, and instead re-starts from mid-May 2020 (when the survey was relaunched). Sample targets were also reduced as a result of the pandemic, resulting in a smaller proportion of summer term responses for 2019-20 when compared to previous years. As part of Sport England’s official publication, an additional Coronavirus report was produced, which outlines changes during the ‘easing restrictions’ phase of lockdown from mid-May to the end of July, comparing the summer term in 2020 with summer 2019. Due to the reduced summer term sample, it is recommended to analyse within term and/or school phase for academic year 2019-20. The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year. The following datasets have been provided: Main dataset: includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).Year 1-2 dataset: includes responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).Teacher dataset – this .sav file includes response from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable. For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets. More general information about the study can be found on the Sport England Active Lives Survey webpages.Latest edition informationFor the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these data.

Families of tax filers; Census families with children by age of children and children by age groups (final T1 Family File; T1FF).

About this dataset

Context

The dataset tabulates the Clifton population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Clifton. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 55,513 (62.20% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Clifton population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Clifton is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Clifton is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Clifton Population by Age. You can refer the same here

This data represents unaccompanied children who are taken into custody by Customs and Border Protection brought to a facility and processed for transfer to the Department of Health and Human Services (HHS) as required by law. HHS holds the child for testing and quarantine, and shelters the child until the child is placed with a sponsor here in the United States.

Introduction: Screentime is ubiquitous with children and parents concerned and anxious about its effect on the well-being of their children. This project uses the 2020 data from the National Survey of Children’s Health (NSCH) to determine if there is a correlation between the amount of weekday screentime in children ages 17 and younger and reported instances of mental health treatment and mental health treatment needed. Objectives: The primary objective of this project is to determine if there is a correlation between screentime and the mental health of children, ages 17 and younger. Methods: This project utilizes 2020 data from the NSCH, specifically the survey information collected about children ages 17 and younger on screentime, mental health professional treatment, and age of the child. Screentime refers to weekday time spent in front of a TV, computer, cellphone, or other electronic device watching programs, playing games, accessing the internet or using social media. After analyzing the three aforementioned variables, the percentage of mental health treatment occurrences by age group per screen time category indicates whether there is a correlation between children’s screentime and their mental health. Results: Preschool-aged (0-5 years old) children who spent 2 hours per weekday in front of a screen had the highest occurrence of mental health treatment, doubling the other categories of screentime. In school-aged (6-13 years old) children, there is a rise in mental health treatment needed as screentime increases. In adolescent (14-17 years old) children, there is a significant increase in the occurrence of mental health treatment as screentime increases, where 60% of adolescents who require mental health treatment spent four or more hours in front of a screen. Conclusions: There is a correlation between increased screentime and the occurrence of mental health treatment in children, particularly with the Adolescent (14-17 years old) age group.

About this dataset

Context

The dataset tabulates the Twin Lakes population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Twin Lakes. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 4,002 (63.01% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Twin Lakes population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Twin Lakes is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Twin Lakes is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Twin Lakes Population by Age. You can refer the same here

This dataset contains aggregate data concerning the number of children with substantiated abuse/neglect reports, and for whom the end of a 6 month observation period (starting with either the date the substantiated report had been accepted, or the specific incident date if one was provided) terminated during the SFY. These figures are broken out by the DCF Region and Office responsible for the child's care, by their Race/Ethnicity, and by whether another report of substantiated abuse/neglect occurred within 12 months of the first substantiation or not. It would be appropriate to roll up the data from all variables across multiple time periods, as they represent specific events in the lives of these children. These data form the basis of measurement for the Juan F. Consent Decree Exit Plan Outcome #7: Safety Maintained (No Repeat Maltreatment), although those figures are reported to the DCF Court Monitor on a quarterly rather than annual schedule.

About this dataset

Context

The dataset tabulates the Thousand Oaks population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Thousand Oaks. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 74,828 (59.54% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Thousand Oaks population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Thousand Oaks is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Thousand Oaks is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Thousand Oaks Population by Age. You can refer the same here

Dataset population: Lone-parent households with dependent children where the lone parent is aged 16 to 74

Lone-parent households with dependent children where the lone parent is aged 16 to 74

In most tables, the term 'lone-parent household' is used to describe a household that comprises a lone parent family and no other person. In the alternative household type variable, a lone-parent household is defined as a household that contains at least one lone-parent family but does not contain any married, same-sex civil partnership or cohabiting couples.

A count of the dependent children living in a household. A dependent child is a person aged 0 to 15 in a household (whether or not in a family) or aged 16 to 18 in full-time education and living in a family with his or her parent(s) or grandparent(s). It does not include any children who have a spouse, partner or child living in the household.

Sex

The classification of a person as either male or female.