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.

Connecticut's Birth to Three System (B23) supports families with infants and toddlers that have developmental delays to learn new ways to make everyday activities enhance the child's development. Birth to Three is administered pursuant to Part C of the Individuals with Disabilities Education Act (IDEA). Once families with children below age 3 are referred, the child's development is evaluated for eligibility, and if eligible the family can receive supports until the child no longer has delays or until the child turns age 3. Because an infant can be referred within days of being born, a family may be enrolled for almost three full years. Annual data is a one-year snapshot, many towns will have more children served than were referred because they were referred in a previous calendar or fiscal year. Connecticut's Birth to Three System publishes data annually by the fiscal and calendar year and longitudinally by birth cohort. CTData.org carries both sets of data, here and in 'Birth To Three Cohort Data'.

This is an official NYC Health Department dataset. the Health Department does not endorse datasets using this data that may be created by others.

This dataset lists active child care programs regulated under NYC Health Code Article 47 (Group Day Care) and Article 43 (School-based Child Care).

Health Code Article 47: https://www.nyc.gov/assets/doh/downloads/pdf/about/healthcode/health-code-article47.pdf

Health Code Article 43: https://www.nyc.gov/assets/doh/downloads/pdf/about/healthcode/health-code-article43.pdf

These data can be used to map NYC Health Code regulated child care programs and filter by attributes, such as location, ages served, facility type. It can also be used to answer questions such as how many group child care (GCC) programs are in a specific ZIP code.

Only active group child care (GCC) and school-based child care (SBCC) programs are displayed in this dataset. Child care programs with a 'preliminary', 'suspended', or 'closed' status are not included.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Children with Elevated Blood Lead Levels - "Lead is a toxic metal that has no safe level. Children are especially sensitive to lead exposure. The legal definition of an elevated blood lead level in Maryland is 10 micrograms/deciliter (mcg/dL), but the current CDC and Maryland guidelines for health care providers urge follow up for any child with a level of 5 mcg/dL or higher. Children most often are exposed to lead if they swallow dust containing lead paint, usually when there is peeling, flaking, or chipping lead paint or from home renovation. Maryland health care providers are now supposed to test all children born on or after January 1, 2015 at their 12 and 24 month well child visits. Link to Data Details "

ANC

The deposit contains a dataset created for the paper, 'Many Models in R: A Tutorial'. ncds.Rds is an R format synthetic dataset created with the synthpop dataset in R using data from the National Child Development Study (NCDS), a birth cohort of individuals born in a single week of March 1958 in Britain. The dataset contains data on fourteen biomarkers collected at the age 46/47 sweep of the survey, four measures of cognitive ability from age 11 and 16, and three covariates, sex, body mass index at age 11 and father's social class. The data is only intended to be used in the tutorial - it is not to be used for drawing statistical inferences.

Data on obesity among children and adolescents aged 2-19 years by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time.

SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

No. of Children Referred and who Received a Service – Subject to a Child in Care Plan 2024. Published by Tusla. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Performance metrics for by year No. of Children Referred and who Received a Service – Subject to a Child in Care Plan...

OEC Funded Child Day Care Contract spaces for Infants, Toddlers, Preschool and School Age children by town and space type. 2015-16 Program Year.

Project name:

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

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, in press). 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 project 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 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 rpy2 mne-bids numpy 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 agreement:

Dataset is distributed under the CC BY license.

Acknowledgements:

We sincerely thank all of volunteers who participated in this study.

Funding:

The study was funded within the framework of the state assignment of the Ministry of Education of the Russian Federation (N 073-00037-24-01).

References:

1. Gutschalk, A., & Uppenkamp, S. (2011). Sustained responses for pitch and vowels map to similar sites in human auditory cortex. Neuroimage, 56(3), 1578-1587. doi:10.1016/j.neuroimage.2011.02.026

2. Orekhova, E. V., Fadeev, K. A., Goiaeva, D. E., Obukhova, T. S., Ovsiannikova, T. M., Prokofyev, A. O., & Stroganova, T. A. (2023). Different Hemispheric Lateralization for Periodicity and Formant Structure of Vowels in the Auditory Cortex and Its Changes between Childhood and Adulthood. Cortex. doi:10.1016/j.cortex.2023.10.020

3. Uppenkamp, S., Johnsrude, I. S., Norris, D., Marslen-Wilson, W., & Patterson, R. D. (2006). Locating the initial stages of speech-sound processing in human temporal cortex. Neuroimage, 31(3), 1284-1296. doi:10.1016/j.neuroimage.2006.01.004

4. Fadeev, K. A., Goyaeva, D. E., Obukhova, T. S.,

This is a monthly report on publicly funded community services for children, young people and adults using data from the Community Services Data Set (CSDS) reported in England for December 2017. The CSDS is a patient-level dataset providing information relating to publicly funded community services for children, young people and adults. These services can include health centres, schools, mental health trusts, and health visiting services. The data collected includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. It has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. Prior to October 2017, the predecessor Children and Young People's Health Services (CYPHS) Data Set collected data for children and young people aged 0-18. The CSDS superseded the CYPHS data set to allow adult community data to be submitted, expanding the scope of the existing data set by removing the 0-18 age restriction. The structure and content of the CSDS remains the same as the previous CYPHS data set. Further information about the CYPHS and related statistical reports is available from https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/children-and-young-people-s-health-services-data-set References to children and young people covers records submitted for 0-18 year olds and references to adults covers records submitted for those aged over 18. Where analysis for both groups have been combined, this is referred to as all patients. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. This month's statistical release also includes a separate quarterly analysis focusing on 6-8 week breastfeeding status and 24, 27 and 30 month Ages and Stages (ASQ-3) scoring, October - December 2017. This file has been revised and is available as part of the March 2018 publication. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use this form to provide us with any feedback or suggestions for improving the report.

Several rebel groups actively recruit children to serve among their ranks. While this constitutes one of the most egregious violations of children’s rights, it remains unclear what impact recruited children have on the fighting capacities of these armed groups. The existing research suggests that, on the one hand, armed groups drafting children might also be militarily effective, since it is cheaper to provide for children, they are more obedient and aggressive than adults, and easily manipulable. On the other hand, children may negatively affect rebel groups’ fighting capacities as they are less proficient combatants than adults and often difficult to control. We add to this debate by systematically analyzing the quantitative evidence on the impact of child soldiers on rebel groups’ fighting capacities. Based on the analysis of newly compiled data on child recruitment by rebel groups between 1989 and 2010, our analyses show that children may actually increase rebel groups’ fighting capacities. That said, rebels’ ability to procure arms and the access to resources seem to be more important determinants of fighting capacity. The authors discuss these findings in light of policy implications and avenues for future research.

Contains data on Community Services Statistics for May 2023 and a provisional data file for June 2023 (note this is intended as an early view until providers submit a refresh of their data).

[2023-05-04 - Added WIC Infant and Toddler Feeding Practices Study-2 Data File Training Manual] 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 four years of life (child ages 1 to 48 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-study-2-second-year-report). 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-ITFPS2-Year4Report-Appendix.pdf).
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 2018. 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. 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-ITFPS2-Year4Report-Appendix.pdf).
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-ITFPS2-Year4Report-Appendix.pdf).
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 Second Year Report (https://www.fns.usda.gov/wic/wic-infant-and-toddler-feeding-practices-study-2-second-year-report) for a detailed explanation 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-human-nutrition-research-center/food-surveys-research-group/docs/ampm-usda-automated-multiple-pass-method/). Children’s length/height and weight data were objectively collected while at the WIC clinic or during visits with healthcare providers. See file list for descriptions of each data file.

About this dataset

Context

The dataset tabulates the Ohio 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 Ohio. 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 7.11 million (60.41% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Ohio population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Ohio is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Ohio 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 Ohio Population by Age. You can refer the same here

The Child Care and Development Fund (CCDF) provides federal money to states and territories to provide assistance to low-income families, to obtain quality child care so they can work, attend training, or receive education. Within the broad federal parameters, states and territories set the detailed policies. Those details determine whether a particular family will or will not be eligible for subsidies, how much the family will have to pay for the care, how families apply for and retain subsidies, the maximum amounts that child care providers will be reimbursed, and the administrative procedures that providers must follow. Thus, while CCDF is a single program from the perspective of federal law, it is in practice a different program in every state and territory. The CCDF Policies Database project is a comprehensive, up-to-date database of CCDF policy information that supports the needs of a variety of audiences through (1) analytic data files, (2) a project website and search tool, and (3) an annual report (Book of Tables). These resources are made available to researchers, administrators, and policymakers with the goal of addressing important questions concerning the effects of child care subsidy policies and practices on the children and families served. A description of the data files, project website and search tool, and Book of Tables is provided below: 1. Detailed, longitudinal analytic data files provide CCDF policy information for all 50 States, the District of Columbia, and the United States Territories and outlying areas that capture the policies actually in effect at a point in time, rather than proposals or legislation. They capture changes throughout each year, allowing users to access the policies in place at any point in time between October 2009 and the most recent data release. The data are organized into 32 categories with each category of variables separated into its own dataset. The categories span five general areas of policy including: Eligibility Requirements for Families and Children (Datasets 1-5) Family Application, Terms of Authorization, and Redetermination (Datasets 6-13) Family Payments (Datasets 14-18) Policies for Providers, Including Maximum Reimbursement Rates (Datasets 19-27) Overall Administrative and Quality Information Plans (Datasets 28-32) The information in the data files is based primarily on the documents that caseworkers use as they work with families and providers (often termed "caseworker manuals"). The caseworker manuals generally provide much more detailed information on eligibility, family payments, and provider-related policies than the CCDF Plans submitted by states and territories to the federal government. The caseworker manuals also provide ongoing detail for periods in between CCDF Plan dates. Each dataset contains a series of variables designed to capture the intricacies of the rules covered in the category. The variables include a mix of categorical, numeric, and text variables. Most variables have a corresponding notes field to capture additional details related to that particular variable. In addition, each category has an additional notes field to capture any information regarding the rules that is not already outlined in the category's variables. 2. The project website and search tool provide access to a point-and-click user interface. Users can select from the full set of public data to create custom tables. The website also provides access to the full range of reports and products released under the CCDF Policies Database project. The project website and search tool and the data files provide a more detailed set of information than what the Book of Tables provides, including a wider selection of variables and policies over time. 3. The annual Book of Tables provides key policy information for October 1 of each year. The report presents policy variations across the states and territories and is available on the project website. The Book of Tables summarizes a subset of the information available in the full database and data files, and includes information about eligibility requirements for families; application, redetermination, priority, and waiting list policies; family co-payments; and provider policies and reimbursement rates. In many cases, a variable in the Book of Tables will correspond to a single variable in the data files. Usuall

What is Growing Up in Bradford?

Growing Up in Bradford is a follow up to the initial Born in Bradford (BiB) cohort study. BiB was established to examine the determinants of health and development during childhood and throughout adult life, and recruited 12,453 mothers who experienced 13,858 births. The Growing Up study is the first full follow up of the cohort and aims to investigate the determinants of primary school aged children’s health and development, with a focus on both parents health and wellbeing and the exposure in childhood that may influence future health. The age of children included in this follow up are between seven and 11 years old.

Recruitment process

The study recruited from the pool of individuals who had taken part in the original BiB study, with as many mothers, partners and children from the original cohort recruited as possible. 6,502 children, 5,291 mothers and 826 partners completed the study.

Available data

Each child completed an age appropriate questionnaire, with one of the child’s parents completing a questionnaire about themselves and their partner and a separate questionnaire about their child. Topics included in the adults questionnaire included residential environment characteristics and satisfaction, socio-economic circumstances, social circumstances, and health and behaviour. The adult completed child survey asked questions about their child’s health, development and behaviour. The child completed child questionnaire asked questions regarding physical activity and diet.

Two subsets of adults were asked further questions. One was asked additional questions regarding their child’s diet and physical activity, and the adult’s views on parenting. The other was asked questions about their child’s experience of asthma and allergies.

Participants (adults and children) could also volunteer to provide a range of biological measures and samples. As a result, the Growing Up data also contains samples/results of blood tests, blood pressure reading, renal analyses and DEXA scans.

Ministry of Home Affairs, Government of India has defined missing child as 'a person below eighteen years of age, whose whereabouts are not known to the parents, legal guardians and any other persons who may be legally entrusted with the custody of the child, whatever may be the circumstances/causes of disappearance”. The dataset contains the state wise and gender-wise number of children reported missing in a particular year, total number of persons missing including those from previous years, number of persons recovered/traced and those unrecovered/untraced. The dataset also contains the percentage recovery of missing persons which is calculated as the percentage share of total number of persons traced over the total number of persons missing. NCRB started providing detailed data on missing & traced persons including children from 2016 onwards following the Supreme Court’s direction in a Writ Petition. It should also be noted that the data published by NCRB is restricted to those cases where FIRs have been registered by the police in respective States/UTs.

This table contains data on the number of licensed day care center slots (facility capacity) per 1,000 children aged 0-5 years in California, its regions, counties, cities, towns, and census tracts. The table contains 2015 data, and includes type of facility (day care center or infant center). Access to child care has become a critical support for working families. Many working families find high-quality child care unaffordable, and the increasing cost of child care can be crippling for low-income families and single parents. These barriers can impact parental choices of child care. Increased availability of child care facilities can positively impact families by providing more choices of child care in terms of price and quality. Estimates for this indicator are provided for the total population, and are not available by race/ethnicity. More information on the data table and a data dictionary can be found in the Data and Resources section. The licensed day care centers table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf

The format of the licensed day care centers table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

GUI14 - Respondents aged 25 years who may or may not have children. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Respondents aged 25 years who may or may not have children...