Viral suppression is measured as a viral load test result of <200 copies/mL at the most recent viral load test during measurement year. Black women are HIV priority population in the Austin TGA who have higher disparities than others with HIV.

PURPOSE Private dataset used to update the following datasets: Law Enforcement Dispatched Calls for Service: Real-Time Law Enforcement Dispatched Calls for Service: Closed

BACKGROUND Logic based on rules developed in conjunction with DEM (Michelle Geddes), POL (Jason Cunningham), and MTA and documented here.

UPDATE PROCESS To update this mapping, edit the Google Spreadsheet here, then download as a CSV and replace the data here in the portal.

NOTES 1. Rules are defined by matching the first characters of a call type (rule_type='prefix'), last characters of a call type (rule_type='suffix'), or the exact call type (rule_type='exact'). 2. Rules are applied in the following order: (1) prefix, (2) suffix, (3) exact. In case of conflict, each rule type supercedes the previous one. 3. Calls not captured by any rule will not have their geographic location suppressed in either dataset. 4. Take care to ensure that no 'prefix' rules conflict with each other, no 'suffix' rules conflict either. An example of a potential conflict: You add a rule stating that all calls beginning with 261B should only be suppressed in the real-time dataset. This would conflict with the existing rule that all 261 calls should be suppressed in both datasets. To resolve this conflict, you would need to specify the behavior for all calls that are exactly '261' as well as for each call beginning with 261A-261Z.

These images, videos, and tables show experimental data, where single lines of viscoelastic inks were extruded into moving viscoelastic support baths. Lines were printed at varying angles relative to the camera, such that videos and images captured the side of horizontal lines, cross-sections of horizontal lines, and the side of vertical lines. Metadata including pressure graphs, programmed speeds, toolpaths, and rheology data are also included.

Wildfire Suppression Difficulty Index (SDI) 90th Percentile is a rating of relative difficulty in performing fire control work under regionally appropriate fuel moisture and 15 mph uphill winds (@ 20 ft). SDI factors in topography, fuels, expected fire behavior under prevailing conditions, fireline production rates in various fuel types with and without heavy equipment, and access via roads, trails, or cross-country travel. SDI does not account for standing snags or other overhead hazards to firefighters, so it is not a firefighter hazard map. It is only showing in relative terms where it is harder or easier to perform suppression work.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

60m_noisesuppression doubledelay_noisesuppression electronicnoise_vacuum

It contains a training data set for reverberation suppression, a reverberation data set, a noise data set, and a starting point data set.

This dataset contains spiking activity from 5 utah-arrays in human LOC in response to a flash suppression paradigm.

No description was included in this Dataset collected from the OSF

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and aut

The Census of Agriculture is disseminated by Statistics Canada's standard geographic units (boundaries). Since these census units do not reflect or correspond with biophysical landscape units (such as ecological regions, soil landscapes or drainage areas), Agriculture and Agri-Food Canada in collaboration with Statistics Canada's Agriculture Division, have developed a process for interpolating (reallocating or proportioning) Census of Agriculture information from census polygon-based units to biophysical polygon-based units.

In the “Interpolated census of agriculture”, suppression confidentiality procedures were applied by Statistics Canada to the custom tabulations to prevent the possibility of associating statistical data with any specific identifiable agricultural operation or individual. Confidentiality flags are denoted where "-1" appears in data cell. This indicates information has been suppressed by Statistics Canada to protect confidentiality. Null values/cells simply indicate no data is reported.

Dataset and syntax for study manuscript titled, "The impact of experimentally instructed suppression on cardiovascular habituation during repeated stress."

This file contains a retinal OCT intensity volume as a demo dataset to generate volumetric speckle-suppressed training data using our non-local-means despeckling (TNode) script and four OCT intensity volumes and their corresponding TNode-processed intensity volumes of different tissue samples to train and test our deep learning framework used in "Probabilistic volumetric speckle suppression in OCT using deep learning" by Chintada et al. 2023. The TNode code for generating the training data and the source code for our deep learning framework are available at https://github.com/bhaskarachintada/DLTNode.git

Description of the ASL sequence A sequence with pseudo-continuous labeling, background suppression and 3D RARE Stack-Of-Spirals readout with optional through-plane acceleration was implemented for this study. At the beginning of the sequence, gradients were rapidly played with alternating polarity to correct for their delay in the spiral trajectories, followed by two preparation TRs, to allow the signal to reach the steady state. A non-accelerated readout was played during the preparation TRs, in order to obtain a fully sampled k-space dataset, used for calibration of the parallel imaging reconstruction kernel, needed to reconstruct the skipped kz partitions in the accelerated images.

Description of study Perfusion data were acquired on an elderly cohort using the single-shot, accelerated sequence. For each participant, first a high-resolution anatomical T1-weighted image was acquired with a magnetization prepared rapid gradient echo (MPRAGE) sequence. Resting perfusion data were acquired with a 1-shot 1D-accelerated readout for a total scan duration of 5 min, with labeling and PLD times of 1.5 and 1.5 s. Two M0 images with long TR and no magnetization preparation were acquired per run for CBF quantification purposes.

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000236/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000236. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Bids-validator Output

1: This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. (code: 1 - NOT_INCLUDED) /sub-01/func/sub-01_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-01_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-02/func/sub-02_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-02_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-03/func/sub-03_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-03_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-04/func/sub-04_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-04_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-05/func/sub-05_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-05_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-06/func/sub-06_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-06_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-07/func/sub-07_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-07_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-08/func/sub-08_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-08_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-09/func/sub-09_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-09_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-10/func/sub-10_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-10_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-11/func/sub-11_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-11_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-12/func/sub-12_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-12_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-13/func/sub-13_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-13_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-14/func/sub-14_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-14_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-15/func/sub-15_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-15_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-16/func/sub-16_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-16_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-17/func/sub-17_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-17_task-rest_acq-1Daccel1shot_asl.nii.gz /sub-18/func/sub-18_task-rest_acq-1Daccel1shot_asl.nii.gz This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: sub-18_task-rest_acq-1Daccel1shot_asl.nii.gz /task-rest_asl.json This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. Evidence: task-rest_asl.json

  Summary:         Available Tasks:    Available Modalities:
  61 Files, 915.87MB                T1w
  18 - Subjects
  1 - Session

This survey was conducted by the National Youth Gang Intervention and Suppression Program. The primary goals of the program were to assess the national scope of the gang crime problem, to identify promising programs and approaches for dealing with the problem, to develop prototypes from the information gained about the most promising programs, and to provide technical assistance for the development of gang intervention and suppression programs nationwide. The survey was designed to encompass every agency in the country that was engaged in or had recently engaged in organized responses specifically intended to deal with gang crime problems. Cities were screened with selection criteria including the presence and recognition of a youth gang problem and the presence of a youth gang program as an organized response to the problem. Respondents were classified into several major categories and subcategories: law enforcement (mainly police, prosecutors, judges, probation, corrections, and parole), schools (subdivided into security and academic personnel), community, county, or state planners, other, and community/service (subdivided into youth service, youth and family service/treatment, comprehensive crisis intervention, and grassroots groups). These data include variables coded from respondents' definitions of the gang, gang member, and gang incident. Also included are respondents' historical accounts of the gang problems in their areas. Information on the size and scope of the gang problem and response was also solicited.

The data are based on mathematical and computational simulations, executed and analyzed in R.

Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

This is the source data shown in Figs. 1a, 3a, and Supplementary Figs. 2, 3, 5a, and 7 for the article "Suppression of mantle convection in Uranus by superionic water stiffer than liquid" by Kimura and Murakami.

Description of the ASL sequence A sequence with pseudo-continuous labeling, background suppression and 3D RARE Stack-Of-Spirals readout with optional through-plane acceleration was implemented for this study. At the beginning of the sequence, gradients were rapidly played with alternating polarity to correct for their delay in the spiral trajectories, followed by two preparation TRs, to allow the signal to reach the steady state. A non-accelerated readout was played during the preparation TRs, in order to obtain a fully sampled k-space dataset, used for calibration of the parallel imaging reconstruction kernel, needed to reconstruct the skipped kz partitions in the accelerated images.

Description of study Single-shot and two-shot versions of the accelerated sequence were acquired during rest. For each participant, first a high-resolution anatomical T1-weighted image was acquired with a magnetization prepared rapid gradient echo (MPRAGE) sequence. Subjects were instructed to remain still and awake, while resting perfusion data were acquired using either 1-shot or 2-shot 1D-accelerated readout. 64 and 32 label-control images were acquired, respectively, during a total scan time of 5 min. Labeling and PLD times where 1.8 and 1.8 s. Two M0 images with long TR and no magnetization preparation were acquired per run for CBF quantification purposes.

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Quality Control

Mriqc was run on the dataset. Results are located in derivatives/mriqc. Learn more about it here: https://mriqc.readthedocs.io/en/latest/

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000235/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000235. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Bids-validator Output