The purpose of this data collection is to provide an official public record of the business of the federal bankruptcy courts. The data include all petitions filed under the Bankruptcy Code in the United States Bankruptcy Courts on or after October 1, 1993, and any petitions filed before October 1, 1993, that were still pending on that date. The records are organized according to the fiscal year of termination with cases still pending at the end included in a separate pending dataset. The records in Part 1, Terminations Data, 2003, include cases that terminated in the year 2003. Part 2, Pending Data, 2003, contains cases still pending in the year 2003. For the bankruptcy data, the unit of analysis is a single case.

Analysis of ‘District Attorney Trials’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/bf4e8477-c11c-42f7-80c8-cf12a7062938 on 11 February 2022.

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

A. SUMMARY

Please note that the "Data Last Updated" date on this page denotes the most recent time the open data portal automation process ran and does not reflect the most recent update to the data in this dataset. To confirm the completeness of this dataset please contact the District Attorney's office at districtattorney@sfgov.org.

This dataset contains information on SFDA trial outcomes since 2014. It includes information on all jury trials except for cases that have been sealed or expunged due to record clearance protocols.

Jury trials are resource-intensive, and often the most public part of the criminal process. The vast majority of cases do not go to trial and resolve after plea negotiations between the prosecutor and the defense attorney. If the case cannot be resolved through plea negotiations, it may go to trial before a judge or a jury. A jury is made up of 12 people from the community, and often a few alternate jurors. Most trials are jury trials, and both the prosecution and the defense will have an opportunity to excuse some jurors before a final jury is sworn. The selected jury will hear the evidence and will be tasked with determining whether the charges have been proven beyond a reasonable doubt.

More information about the trial process and this dataset can be found under the Trials tab on the DA Stat page.

Disclaimer: The San Francisco District Attorney's Office does not guarantee the accuracy, completeness, or timeliness of the information as the data is subject to change as modifications and updates are completed.

B. HOW THE DATASET IS CREATED

At the conclusion of a trial, relevant data is manually entered into the District Attorney Office's case management system. Trial data reports are pulled from this system on a semi-regular basis, cleaned, anonymized, and added to Open Data.

C. UPDATE PROCESS District Attorney's Office strive to update this dataset every month. However, the creation of this dataset requires a manual pull from the Office's case management system and is dependent on staff availability. The Open Data portal automation process will run the 1st of every month regardless of if an update to this dataset has been made.

D. HOW TO USE THIS DATASET Please review the Trials tab on the DA Stat page for more information about this dataset.

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

The data contain records of defendants in criminal cases filed in United States District Court during fiscal year 2004. The data were constructed from the Administrative Office of the United States District Courts' (AOUSC) criminal file. Defendants in criminal cases may be either individuals or corporations. There is one record for each defendant in each case filed. Included in the records are data from court proceedings and offense codes for up to five offenses charged at the time the case was filed. (The most serious charge at termination may differ from the most serious charge at case filing, due to plea bargaining or action of the judge or jury.) In a case with multiple charges against the defendant, a "most serious" offense charge is determined by a hierarchy of offenses based on statutory maximum penalties associated with the charges. The data file contains variables from the original AOUSC files as well as additional analysis variables, or "SAF variables," that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 4.1-4.5 and 5.1-5.6. Variables containing information (e.g., name, Social Security number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

Adult criminal courts, charges and cases by offence, age and sex of accused and type of decision, Canada, provinces, territories, ten jurisdictions and eight jurisdictions, five years of data.

As a part of the effort to promote e-governance and judicial transparency, China has been promoting mass online digitalization of court, including an archive of judgment text (司法文书), a platform of online trial videos and live broadcasting (庭审直播), and a judgment implementation tracker (判决执行). China has become one of the few countries that allow cameras in the courtroom. Though a growing number of studies use court decision data, little research has been conducted on the court trial videos. The goal of the Chinese Courtroom Video Database is to meet the needs of those interested in broad research of government policy diffusion, judicial transparency, and judicial behavior in the China context by filling the vacancy in judicial data and providing a new perspective to the existing scholarship. The database includes two sets of data. The first dataset is a catalog of half-million entries of criminal and administrative trial videos in all 31 provinces from January 2013 to February 2019. Each profile records the basic information of a trial video, such as case identification number, date and time of the trial, participants, reason of trial, location of the court, number of views of the video, and other descriptions. The second dataset is a collection of 1,491 audio files of online criminal trials in Yunnan, China. Each audio was downloaded and converted from the original video. The datasets were collected using the Selenium package of Python and Downie, an online stream downloader.

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 autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

Context

The goal of this Corporate Prosecutions Registry is to provide comprehensive and up-to-date information on federal organizational prosecutions in the United States, so that we can better understand how corporate prosecutions are brought and resolved. It includes detailed information about every federal organizational prosecution since 2001, as well as deferred and non-prosecution agreements with organizations since 1990.

Dataset Description

These data on deferred prosecution and non-prosecution agreements were collected by identifying agreements through news searches, press releases by the Department of Justice and U.S. Attorney’s Office, and also when practitioners brought agreements to our attention. The Government Accountability Office conducted a study of federal deferred prosecution and non-prosecution agreements with organizations, and in August 2010, the GAO provided a list of those agreements in response to an information request. Finally, searches of the Bloomberg dockets database located additional prosecution agreements with companies that had not previously been located. Jon Ashley has contacted U.S. Attorney’s Offices to request agreements. An effort by the First Amendment Clinic at the University of Virginia School of Law to litigate Freedom of Information Act requests resulted in locating a group of missing agreements which are now available on the Registry.

This Registry only includes information about federal organizational prosecutions, and not cases brought solely in state courts. Nor does this Registry include leniency agreements entered through the Antitrust Division’s leniency program, which are kept confidential. The Registry also does not include convictions overturned on appeal, or cases in which the indictment was dismissed or the company was acquitted at a trial.

The U.S. Sentencing Commission reports sentencing data concerning organizational prosecutions each year. That data does not include cases resolved without a formal sentencing, such as deferred and non-prosecution agreements.

Acknowledgements

The Corporate Prosecutions Registry is a project of the University of Virginia School of Law. It was created by Professor Brandon Garrett (bgarrett@virginia.edu) and Jon Ashley (jaa6c@virginia.edu). Please cite this dataset as: “Brandon L. Garrett and Jon Ashley, Corporate Prosecutions Registry, University of Virginia School of Law, at http://lib.law.virginia.edu/Garrett/corporate-prosecution-registry/index.html”

Inspiration

• Which industries face the most prosecutions?
• Which government organizations have been the most successful at pursuing cases against corporations?
• Not a single case in the dataset led to a trial conviction. Can you link these corporate cases to criminal cases against the individuals involved? How many of them were convicted instead?

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

Note: 11/1/2023: Publication of the COVID data will be delayed because of technical difficulties. Note: 9/20/2023: With the end of the federal emergency and reporting requirements continuing to evolve, the Indiana Department of Health will no longer publish and refresh the COVID-19 datasets after November 15, 2023 - one final dataset publication will continue to be available. Note: 5/10/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. Note: 3/22/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. Note: 3/15/2023 test data will be removed from the COVID dashboards and HUB files in recognition of the fact that widespread use of at-home tests and a decrease in lab testing no longer provides an accurate representation of COVID-19 spread. Number of Indiana COVID-19 cases and deaths by age group, gender, race and ethnicity by day. All data displayed is preliminary and subject to change as more information is reported to IDOH. Expect historical data to change as data is reported to IDOH. Historical Changes: 1/11/2023: Due to a technical issue updates are delayed for COVID data. New files will be published as soon as they are available. 1/5/2023: Due to a technical issue the COVID datasets were not updated on 1/4/23. Updates will be published as soon as they are available. 9/29/22: Due to a technical difficulty, the weekly COVID datasets were not generated yesterday. They will be updated with current data today - 9/29 - and may result in a temporary discrepancy with the numbers published on the dashboard until the normal weekly refresh resumes 10/5. 9/27/2022: As of 9/28, the Indiana Department of Health (IDOH) is moving to a weekly COVID update for the dashboard and all associated datasets to continue to provide trend data that is applicable and usable for our partners and the public. This is to maintain alignment across the nation as states move to weekly updates. 2/10/2022: Data was not published on 2/9/2022 due to a technical issue, but updated data was released 2/10/2022. 12/30/21: This dataset has been updated, and should continue to receive daily updates. 12/15/21: The file has been adjusted with data through 12/13, and regular updates will resume to it today. 11/12/2021: Historical re-infections have been added to the case counts for all pertinent COVID datasets back to 9/1/2021 and new re-infections will be added to the total case counts as they are reported in accordance with CDC guidance. 06/23/2021: COVID Hub files will no longer be updated on Saturdays. The normal refresh of these files has been changed to Mon-Fri. 06/10/2021: COVID Hub files will no longer be updated on Sundays. The normal refresh of these files has been changed to Mon-Sat. 6/03/2021 : A batch of historical negative and positive test results added 16,492 historical tests administered, 7,082 tested individuals, and 765 historical cases to today's counts. These cases are not included in the new positive counts but have been added to the total positive cases. Today’s total case counts include historical cases received from other states. 2/4/2021 : Today’s dataset now includes 1,507 historical deaths identified through an audit of 2020 and 2021 COVID death records and test results.

Judgment documents are an important carrier for the public trial activities, reasons, basis, and results of the people's courts. The judicial summary is a compression, induction, and summary of the content of the judgment documents, reflecting the judgment process, facts, reasons, and judgment basis during the case trial process. The judicial summary of judicial documents has practical significance and necessity for the construction of the rule of law in China. Specifically, we will provide the original text of the judgment document, and the contestant's task is to output the corresponding judicial summary text. We allow contestants to use any external information as knowledge to assist the model, but we require contestants not to engage in online operations during the prediction process. More detailed information and the resources mentioned below can be referred to https://github.com/china-ai-law-challenge/CAIL2020/tree/master/sfzy .

Analysis of ‘Covid-19 Tests by Race Ethnicity and Date’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/68410b4b-052f-4ce3-8d0c-873b5664f1a4 on 27 January 2022.

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

Note: As of April 16, 2021, this dataset will update daily with a five-day data lag.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ ethnicity and date. For each day, this dataset represents the daily count of tests collected by race/ethnicity, and how many of those were positive, negative, and indeterminate. Tests in this dataset include all tests collected from San Francisco residents who listed a San Francisco home address at the time of testing, and tests that were collected in San Francisco but had a missing home address. Data are based on information collected at the time of testing.

For recent data, about 25-30% of tests are missing race/ ethnicity information. Tests where the race/ ethnicity of the patient is unknown are included in the dataset under the "Unknown" category.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. Each positive test result is investigated. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times. In both cases, these results are not included in San Francisco’s total COVID-19 case count. To track the number of cases by race/ ethnicity, see this dashboard: https://data.sfgov.org/stories/s/w6za-6st8

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ ethnicity by the total number of residents who identify as that race/ ethnicity (according to the 2018 5-year estimates from the American Community Survey), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://data.sfgov.org/stories/s/nudz-9tg2

There are two other datasets related to tests: 1. COVID-19 Tests 2. <a href="https://data.sfgov.org/dataset/Covid-19-Testing-by

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

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Demographic Summary of Available Imaging

Characteristic	Value (N = 26254)
Age (years)	Mean ± SD: 61.4± 5 Median (IQR): 60 (57-65) Range: 43-75
Sex	Male: 15512 (59%) Female: 10742 (41%)
Race	White: 23969 (91.3%) Black: 1135 (4.3%) Asian: 547 (2.1%) American Indian/Alaska Native: 88 (0.3%) Native Hawaiian/Other Pacific Islander: 87 (0.3%) Unknown: 428 (1.6%)
Ethnicity	Not Available

Background: The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.

Methods: From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009. This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.

Results: The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).

Conclusions: Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385).

Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data. The full clinical data set from NLST is available through CDAS. Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial. (These previously were restricted.)

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total. In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below) Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1% To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End). Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data. This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date. Data are prepared by close of business Monday through Saturday for public display. C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments. In order to track trends over time, a data user can analyze this data by "specimen_collection_date". Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of pe

The dataset presented in the article comprises of finger forces of participants during a finger pressing task. The finger pressing task involves the production of fingertip forces using Index, Middle, Ring, and Little (I, M, R&L) fingers of the right hand. During experimentation, first, the participants performed the Maximum Voluntary Contraction (MVC) task, where they were instructed to produce maximum possible force from each finger individually and all together. The MVC task is followed by the finger pressing task that involves two conditions, i.e., without the epilogue and with the epilogue. The epilogue is a particular case of post-trial visual feedback where only the outcome of the just-concluded trial is shown to the participant. The force levels in the experiments of both these cases were normalised with respect to MVC. Fourteen healthy participants were recruited for the experiments and were instructed to produce fingertip forces using four fingers of the right hand with the target line at 15% MVC (15% of the force that they produced in the MVC task). Each of the two conditions has 30 trials, and the subjects performed 60 trials in total for the complete experiment apart from the MVC task. A single trial of both the conditions lasted for sixteen seconds, where for the initial eight seconds, there is visual feedback followed by the visual occlusion period where there is no visual feedback. For the experiment involving epilogue, the participants were shown the feedback of the just-concluded trial, whereas for the without epilogue case, they were not given any post-trial feedback. The dataset consists of three files; the first file has the data of Maximum Voluntary Contraction (MVC) data, the second file has the data of without epilogue case, and the third file has the data of with epilogue case. However, the date for with epilogue and without epilogue conditions have also been uploaded in CSV format inside the folder named Supplementary data with the explanation given in a file name read me. The LabVIEW code and Protocol used for the experiment has been added, and it is inside the folder LabVIEW Code.

This dataset has been retired as of February 17, 2023. This dataset will be kept for historical purposes, but will no longer be updated. Similar data are available on the state’s open data portal: https://data.chhs.ca.gov/dataset/covid-19-time-series-metrics-by-county-and-state.

A. DATASET DESCRIPTION This dataset contains COVID-19 positive confirmed cases aggregated by several different geographic areas and by day. COVID-19 cases are mapped to the residence of the individual and shown on the date the positive test was collected. In addition, 2019 American Community Survey (ACS) 5-year population estimates are included to calculate the cumulative rate per 10,000 residents.

Dataset covers cases going back to March 18th, 2020 when the first person in Marin County tested positive for COVID-19. This data may not be immediately available for recently reported cases and data will change to reflect as information becomes available. Data updated daily.

COVID-19 case data undergo quality assurance and other data verification processes and are continually updated to maximize completeness and accuracy of information. This means data may change for previous days as information is updated.

Geographic areas summarized are: 1. City, Town, or Community Area 2. Census Tracts 3. Census ZIP Code Tabulation Areas (ZCTAs)

B. HOW THE DATASET IS CREATED Addresses from the COVID-19 case data are geocoded by Marin County HHS. Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area for a given date.

The 2019 ACS estimates for population provided by the Census are used to create a cumulative rate which is equal to ([cumulative count up to that date] / [acs_population]) * 10000) representing the number of total cases per 10,000 residents (as of the specified date).

C. UPDATE PROCESS Geographic analysis is scripted by Marin HHS staff and synced to this dataset each day.

D. HOW TO USE THIS DATASET This dataset can be used to track the spread of COVID-19 throughout Marin County in a variety of geographic areas. Note that the new cases column in the data represents the number of new cases confirmed in a certain area on the specified day, while the cumulative cases column is the cumulative total of cases in a certain area as of the specified date.

Privacy rules in effect To protect privacy, certain rules are in effect: 1. Any area with a cumulative case count less than 10 are dropped for all days the cumulative count was less than 10. These will be null values. For example if a zip code did not have 10 cumulative cases until June 1, 2020 that location will not be included in the dataset until June 1. 2. Once an area has a cumulative case count of 10 or greater, that area will have a new row of case data every day following. 3. 3. Cases are dropped altogether for areas where acs_population < 1000. Some adjacent geographic areas may be combined until the ACS population exceeds 1,000 to still provide information for these regions.

Note: 14-day case rate or 30-day case rate where the counts are lower than 20 may be unstable. We advise caution in interpreting rates at these small numbers.

A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes.

This file contains the following characteristics per case tested positive in the Netherlands: Date for statistics, Age group, Sex, Hospital admission, Death, Week of death, Province, Notifying GGD

The file is structured as follows: A record for every laboratory confirmed COVID-19 patient in the Netherlands, since the first COVID-19 report in the Netherlands on 27/02/2020 (Date for statistics may be earlier). The file is refreshed daily at 4 pm, based on the data as registered at 10 am that day in the national system for notifiable infectious diseases (Osiris AIZ).

Description of the variables

Date_file: Date and time when the data was published by RIVM

Date_statistics: Date for statistics; first day of illness, if unknown, date lab positive, if unknown, report date to GGD (format: yyyy-mm-dd)

Date_statistics_type: Type of date that was available for date for the variable "Date for statistics", where: DOO = Date of disease onset : First day of illness as reported by GGD. Please note: it is not always known whether this first day of illness was really Covid-19. DPL = Date of first Positive Labresult : Date of the (first) positive lab result. DON = Date of Notification : Date on which the notification was received by the GGD.

Agegroup: Age group at life; 0-9, 10-19, ..., 90+; at death <50, 50-59, 60-69, 70-79, 80-89, 90+, Unknown = Unknown

Sex: Sex; Unknown = Unknown, Male = Male, Female = Female

Province: Name of the province (based on the patient's whereabouts)

Hospital_admission: Hospital admission reported by the GGD. Unknown = Unknown, Yes = Yes, No = No From May 1, 2020, the indication of hospitalization will be related to Covid-19. If not, the value of this column is "No". Until June 1, only seriously ill people were tested, a large part of these people had already been or were admitted shortly afterwards. As a result, the hospital admissions registered by the GGD were more complete during the first wave. As of June 1, everyone can be tested and more people will be tested at an early stage. As a result, the GGD is not always informed, or with a delay, of a hospital admission. That is why RIVM has been actively naming the registered hospital admissions of the NICE Foundation (https://data.rivm.nl/geonetwork/srv/dut/catalog.search#/metadata/4f4ad069-8f24-4fe8-b2a7-533ef27a899f) since 6 October. RIVM uses these figures as a guideline because they provide a more complete picture than the hospital admissions reported by the GGD. Click here (https://www.rivm.nl/nieuws/nummer-nieuw-melde-covid-19-verzekeraars-stable) for more information about this.

Deceased: Death. Unknown = Unknown, Yes = Yes, No = No

Week of Death: Week of death. YYYYMM according to ISO week notation (start from Monday to Sunday)

Municipal_health_service: GGD that made the report.

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 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). This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. 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. 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; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Data have been generated during the INFORE22 sea trial, run by CMRE from 21st February to 1st March 2022 in the Gulf of La Spezia to experiment and validate the CMRE hybrid robotic network in support of the INFORE maritime use case [*].

The dataset, which covers two days of experiments (28 February and 1 March 2022), includes:

- GPS NMEA positions of targets

- Target contact messages from marine autonomous robots

- Thermal camera video streams with metadata on detected targets

- AIS data (log of NMEA messages, processed messages, maps of vessel positions), showing vessel traffic in the Gulf of La Spezia during the experiments

This dataset, is complemented by a Raw acoustic data from SliTa array towed on AUVs (and Matlab reader) published by the authors in Zenodo https://zenodo.org/record/6375048.

For a full description of the dataset, see [**]

Conditions for use and distributions

This dataset is provided by NATO STO CMRE within the condition stated in the H2020 INFORE Grant and Consortium Agreement (GA. no. 825070) . The creation of derived products, as well the use in scientific publications must be pre-approved by CMRE and acknowledged.

Non liability clause

These data and software are provided in the scope of INFORE by NATO STO CMRE, in compliance with the INFORE open data strategy. Data and software are provided as they are. NATO and NATO STO CMRE decline any responsibility for bugs and any damage or accidental issue that the use of those data and software could cause.

References

[*] Gabriele Ferri, Raffaele Grasso, Elena Camossi, Francesca de Rosa, Alessandro Faggiani, Kevin LePage, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis, Maritime Use Case: Final Evaluation Report Work Package 3 Tasks 3.3 INFORE Deliverable D3.3

[**] Nikos Giatrakos, Antonios Deligiannakis, Arnau Montagud, Miguel Ponce de León, Thaleia Ntiniakou, Holger Arndt, Stefan Burkard, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Raffaele Grasso, Gabriele Ferri, Arjan Vermeij, Alessandro Faggiani, Elena Camossi, Kevin Le Page: Data Management Plan V3 Work Package 8 Task 8.3 INFORE Deliverable 8.6

The datasets consist of energy consumption of applications over multiple versions, associated with execution traces under various test cases.

Our datasets were generated by analyzing two open source applications, the text editor gedit and the audio player mpg123. For each application, we have built multiple versions and developed two test cases to gather the data. The data gathered from each test case forms a dataset and it contains the mean power consumption and the corresponding invocation count of system calls for each version. Each dataset is in CSV format and each row in the CSV file represents the data of each application version. The number of columns in each dataset varies because of the number of different system calls traced in different applications as well as test cases. But the first column is always the mean power consumption of each application version ordered chronologically. The rest of the columns are different system calls and each entry shows the number of system call invocations.

Explanation of the text files

gedit-versionsstores the gedit version numbers we have tested under text editing and syntax highlighting test cases.

gedit-commit-versionstores the tested gedit version numbers with the corresponding git commit hash.

mpg123-play-mp3-versionsstores the mpg123 version numbers we have tested under mp3 playing test case.

mpg123-play-stream-versionsstores the mpg123 version number we have tested under stream playing test case.

License

The LICENSE for the Source Code is generally GPL-2+ or GPL-2 compatible.

For DATA creativecommons.org/licenses/by/4.0 The LICENSE for the Data is CC-BY 4.0 please attribute Abram Hindle using the following instructions:

To properly attribute Chenlei Zhang on as per the requirements of CC-BY 4.0:

@MastersThesis{zhang2013thesis, author = {Chenlei Zhang}, title = {The Impact of User Choice and Software Change on Energy Consumption}, school = {University of Alberta}, year = {2013} }

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study was to produce knowledge about how to prevent at-risk youth from joining gangs and reduce delinquency among active gang members. The study evaluated a modification of Functional Family Therapy, a model program from the Blueprints for Healthy Youth Development initiative, to assess its effectiveness for reducing gang membership and delinquency in a gang-involved population. The collection contains 5 SPSS data files and 4 SPSS syntax files: adolpre_archive.sav (129 cases, 190 variables), adolpost_archive.sav (119 cases, 301 variables), Fidelity.archive.sav (66 cases, 25 variables), parentpre_archive.sav (129 cases, 157 variables), and parentpost_archive.sav {116 cases, 220 variables).