Unlock the full potential of CNN broadcast data with our comprehensive dataset featuring transcripts, program schedules, headlines, topics, and multimedia resources. This all-in-one dataset is designed to empower media analysts, researchers, journalists, and advocacy groups with actionable insights for media analysis, transparency studies, and editorial assessments.

Dataset Features

Transcripts: Access detailed broadcast transcripts, including headlines, content, author details, and publication dates. Perfect for analyzing media framing, topic frequency, and news narratives across various programs. Program Schedules: Explore program schedules with accurate timing, show names, and related metadata to track news coverage patterns and identify trends. Topics and Keywords: Analyze categorized topics and keywords to understand content diversity, editorial focus, and recurring themes in news broadcasts. Multimedia Content: Gain access to videos, images, and related articles linked to each broadcast for a holistic understanding of the news presentation. Metadata: Includes critical data points like publication dates, last updates, content URLs, and unique IDs for easier referencing and cross-analysis.

Customizable Subsets for Specific Needs Our CNN dataset is fully customizable to match your research or analytical goals. Focus on transcripts for in-depth media framing analysis, extract multimedia for content visualization studies, or dive into program schedules for broadcast trend analysis. Tailor the dataset to ensure it aligns with your objectives for maximum efficiency and relevance.

Popular Use Cases

Media Analysis: Evaluate news framing, content diversity, and topic coverage to assess editorial direction and media focus. Transparency Studies: Analyze journalistic standards, corrections, and retractions to assess media integrity and accountability. Audience Engagement: Identify recurring topics and trends in news content to understand audience preferences and behavior. Market Analysis: Track media coverage of key industries, companies, and topics to analyze public sentiment and industry relevance. Journalistic Integrity: Use transcripts and metadata to evaluate adherence to reporting practices, fairness, and transparency in news coverage. Research and Scholarly Studies: Leverage transcripts and multimedia to support academic studies in journalism, media criticism, and political discourse analysis.

Whether you are evaluating transparency, conducting media criticism, or tracking broadcast trends, our CNN dataset provides you with the tools and insights needed for in-depth research and strategic analysis. Customize your access to focus on the most relevant data points for your unique needs.

The Prompt Assessment of Global Earthquakes for Response (PAGER) System plays a primary alerting role for global earthquake disasters as part of the U.S. Geological Surveys (USGS) response protocol. PAGER monitors the USGSs near real-time U.S. and global earthquake origins and automatically identifies events that are of societal importance, well in advance of ground-truth or news accounts. Current PAGER notifications and Web pages estimate the population exposed to each seismic intensity level. In addition to being a useful indicator of potential impact, PAGERs intensity/exposure display provides a new standard in the dissemination of rapid earthquake information. This paper provides an overview of the PAGER system, both of its current capabilities and ongoing research and development. Specifically, this paper summarises the underpinning models and datasets developed to improve PAGER exposure and impact modules. These include: global site-response models, enhanced earthquake source and loss databases, the Atlas of ShakeMaps and population exposure catalogue, and a global building inventory. The use of these methods and databases are demonstrated using the USGSs response to the 12 May 2008 Wenchuan, China, earthquake. Finally, we comment on the potential use of PAGER tools and databases for improved near real-time earthquake alerting in Australia.

Database of curated links to molecular resources, tools and databases selected on the basis of recommendations from bioinformatics experts in the field. This resource relies on input from its community of bioinformatics users for suggestions. Starting in 2003, it has also started listing all links contained in the NAR Webserver issue. The different types of information available in this portal: * Computer Related: This category contains links to resources relating to programming languages often used in bioinformatics. Other tools of the trade, such as web development and database resources, are also included here. * Sequence Comparison: Tools and resources for the comparison of sequences including sequence similarity searching, alignment tools, and general comparative genomics resources. * DNA: This category contains links to useful resources for DNA sequence analyses such as tools for comparative sequence analysis and sequence assembly. Links to programs for sequence manipulation, primer design, and sequence retrieval and submission are also listed here. * Education: Links to information about the techniques, materials, people, places, and events of the greater bioinformatics community. Included are current news headlines, literature sources, educational material and links to bioinformatics courses and workshops. * Expression: Links to tools for predicting the expression, alternative splicing, and regulation of a gene sequence are found here. This section also contains links to databases, methods, and analysis tools for protein expression, SAGE, EST, and microarray data. * Human Genome: This section contains links to draft annotations of the human genome in addition to resources for sequence polymorphisms and genomics. Also included are links related to ethical discussions surrounding the study of the human genome. * Literature: Links to resources related to published literature, including tools to search for articles and through literature abstracts. Additional text mining resources, open access resources, and literature goldmines are also listed. * Model Organisms: Included in this category are links to resources for various model organisms ranging from mammals to microbes. These include databases and tools for genome scale analyses. * Other Molecules: Bioinformatics tools related to molecules other than DNA, RNA, and protein. This category will include resources for the bioinformatics of small molecules as well as for other biopolymers including carbohydrates and metabolites. * Protein: This category contains links to useful resources for protein sequence and structure analyses. Resources for phylogenetic analyses, prediction of protein features, and analyses of interactions are also found here. * RNA: Resources include links to sequence retrieval programs, structure prediction and visualization tools, motif search programs, and information on various functional RNAs.

The AQS Data Mart is a database containing all of the information from AQS. It has every measured value the EPA has collected via the national ambient air monitoring program. It also includes the associated aggregate values calculated by EPA (8-hour, daily, annual, etc.). The AQS Data Mart is a copy of AQS made once per week and made accessible to the public through web-based applications. The intended users of the Data Mart are air quality data analysts in the regulatory, academic, and health research communities. It is intended for those who need to download large volumes of detailed technical data stored at EPA and does not provide any interactive analytical tools. It serves as the back-end database for several Agency interactive tools that could not fully function without it: AirData, AirCompare, The Remote Sensing Information Gateway, the Map Monitoring Sites KML page, etc.

AQS must maintain constant readiness to accept data and meet high data integrity requirements, thus is limited in the number of users and queries to which it can respond. The Data Mart, as a read only copy, can allow wider access.

The most commonly requested aggregation levels of data (and key metrics in each) are:

Sample Values (2.4 billion values back as far as 1957, national consistency begins in 1980, data for 500 substances routinely collected) The sample value converted to standard units of measure (generally 1-hour averages as reported to EPA, sometimes 24-hour averages) Local Standard Time (LST) and GMT timestamps Measurement method Measurement uncertainty, where known Any exceptional events affecting the data NAAQS Averages NAAQS average values (8-hour averages for ozone and CO, 24-hour averages for PM2.5) Daily Summary Values (each monitor has the following calculated each day) Observation count Observation per cent (of expected observations) Arithmetic mean of observations Max observation and time of max AQI (air quality index) where applicable Number of observations > Standard where applicable Annual Summary Values (each monitor has the following calculated each year) Observation count and per cent Valid days Required observation count Null observation count Exceptional values count Arithmetic Mean and Standard Deviation 1st - 4th maximum (highest) observations Percentiles (99, 98, 95, 90, 75, 50) Number of observations > Standard Site and Monitor Information FIPS State Code (the first 5 items on this list make up the AQS Monitor Identifier) FIPS County Code Site Number (unique within the county) Parameter Code (what is measured) POC (Parameter Occurrence Code) to distinguish from different samplers at the same site Latitude Longitude Measurement method information Owner / operator / data-submitter information Monitoring Network to which the monitor belongs Exemptions from regulatory requirements Operational dates City and CBSA where the monitor is located Quality Assurance Information Various data fields related to the 19 different QA assessments possible

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.epa_historical_air_quality.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Data provided by the US Environmental Protection Agency Air Quality System Data Mart.

The National Institute of Food and Agriculture is committed to serving its stakeholders, Congress, and the public by using new technologies to advance greater openness. To strengthen transparency and promote open government, NIFA is providing easy access to data and metrics on how the agency disseminates funding. NIFA is committed to increasing transparency and making technical advancements to ensure that data is easily accessible. The Data Gateway provides the ability to filter and export data. Recently added features to the Congressional District Map and Data Gateway Search make for an improved user experience when searching and reporting information on NIFA-administered grants and projects! New interactive features in the Congressional District Map allow users to see the total amount of funding by state and further to drill down to the individual awards. Funding information is available for awards made from 2011-2015. Simply click on a state listing on the right of the screen. No need to create your own search if you are looking for NIFA funding by Congressional District. Key enhancements in the Data Gateway Search tool include:

A project-based display of data Embedded help text within tool Drop down lists allowing you to choose the fields you want to search and display Expanded filter lists

The Current Research Information System (CRIS) provides documentation and reporting for ongoing agricultural, food science, human nutrition, and forestry research, education and extension activities for the United States Department of Agriculture; with a focus on the National Institute of Food and Agriculture (NIFA) grant programs. Projects are conducted or sponsored by USDA research agencies, state agricultural experiment stations, land-grant universities, other cooperating state institutions, and participants in NIFA-administered grant programs, including Small Business Innovation Research and Agriculture and Food Research Initiative. The Planning, Accountability, & Reporting Staff office of NIFA is responsible for maintaining CRIS. Resources in this dataset:Resource Title: NIFA Reporting Portal. File Name: Web Page, url: https://portal.nifa.usda.gov Main html page for the database

A later version of this dataset exists published 2019-01-18, accessible through the data links on this page.

This dataset contains records of sting events and specimen samples of jellyfish (Irukandji) along the north Queensland coast from December 1998 to March 2017. This dataset contains an extract (265 records in CSV format) of the publicly available data contained in the Venomous Jellyfish Database. The full database contains approximately 3000 sting events from around Australia and includes records from sources that have not yet been cleared for release.

This extract was made for eAtlas as part of the 2.2.3 NESP Irukandji forecasting system project and used as part of the development of the Irukandji forecasting model. The data was compiled from numerous sources (noted in each record), including Lisa-ann Gershwin and media reports.

The sting data includes primary information such as date, time of day and locality of stings, as well as secondary details such as age and gender of the sting victim, where on the body they were stung, their activity at the time of the sting and their general medical condition.

Limitations:

This data shows the occurrence of reported jellyfish stings and specimens along the north Queensland coast. It does NOT provide a prediction of where jellyfish or jellyfish sting events may occur.

These records represent a fraction of known sting events and specimen collections, with more being added to the list of publicly available data as permissions are granted.

Historical data dates may be coarse, showing month and year that the sting occurred in. Some events have date only.

Methods:

This data set contains information on sting events and specimen collections that have occurred around Australia, which involved venomous jellyfish (Irukandji syndrome-producing species in the genera Carukia, Malo, Morbakka).

This data was collected over numerous years by Lisa-ann Gershwin from various sources, predominantly news reports. This data was entered into an Excel spreadsheet, which formed the basis of the Venomous Jellyfish Database. This database was developed as part of the 2.2.3 NESP Irukandji forecasting system project.

Some data have been standardised, e.g., location information and sting site on the body. Data available to the public have been approved by the data owners, or came from a public source (e.g. newspaper reports, media alerts).

Format:

Comma Separated Value (CSV) table. eAtlas Note: The original database extract was provided as an Excel spreadsheet table. This was converted to a CSV file.

Data Dictionary:

• CSIRO_ID: Unique id
• EVENT_TYPE: Type of event – sting or specimen
• STATE: State in which event occurred
• REGION: Broader region of State the event occurred in
• LOCAL_GOV_AREA: Local government area that the event occurred in – if known
• MAIN_LOCALITY: Main locality that the event occurred in
• SITE_INFO: Site details/comments
• YEAR: Year event occurred
• MONTH: Month event occurred
• DAY: Day of the month the event occurred
• EVENT_TIME: Time the event occurred HH24:MI If time is unknown then NULL
• EVENT_RECORDED: time/date event reported e.g. early afternoon, morning, on weekend
• EVENT_COMMENTS: Comments about the event
• LAT: Latitude in decimal degrees
• LON: Longitude in decimal degrees
• LOCATION_ACCURACY: How accurate the location is
• EVENT_OFFSHORE_ONSHORE: Where the event occurred (if known) – beach, island, reef
• LOCATION_COMMENTS: Comments relating to the location of the event
• WATER_DEPTH_M: Depth of water, in metres, that the event occurred in (if known)
• AGE: Age of patient if known
• SEX: Gender of patient if known
• HOME: Home state/county of patient
• HOSPITAL: Hospital the patient was treated at (if known)
• STING_SITE_REPORTED: Reported sting site on the body
• STING_SITE_BODY: Standardised area on body that sting was reported – upper limb, lower limb etc.
• NUMBER_STINGS: Number of stings recorded, if known
• VISIBLE_STING: The nature of visible sting marks, if reported
• PPE_WORN: Was Personal Protective Equipment (PPE) worn?
• PATIENT_COMMENTS: Comments about the patient
• TIME_TO_ONSET: Delay between sting and onset of symptoms, if reported
• PATIENT_CONDITION: State the patient was in, e.g. distressed, calm, stable
• BLOOD_PRESSURE: Comments relating to blood pressure of the patient
• NAUSEA_VOMITING: Did the patient experience nausea and/or vomiting?
• PAIN: Location and/or intensity of pain experienced by the patient
• SWEATING: Did the patient experience sweating?
• TREATMENT: What treatment the patient was given
• DISCHARGED: When the patient was discharged from hospital
• ONGOING_SYMPTOMS: What ongoing symptoms the patient is experiencing
• NEMATO_SAMPLES: Were nematocyst samples taken?
• SPECIES_NAME: Species name, if determined
• PATROL: Was the sting on a patrolled beach
• CURATOR: Where the data came from e.g. Gershwin = Lisa-ann Gershwin
• DATA_CODE: Access constraint on data
• REFERENCE: Source of the information for event
• ENTERED_BY: Who entered the data
• ENTERED_DATE: When the data was entered

References:

Gershwin, L. (2013). Stung! On Jellyfish Blooms and the Future of the Ocean. Chicago, University of Chicago Press.

Lisa-Ann Gershwin , Monica De Nardi , Kenneth D. Winkel & Peter J. Fenner (2010) Marine Stingers: Review of an Under-Recognized Global Coastal Management Issue, Coastal Management, 38:1, 22-41, http://dx.doi.org/10.1080/08920750903345031

Gershwin L, Condie SA, Mansbridge JV, Richardson AJ. 2014 Dangerous jellyfish blooms are predictable. J. R. Soc. Interface 11: 20131168. http://dx.doi.org/10.1098/rsif.2013.1168

Gershwin, L., A. J. Richardson, K. D. Winkel, P. J. Fenner, J. Lippmann, R. Hore, G. Avila-Soria, D. Brewer, R. J. Kloser, A. Steven and S. Condie (2013). Biology and ecology of Irukandji jellyfish (Cnidaria: Cubozoa). Advances in Marine Biology 66: 1-85.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2016-18-NESP-TWQ-2\2.2.3_Jellyfish-early-warning\AU_NESP-TWQ-2-2-3_CSIRO_Venomous-Jellyfish-DB

IntroductionChronic pulmonary aspergillosis (CPA) is a debilitating disease estimated to affect over 3 million people worldwide. Pulmonary tuberculosis (PTB) is the most significant risk factor for CPA. However, the true burden of CPA at the time of PTB diagnosis, during, and after PTB treatment remains unknown. In this paper, we present a protocol for a living systematic review aimed at estimating the current burden of CPA along the continuum of PTB care.Materials and methodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines to formulate this protocol, which is registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023453900). We will identify primary literature through various electronic databases, including CINAHL, Ovid MEDLINE, MEDLINE (PubMed), EMBASE, Google Scholar, Cochrane Database of Systematic Reviews, and African Journal Online. The search will encompass articles from inception to December 31st, 2023, using medical subject heading search terms "pulmonary tuberculosis" AND "chronic pulmonary aspergillosis". Two reviewers will independently assess titles, abstracts, and full texts for eligibility using the Covidence web-based software. The eligible studies will comprise original observational research that reports on the prevalence of CPA diagnosed in individuals with PTB, based on established criteria, without language or geographic restriction. We intend to exclude single case reports and case series with fewer than 10 participants, as well as review articles, guidelines, and letters to the editors. Cochrane Risk of Bias Tools (ROB2 and ROBINS-I) will used to assess study quality and risk of bias and the quality of the evidence will be rated using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) tool. Our data syntheses will encompass meta-analysis and meta-regression, conducted using STATA version 18 and R- Studio version 4.0.2. This systematic review will be updated every 3–5 years as more data emerges.ConclusionsThe findings of this proposed systematic review will summarize the available evidence on the occurrence of CPA, at the time of PTB diagnosis, during and after PTB treatment. The study results have the potential to guide healthcare policies regarding screening for CPA, enhance clinical decision-making, and catalyse further research into understanding the interplay between PTB and CPA. By shedding light on the current burden of CPA along the continuum of PTB care, we aspire to contribute to the betterment of patient care, disease management, and global health outcomes.PROSPERO registrationCRD42023453900.

[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey. Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values. Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

Abstract copyright UK Data Service and data collection copyright owner.

UNESCO is a major collector and disseminator of statistical data on education and related subjects. Its statistical activities are aimed at providing relevant, reliable and current information for development and policy-making purposes, both at the national and international levels, and the production of reliable statistical indicators for education. These indicators cover four main areas: educational population; access and participation; the efficiency and effectiveness of education; human and financial resources.
The UNESCO Education Database covers a wide range of these areas, at four main educational levels: pre-primary, primary, secondary and tertiary, in accordance with the International Standard Classification of Education (ISCED) system. This system provides standard definitions for each of the four levels of education examined. UNESCO collects and collates education data according to these definitions from approximately 200 countries, and compiles them into the Education Database time series, which is published annually.

Main Topics:

`Educational expenditure' as presented in this data collection covers only public expenditure on public education plus subsidies for private education. This generally covers educational expenditure at every governmental level, i.e. central or federal government, and local government. Private expenditure on education and foreign aid received for education have not been included due to a lack of available data for many countries. Data are expressed in national currencies at current market prices, and data on exchange rates between national currencies and the U.S. dollar ($) and Gross National Product (GNP) are also included for reference. For most countries, single totals cover expenditure over all three ISCED (International Standard Classification of Education) levels and pre-primary education (see SNs:3697-3701), except where variable notes expressly state otherwise.
Topics covered include:
Capital expenditure; includes expenditure on land, buildings, construction, equipment and loan transactions.
Current expenditure; includes administration costs, staff costs, school books and other teaching materials, scholarships, welfare services and school building maintenance.
Total expenditure; includes capital + current expenditure.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

This dataset is published by the City of Dallas for research purposes only. The authoritative source for crime data is the Crime Analytics Dashboard.

This dataset represents the Dallas Police Public Data - RMS Incidents beginning June 1, 2014 to current-date. The Dallas Police Department strives to collect and disseminate police report information in a timely, accurate manner. This information reflects crimes as reported to the Dallas Police Department as of the current date. Crime classifications are based upon preliminary information supplied to the Dallas Police Department by the reporting parties and the preliminary classifications may be changed at a later date based upon additional investigation. Therefore, the Dallas Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information contained herein and the information should not be used for comparison purposes over time. The Dallas Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information.

This online site is an attempt to make it easier for citizens to access offense reports. In disseminating this crime information, we must also comply with current laws that regulate the release of potentially sensitive and confidential information. To ensure that privacy concerns are protected and legal standards are met, report data is "filtered" prior to being made available to the public. Among the exclusions are:

1.) Sexually oriented offenses

2.) Offenses where juveniles or children (individuals under 17 years of age) are the victim or suspect

3.) Listing of property items that are considered evidence

4.) Social Service Referral offenses

5.) Identifying vehicle information in certain offenses

Quantitative proteomics employing isobaric reagents has been established as a powerful tool for biological discovery. Current workflows often utilize a dedicated quantitative spectrum to improve quantitative accuracy and precision. A consequence of this approach is a dramatic reduction in the spectral acquisition rate, which necessitates the use of additional instrument time to achieve comprehensive proteomic depth. This work assesses the performance and benefits of online and real-time spectral identification in quantitative multiplexed workflows. A Real-Time Search (RTS) algorithm was implemented to identify fragment spectra within milliseconds as they are acquired using a probabilistic score and to trigger quantitative spectra only upon confident peptide identification. The RTS-MS3 was benchmarked against standard workflows using a complex two-proteome model of interference and a targeted 10-plex comparison of kinase abundance profiles. Applying the RTS-MS3 method provided the comprehensive characterization of a 10-plex proteome in 50% less acquisition time. These data indicate that the RTS-MS3 approach provides dramatic performance improvements for quantitative multiplexed experiments.

BackgroundDachaihu Decoction (DCD) is a traditional herbal formula widely used for treating acute pancreatitis (AP) in China. However, the efficacy and safety of DCD has never been validated, limiting its application. This study will assess the efficacy and safety of DCD for AP treatment.MethodsRelevant randomized controlled trials of DCD in treating AP will be searched through Cochrane Library, PubMed, Embase, Web of Science, Scopus, CINAHL, China National Knowledge Infrastructure database, Wanfang Database, VIP Database, and Chinese Biological Medicine Literature Service System database. Only studies published between the inception of the databases and May 31, 2023 shall be considered. Searches will also be performed in the WHO International Clinical Trials Registry Platform, Chinese Clinical Trial Registry, and ClinicalTrials.gov. Preprint databases and grey literature sources such as OpenGrey, British Library Inside, ProQuest Dissertations & Theses Global, and BIOSIS preview will also be searched for relevant resources. The primary outcomes to be assessed will include mortality rate, rate of surgical intervention, proportion of patients with severe acute pancreatitis transferred to ICU, gastrointestinal symptoms, and the acute physiology and chronic health evaluation II score. Secondary outcomes will include systemic complications, local complications, the normalization period of C-reactive protein, length of stay in the hospital, TNF-α, IL-1, IL-6, IL-8, and IL-10 levels, and adverse events. Study selection, data extraction, and assessment of bias risk will be conducted independently by two reviewers using the Endnote X9 and Microsoft Office Excel 2016 software. The risk of bias of included studies will be assessed by the Cochrane "risk of bias” tool. Data analysis will be performed using the RevMan software (V.5.3). Subgroup and sensitivity analysis will be performed where necessary.ResultsThis study will provide high-quality current evidence of DCD for treating AP.ConclusionThis systematic review will provide evidence of whether DCD is an effective and safe therapy for treating AP.Trial registrationPROSPERO registration numberCRD42021245735. The protocol for this study was registered at PROSPERO, and is available in the S1 Appendix. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021245735.

Accelerometry reporting using a tool from Montoye et al. 13.