The POLitical Event Classification, Attributes, and Types (POLECAT) dataset is the successor event dataset to the Integrated Crisis Early Warning System (ICEWS). Event data consists of coded interactions between socio-political actors (i.e., cooperative or hostile actions between individuals, groups, sectors, and nation states). POLECAT uses a newly developed, machine-learning coder based on the Political Language Ontology for Verifiable Event Records (PLOVER) ontology to produce an event database by analyzing news stories in seven languages from over one-thousand sources across the globe. The database includes millions of time-stamped, geolocated events from 2018 onward, is updated hourly, and posted weekly. Each event includes several attributes, as defined in the included POLECAT Dictionary and PLOVER Manual. Please see the companion POLECAT Dataverse page at doi.org/10.7910/DVN/LMFPIP for associated documentation.

Dataset Card for dwb2023/gdelt-mentions-2025-v3

This dataset contains the mentions records from the GDELT (Global Database of Events, Language, and Tone) Project, tracking how global events are mentioned across media sources over time.

  Dataset Details





  Dataset Description

The GDELT Mentions table is a component of the GDELT Event Database that tracks each mention of an event across all monitored news sources. Unlike the Event table which records unique events… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/gdelt-mentions-2025-v3.

PredictLeads News Events Data provides sales professionals with valuable insights into corporate developments, helping optimize B2B outreach and sales strategies. With over 7.9 million news events detected across 2 million websites since 2016, this dataset delivers intelligence on business changes, including funding rounds, acquisitions, expansions, and leadership shifts.

Use Cases: ✅ Cold Outreach – Leverage news on company hires or leadership changes to craft timely, personalized outreach. ✅ B2B Data Enrichment – Update CRM records with corporate events like funding rounds, expansions, or product launches. ✅ Account Scoring – Integrate event-based insights to rank leads based on their growth and activity. ✅ Sales Strategy Optimization – Adjust sales messaging based on a company’s latest announcements. ✅ Company Data Enrichment – Gain a deeper understanding of company activities to improve prospecting.

Key API Attributes:

• id (string, UUID) – Unique identifier for the news event.
• summary (string) – Brief description of the event.
• category (string) – Type of event (e.g., "expands_offices_in", "funding").
• found_at (ISO 8601 date-time) – Timestamp of when the event was detected.
• confidence (float) – Probability score for event accuracy.
• article_sentence (string) – Extracted sentence containing the event.
• location (string, nullable) – Country or region related to the event.
• company1 (object) – Company associated with the event, including domain and name.
• most_relevant_source (object) – News article providing event details, including URL and publication date.

📌 Trusted by sales and marketing teams to turn business insights into actionable sales opportunities.

API example: https://docs.predictleads.com/v3/api_endpoints/news_events_dataset

The Cline Center Global News Index is a searchable database of textual features extracted from millions of news stories, specifically designed to provide comprehensive coverage of events around the world. In addition to searching documents for keywords, users can query metadata and features such as named entities extracted using Natural Language Processing (NLP) methods and variables that measure sentiment and emotional valence. Archer is a web application purpose-built by the Cline Center to enable researchers to access data from the Global News Index. Archer provides a user-friendly interface for querying the Global News Index (with the back-end indexing still handled by Solr). By default, queries are built using icons and drop-down menus. More technically-savvy users can use Lucene/Solr query syntax via a ‘raw query’ option. Archer allows users to save and iterate on their queries, and to visualize faceted query results, which can be helpful for users as they refine their queries. Additional Resources: - Access to Archer and the Global News Index is limited to account-holders. If you are interested in signing up for an account, please fill out the Archer Access Request Form so we can determine if you are eligible for access or not. - Current users who would like to provide feedback, such as reporting a bug or requesting a feature, can fill out the Archer User Feedback Form. - The Cline Center sends out periodic email newsletters to the Archer Users Group. Please fill out this form to subscribe to it. Citation Guidelines: 1) To cite the GNI codebook (or any other documentation associated with the Global News Index and Archer) please use the following citation: Cline Center for Advanced Social Research. 2025. Global News Index and Extracted Features Repository [codebook], v1.3.0. Champaign, IL: University of Illinois. June. XX. doi:10.13012/B2IDB-5649852_V6 2) To cite data from the Global News Index (accessed via Archer or otherwise) please use the following citation (filling in the correct date of access): Cline Center for Advanced Social Research. 2025. Global News Index and Extracted Features Repository [database], v1.3.0. Champaign, IL: University of Illinois. Jun. XX. Accessed Month, DD, YYYY. doi:10.13012/B2IDB-5649852_V6 *NOTE: V6 is replacing V5 with updated ‘Archer’ documents to reflect changes made to the Archer system.

PredictLeads News Events Data offers a dynamic and comprehensive dataset tailored for sales professionals looking to enhance their B2B outreach and sales strategies. With over 7.5 million news events detected since 2016 across 2 million websites, our data provides invaluable insights into significant corporate activities that can significantly influence B2B sales processes.

Key Features for Sales Enhancement:

➡️Extensive Coverage: Access a wide array of news events from 2 million websites, offering deep insights into both large and emerging markets. Diverse Event Categories: From hiring and promotions to mergers and acquisitions, our dataset includes various triggers that can inform and refine sales strategies. ➡️Real-Time Updates: Stay ahead with the latest developments, as our data captures the most recent and relevant corporate changes and announcements.

Improving B2B Sales with PredictLeads News Events Data:

➡️Cold Outreach: Utilize real-time data on company hires or leadership changes to tailor your outreach, ensuring messages are relevant and timely, thus increasing the chances of engagement. ➡️B2B Data Enrichment: Enrich your CRM with up-to-date details on potential and existing clients, including expansions, new launches, or financial moves, to better understand your target accounts. ➡️Account Scoring: Enhance lead scoring models by integrating news events like funding, expansions, or product launches, indicating a company’s growth trajectory and propensity to buy. ➡️Sales Strategy Optimization: Adjust and personalize your sales approaches based on detailed insights into a company’s latest activities, such as partnerships or market expansions, to better align with their current needs and challenges. ➡️Company Data Enrichment: Deepen company profiles in your database with structured data on significant events, providing your sales teams with a richer context for each prospect.

Why PredictLeads News Events Data Stands Out for B2B Sales:

✅Tailored Insights: Our dataset is designed not just to inform but to transform how sales teams engage with prospects, providing targeted insights that drive meaningful conversations and conversions. ✅Strategic Advantage: By understanding when and where significant corporate events occur, sales professionals can anticipate market trends and customer needs, positioning themselves as valuable partners. ✅Trusted Data Source: Leading companies rely on PredictLeads for accurate and actionable data, trusting our comprehensive approach to data collection and categorization.

Leverage PredictLeads News Events Data to supercharge your sales efforts, utilizing our detailed corporate events data to gain a competitive edge in your outreach and sales strategies.

The Global Terrorism Database (GTD) is an open-source database including information on terrorist events around the world from 1970 through 2017 (with additional annual updates planned for the future). Unlike many other event databases, the GTD includes systematic data on domestic as well as transnational and international terrorist incidents that have occurred during this time period and now includes more than 180,000 cases. For each GTD incident, information is available on the date and location of the incident, the weapons used and nature of the target, the number of casualties, and--when identifiable--the group or individual responsible.Statistical information contained in the Global Terrorism Database is based on reports from a variety of open media sources. Information is not added to the GTD unless and until we have determined the sources are credible. Users should not infer any additional actions or results beyond what is presented in a GTD entry and specifically, users should not infer an individual associated with a particular incident was tried and convicted of terrorism or any other criminal offense. If new documentation about an event becomes available, an entry may be modified, as necessary and appropriate.The National Consortium for the Study of Terrorism and Responses to Terrorism (START) makes the GTD available via this online interface in an effort to increase understanding of terrorist violence so that it can be more readily studied and defeated.Characteristics of the GTDContains information on over 180,000 terrorist attacksCurrently the most comprehensive unclassified database on terrorist attacks in the worldIncludes information on more than 88,000 bombings, 19,000 assassinations, and 11,000 kidnappings since 1970Includes information on at least 45 variables for each case, with more recent incidents including information on more than 120 variablesMore than 4,000,000 news articles and 25,000 news sources were reviewed to collect incident data from 1998 to 2017 aloneGovernment representatives and interested analysts may download the data directly through this form.

The Economic Indictor Service (EIS) aims to deliver professional economic content to financial institutions on both the buy and sell side service providers. This service covers 136 countries and 43,000 recurring indicators, which are updated on a real-time basis.

We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. In addition, it provided data of over 1,700 non-recurring indicators in 2020.

The EIS service includes historic data on recurring economic indicators. Recurring events include GDP data, unemployment releases, PMI numbers etc. Information on economic indicators, includes details of issuing agency and historical data series is made available depending on its availability.

The two components available for the Economic Calendar are the following:

1. Live Calendar - updated 24/5 immediately after the data is released and with limited history for recurring indicators.

2. Historical Database - Database of all recurring indicators (with complete history) and non-recurring indicators

Live Calendar can be embedded on client's website using iFrame or API. Historical Database can be made available via API or FTP.

Additional Features of the Economic Indicator Service - Delivery of unique newsfeed by using algorithms and analysts - Feed to client’s website with customized branding - Automatic feed to social media accounts, such as: Twitter and Facebook - Desktop ticker updates - Mobile App integration - Bespoke dashboards for macro-economic & industry reports And most importantly, clients can customize filters to get the specific economic indicators (e.g. for specific countries) they need.

A good retail broker can gain advantage by minimizing the time lag in real time information flow to retail investors vis-à-vis institutional investors. One way to achieve this is by providing access to clients with timely and accurate access to all major economic and other market moving announcements / data. - In order to minimize this disadvantage, many broker dealers provide economic calendar and news flows on their trading platforms. - We have developed two distinct products – Economic Calendar and Economic News to meet this requirement.

Contact Ilze Gouws, i.gouws@africadata.com for more information.

Structured summaries of material news and events - more than two million key developments sourced from over 20,000 news sources including press releases, regulatory filings, company web sites, web mining, and call transcripts.

The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries between 1870 and 2020. The data was collected by extensive data-collection from more than 800 sources ranging from news reports through government databases to scientific papers. The dataset includes 2521 events characterized by at least one impact statistic: area inundated, fatalities, persons affected or economic loss. Economic losses are presented both in the original currencies and price levels as well as inflation and exchange-rate adjusted to 2020 value of the euro. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Daily start and end dates, information on causes of the event, notes on data quality issues or associated non-flood impacts, and full bibliography of each record supplement the dataset. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset is designed to be complimentary to HANZE-Exposure, a high-resolution model of historical exposure changes (such as population and asset value), and be easily usable in statistical and spatial analyses.

The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders)

HANZE flood events database

HANZE_events.csv - Flood event data

HANZE_references.csv - List of all references

HANZE_events_regions_2010.zip - Flood event data as GIS file (regions v2010)

HANZE_events_regions_2021.zip - Flood event data as GIS file (regions v2021)

Supplementary data

S1_countries_codes_and_names.csv - Country codes/names

S2_regions_codes_and_names_v2010.csv - Region codes/names, v2010

S3_regions_codes_and_names_v2021.csv - Region codes/names, v2021

S4_list_of_all_currencies_by_country.csv - Data on all currencies used in the study area since 1870

S5_currency_conversion_rates.csv - Conversion rates applied to compute losses in 2020 euros

S6_GDP_deflators_by_country.csv - Gross domestic product deflator by country, 1870-2020

S7_floods_removed_from_HANZE.csv - Flood events in HANZE v1, which were excluded from v2

Regions_v2010_simplified.zip - Map of subnational regions used in the database, v2010

Regions_v2021_simplified.zip - Map of subnational regions used in the database, v2021

Note: this is a minor update of the original upload. It corrects the erroneous rendering of NUTS regions for event 2751, fixes some geometry problems with the GIS files and makes some small changes to the flood data (2 events were added and the regional codes for Kosovo in version 2021 were modified based on the upcoming NUTS 2024 classification).

Dataset Card for dwb2023/gdelt-event-2025-v3

This dataset contains global event records from the GDELT (Global Database of Events, Language, and Tone) Project, capturing real-world events and their characteristics across the globe through news media coverage.

  Dataset Details





  Dataset Description

The GDELT Event Database is a comprehensive repository of human societal-scale behavior and beliefs across all countries of the world, connecting every person… See the full description on the dataset page: https://huggingface.co/datasets/dwb2023/gdelt-event-2025-v3.

Find unrivaled company, commodity and economic stories formatted for automated consumption, with LSEG Real-Time News, powered by Reuters.

The Significant Earthquake Database is a global listing of over 5,700 earthquakes from 2150 BC to the present. A significant earthquake is classified as one that meets at least one of the following criteria: caused deaths, caused moderate damage (approximately $1 million or more), magnitude 7.5 or greater, Modified Mercalli Intensity (MMI) X or greater, or the earthquake generated a tsunami. The database provides information on the date and time of occurrence, latitude and longitude, focal depth, magnitude, maximum MMI intensity, and socio-economic data such as the total number of casualties, injuries, houses destroyed, and houses damaged, and $ dollage damage estimates. References, political geography, and additional comments are also provided for each earthquake. If the earthquake was associated with a tsunami or volcanic eruption, it is flagged and linked to the related tsunami event or significant volcanic eruption.

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

Sample size and counts of siblings in Iraq, by governorate.

This dataset is the news articles scraped from New York Times, CNN, Business Insider and Breitbart. The original dataset published in Kaggle did not provide any human summaries, it only offered the title of the article, while this could be used as the summary, it is not ideal as the headline title was too short. We generated the label manually by adding the human summary for the available articles. We also added another column called theme to the dataset, this column would state the genre of the news articles.

The dataset is ideal for summarization as the provided news articles are long and will consume lots of time to read it. Therefore, it is ideal to generate automatic summarization for the articles in the dataset. The dataset consists of 50,001 rows of data.

This layer features tropical storm (hurricanes, typhoons, cyclones) tracks, positions, and observed wind swaths from the past hurricane season for the Atlantic, Pacific, and Indian Basins. These are products from the National Hurricane Center (NHC) and Joint Typhoon Warning Center (JTWC). They are part of an archive of tropical storm data maintained in the International Best Track Archive for Climate Stewardship (IBTrACS) database by the NOAA National Centers for Environmental Information.Data SourceNOAA National Hurricane Center tropical cyclone best track archive.Update FrequencyWe automatically check these products for updates every 15 minutes from the NHC GIS Data page.The NHC shapefiles are parsed using the Aggregated Live Feeds methodology to take the returned information and serve the data through ArcGIS Server as a map service.Area CoveredWorldWhat can you do with this layer?Customize the display of each attribute by using the ‘Change Style’ option for any layer.Run a filter to query the layer and display only specific types of storms or areas.Add to your map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools like ‘Enrich Data’ on the Observed Wind Swath layer to determine the impact of cyclone events on populations.Visualize data in ArcGIS Insights or Operations Dashboards.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency. Always refer to NOAA or JTWC sources for official guidance.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The International Terrorism: Attributes of Terrorist Events (ITERATE) numeric database is based on international terrorism event chronologies represented in major media, research, news, and information services. ITERATE consists of four interrelated files. The Common File codifies terrorist activities by venue, type of incident, originating groups and their affiliations, characteristics of victims, and fatalities. The Fate of Terrorists File describes terrorist participants, their nationalities, and post-event outcomes. The Hostage File contains information on hostage events, demands and ransom, terrorist and negotiator behaviors, characteristics of negotiations, length and outcome of events, and nationalities involved. The Skyjack File includes information on aircraft and airlines involved, locations, duration and outcome of incidents, and number of victims.

Abstract copyright UK Data Service and data collection copyright owner.

Drawing together the results of research in written and printed records, iconographical analysis and anthropological fieldwork, Imagining Belfast attempts a comprehensive review of identity practices in Belfast from the nineteenth to the twentieth century, focussing in particular on the use of public space. Two data bases were constructed. The first covers crowd events in Belfast in the period 1850-1960. A database has been constructed from the files of the Belfast News Letter comprising all recorded events involving the use of public space for every eleventh year across the period. Events are catalogued by type, size of crowd, location, level of organisation, and the incidence of violence. The second, constructed from the Belfast News Letter and Irish News, lists all recorded events involving the use of public space for every four years across the period from 1961-2005.

Main Topics:

The aim of the project is to explore the formation and public expression of identity in Belfast by means of a long term historical study combined with an anthropological investigation of recent developments. By exploring the variety of urban identities that have found expression across this period it will challenge conventional perceptions of Northern Ireland society as dominated by two monolithic and inflexible 'traditions'. At the same time it will try to move beyond simplistic debates on the authenticity of particular cultural forms to an understanding of the way in which historically based identities can be both sustained over time and redefined in response to changing circumstances.

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.

This dataset contains Crime and Safety data from the Cary Police Department.

This data is extracted by the Town of Cary's Police Department's RMS application. The police incidents will provide data on the Part I crimes of arson, motor vehicle thefts, larcenies, burglaries, aggravated assaults, robberies and homicides. Sexual assaults and crimes involving juveniles will not appear to help protect the identities of victims.

This dataset includes criminal offenses in the Town of Cary for the previous 10 calendar years plus the current year. The data is based on the National Incident Based Reporting System (NIBRS) which includes all victims of person crimes and all crimes within an incident. The data is dynamic, which allows for additions, deletions and/or modifications at any time, resulting in more accurate information in the database. Due to continuous data entry, the number of records in subsequent extractions are subject to change. Crime data is updated daily however, incidents may be up to three days old before they first appear.

About Crime Data

The Cary Police Department strives to make crime data as accurate as possible, but there is no avoiding the introduction of errors into this process, which relies on data furnished by many people and that cannot always be verified. Data on this site are updated daily, adding new incidents and updating existing data with information gathered through the investigative process.

This dynamic nature of crime data means that content provided here today will probably differ from content provided a week from now. Additional, content provided on this site may differ somewhat from crime statistics published elsewhere by other media outlets, even though they draw from the same database.

Withheld Data

In accordance with legal restrictions against identifying sexual assault and child abuse victims and juvenile perpetrators, victims, and witnesses of certain crimes, this site includes the following precautionary measures: (a) Addresses of sexual assaults are not included. (b) Child abuse cases, and other crimes which by their nature involve juveniles, or which the reports indicate involve juveniles as victims, suspects, or witnesses, are not reported at all.

Certain crimes that are under current investigation may be omitted from the results in avoid comprising the investigative process.

Incidents five days old or newer may not be included until the internal audit process has been completed.

This data is updated daily.