Update

Unfortunately, the API this dataset used to pull the stock data isn't free anymore. Instead of having this auto-updating, I dropped the last version of the data files in here, so at least the historic data is still usable.

Content

This dataset provides free end of day data for all stocks currently in the Dow Jones Industrial Average. For each of the 30 components of the index, there is one CSV file named by the stock's symbol (e.g. AAPL for Apple). Each file provides historically adjusted market-wide data (daily, max. 5 years back). See here for description of the columns: https://iextrading.com/developer/docs/#chart

Since this dataset uses remote URLs as files, it is automatically updated daily by the Kaggle platform and automatically represents the latest data.

Acknowledgements

List of stocks and symbols as per https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average

Thanks to https://iextrading.com for providing this data for free!

Terms of Use

Data provided for free by IEX. View IEX’s Terms of Use.

Cbonds collects and normalizes indices data, offering daily updated and historical data on over 40,000 indices, including macroeconomic indicators, yield curves and spreads, currency markets, stock and funds markets, and commodities. Using the Indices API, you can access an index's holdings, such as its assets, sectors, and weight, as well as basic data on the asset. You can obtain end-of-day, and historical API indicator prices in CSV, XLS, and JSON formats. Cbonds provides a free Indices API for a limited test period of two weeks or for a longer period with a limited number of instruments.

The best choice for those looking for license-free US market data for commercial use is US Equities Basic, which includes data display, redistribution, professional trading, and more.

US Equities Basic is based upon a derived IEX feed. The volume coverage is 3-5% of the total trading volume in North America, which helps entities mitigate license expenses and start with real-time data.

US Equities Basic provides raw quotes, trades, aggregated time series (OHLCV), and snapshots. Both REST API and WebSocket API are available.

End-of-day price information disseminated after 12:00 AM EST does not require licensing in the United States by law. This applies to all exchanges, even those not included in the US Equities Basic. Finazon combines all price information after every trading day, meaning that while markets are open, real-time prices are available from a subset of exchanges, and when markets close, data is synced and contains 100% of US volume. All historical prices are adjusted for corporate actions and splits.

Tip: Individuals with non-professional usage are not required to get exchange licenses for real-time data and, hence, are better off with the US Equities Max dataset.

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

The IvyDB Signed Volume dataset, available as an add-on product for IvyDB US, contains daily data on detailed option trading volume. Trades in the IvyDB US dataset are assigned as either buyer-initiated or seller-initiated based on the trade price and the bid-ask quote at the time of the trade. The total assigned daily volume is aggregated and updated nightly.

Access LSEG's London Stock Exchange (LSE) Market Data, and find benchmarks, indices, and real-time and historic market information.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Argus is a prominent source of pricing evaluations and business insights extensively utilized in the energy and commodity sectors, specifically for physical supply agreements and the settlement and clearing of financial derivatives. Argus pricing is also employed as a benchmark in swaps markets, for mark-to-market valuations, project financing, taxation, royalties, and risk management. Argus provides comprehensive services globally and continuously develops new assessments to mirror evolving market dynamics and trends. Covered assets encompass Energy, Oil, Refined Products, Power, Gas, Generation fuels, Petrochemicals, Transport, and Metals.

Extensive and dependable pricing information spanning the entire range of financial markets. Encompassing worldwide coverage from stock exchanges, trading platforms, indicative contributed prices, assessed valuations, expert third-party sources, and our enhanced data offerings. User-friendly request-response, bulk access, and tailored desktop interfaces to meet nearly any organizational or application data need. Worldwide, real-time, delayed streaming, intraday updates, and meticulously curated end-of-day pricing information.

This dataset contains historical Bitcoin (BTC/USDT) price data from Binance exchange with the following specifications:

Timezone Information: - All timestamps are in UTC (Coordinated Universal Time) - Open time format: YYYY-MM-DD HH:MM:SS.ffffff UTC - Close time format: YYYY-MM-DD HH:MM:SS.ffffff UTC

Daily Timeframe Specific: - Open time: Always shows 00:00:00.000000 UTC (start of day) - Close time: Always shows 23:59:59.999000 UTC (end of day)

Timeframes Available: - 15-minute intervals (15m) - 1-hour intervals (1h) - 4-hour intervals (4h) - 1-day intervals (1d)

Data Columns: - Open time: Opening timestamp in UTC - Open: Opening price - High: Highest price during period - Low: Lowest price during period - Close: Closing price - Volume: Trading volume - Close time: Closing timestamp in UTC - Quote asset volume: Volume in quote asset (USDT) - Number of trades: Number of trades during period - Taker buy base asset volume: Volume of taker buy orders - Taker buy quote asset volume: Volume of taker buy orders in quote asset - Ignore: Unused field

Data is automatically updated and maintained through automated scripts.

Our collection of data is multi-sourced and go through a rigorous scrubbing, cleaning and validation process before it is normalized and distributed on to our clients. By using the most recent technology we are able to automate much of our processes to make sure to meet our clients’ deadlines.

The experienced Stamdata team of analysts, with long history of working with Nordic issuers, can help support our clients in detailed queries if the need arises.

​Ensure you have an automated and streamlined process in place fed with the highest quality corporate actions data for Nordic fixed income securities. Come speak to us at Stamdata to learn more about how we can help you.

​Examples of the data we provide:

​Instrument reference data ​Daily validated and updated instrument reference data like ISIN, ticker, region/sector/industry class, security type, risk type, interest type, reference rate, calculated interest, coupon, day-count conventions, program type, oustanding/face/principal amounts, and more.

​Corporate actions data ​Corporate actions are complex to manage and often require manual interventions which can increase your operational costs, risks, and cause you reputational damage. We can help you get on top of corporate action events occuring in the Nordic fixed income market:

Sign up to receive intra or end-of-day updated corporate actions data for Nordic fixed income securities. We source our data from multiple sources, preferrably directly from the issuing banks, which we have a history of long and close relationships with.

Our analyst team consisting of Nordic fixed income market experts make sure the data is cleansed, validated and normalized before provided on to our clients.

Corporate action events can have many downstream effects, such as impact on trading limits and regulatory compliance reporting. A few examples of key events we can help provide you data for are:

• Outstanding amount changes
• Name changes
• Taps / redemptions
• Calls
• Defaults
• Amendments to bond terms ​- ESG data

Analyze and research the Nordic market for sustainable bonds using our green bond indicator and statistics database. Make easy and quick adhoc filtered searches using our website login at stamdata.com, or subscribe to our direct feed or API-delivery to automate your workflow.

​Documentation ​Review and download a bond loan’s full terms & conditions documentation, available on each instrument, in our database. Find out which covenant types are most frequently used, which key metrics issuers have to report on and how frequently, security package structures for secured bonds, if there are inter-creditor agreements in place, and much more.

Delivery options:

Stamdata web Stamdata.com is our most flexible and easy to use portal service for individual use. Use it to dig deeper into our broad database containing reference data on over 45,000 instruments.

Use the Statistics module to analyze the market based on your own parameters or our pre-defined parameters with easy exportable-to-Excel functionality.

Use our Deal Monitor to stay updated with the most recently announced bond issues in the Nordic market.

Track arrangers and leading banks activity and performance in the different types of market segments, sectors, currencies, etc.

Feed or API We provide multiple options for download automation, such as:

• Feed (sftp): daily file delivery, intra- or at end-of-day, delta or full universe update. Custom delivery times can be setup if required.
• REST-API: easy to use and connect to for instant download of data. Custom setups: Our team of analysts sit on many years of experience working with market data and consultancy. The team can help tailor files and file deliveries to suit you specific requirements, independent on your profile (client or partner).

EDI's history of corporate action events dates back to January 2007 and uses unique Security IDs that can track the history of events by issuer since January 2007.

Choose to receive accurate corporate actions data via an SFTP connection either 4x daily or end-of-day. Proprietary format. ISO 15022 message standard, providing MT564 & 568 announcements.

Lucror Analytics: Proprietary Hedge Funds Data for Credit Quality & Bond Valuation

At Lucror Analytics, we provide cutting-edge corporate data solutions tailored to fixed income professionals and organizations in the financial sector. Our datasets encompass issuer and issue-level credit quality, bond fair value metrics, and proprietary scores designed to offer nuanced, actionable insights into global bond markets that help you stay ahead of the curve. Covering over 3,300 global issuers and over 80,000 bonds, we empower our clients to make data-driven decisions with confidence and precision.

By leveraging our proprietary C-Score, V-Score , and V-Score I models, which utilize CDS and OAS data, we provide unparalleled granularity in credit analysis and valuation. Whether you are a portfolio manager, credit analyst, or institutional investor, Lucror’s data solutions deliver actionable insights to enhance strategies, identify mispricing opportunities, and assess market trends.

What Makes Lucror’s Hedge Funds Data Unique?

Proprietary Credit and Valuation Models Our proprietary C-Score, V-Score, and V-Score I are designed to provide a deeper understanding of credit quality and bond valuation:

C-Score: A composite score (0-100) reflecting an issuer's credit quality based on market pricing signals such as CDS spreads. Responsive to near-real-time market changes, the C-Score offers granular differentiation within and across credit rating categories, helping investors identify mispricing opportunities.

V-Score: Measures the deviation of an issue’s option-adjusted spread (OAS) from the market fair value, indicating whether a bond is overvalued or undervalued relative to the market.

V-Score I: Similar to the V-Score but benchmarked against industry-specific fair value OAS, offering insights into relative valuation within an industry context.

Comprehensive Global Coverage Our datasets cover over 3,300 issuers and 80,000 bonds across global markets, ensuring 90%+ overlap with prominent IG and HY benchmark indices. This extensive coverage provides valuable insights into issuers across sectors and geographies, enabling users to analyze issuer and market dynamics comprehensively.

Data Customization and Flexibility We recognize that different users have unique requirements. Lucror Analytics offers tailored datasets delivered in customizable formats, frequencies, and levels of granularity, ensuring that our data integrates seamlessly into your workflows.

High-Frequency, High-Quality Data Our C-Score, V-Score, and V-Score I models and metrics are updated daily using end-of-day (EOD) data from S&P. This ensures that users have access to current and accurate information, empowering timely and informed decision-making.

How Is the Data Sourced? Lucror Analytics employs a rigorous methodology to source, structure, transform and process data, ensuring reliability and actionable insights:

Proprietary Models: Our scores are derived from proprietary quant algorithms based on CDS spreads, OAS, and other issuer and bond data.

Global Data Partnerships: Our collaborations with S&P and other reputable data providers ensure comprehensive and accurate datasets.

Data Cleaning and Structuring: Advanced processes ensure data integrity, transforming raw inputs into actionable insights.

Primary Use Cases

1. Portfolio Construction & Rebalancing Lucror’s C-Score provides a granular view of issuer credit quality, allowing portfolio managers to evaluate risks and identify mispricing opportunities. With CDS-driven insights and daily updates, clients can incorporate near-real-time issuer/bond movements into their credit assessments.

2. Portfolio Optimization The V-Score and V-Score I allow portfolio managers to identify undervalued or overvalued bonds, supporting strategies that optimize returns relative to credit risk. By benchmarking valuations against market and industry standards, users can uncover potential mean-reversion opportunities and enhance portfolio performance.

3. Risk Management With data updated daily, Lucror’s models provide dynamic insights into market risks. Organizations can use this data to monitor shifts in credit quality, assess valuation anomalies, and adjust exposure proactively.

4. Strategic Decision-Making Our comprehensive datasets enable financial institutions to make informed strategic decisions. Whether it’s assessing the fair value of bonds, analyzing industry-specific credit spreads, or understanding broader market trends, Lucror’s data delivers the depth and accuracy required for success.

Why Choose Lucror Analytics for Hedge Funds Data? Lucror Analytics is committed to providing high-quality, actionable data solutions tailored to the evolving needs of the financial sector. Our unique combination of proprietary models, rigorous sourcing of high-quality data, and customizable delivery ensures that users have the insights they need to make smarter dec...

Approximately 20% of Winnipeg Transit buses are equipped with automated passenger counting sensors at each door that count the number of people entering (boarding) and exiting (alighting) the bus along with the relevant bus stop information.

This data is extrapolated into an estimate of the average daily boardings and alightings, aggregated by route, stop, and time of day, for each day type (weekday, Saturday, Sunday). Data is collected over time ranges corresponding to regular seasonal schedules (September-December, December-April, April-June, June-September), and will be uploaded within 30 days after the end of each seasonal schedule.

The time of day field for weekdays is defined as AM Peak (05:00-09:00), Mid-Day (09:00-15:30), PM Peak (15:30-18:30), Evening (18:30-22:30), and Night (22:30-end of service). For Saturdays and Sundays, it is defined as Morning (05:00-11:00), Afternoon (11:00-19:00), Evening (19:00-22:30), and Night (22:30-end of service).

Due to detection errors and small sample sizes in some cases, boarding numbers may not exactly match alighting numbers. On-request passenger counts are not included in this data set.

More transit data can be found on Winnipeg Transit's Open Data Web Service, located here: https://api.winnipegtransit.com/home/api/v3

Lucror Analytics: Proprietary Risk Modelling Data for Credit Quality & Bond Valuation

At Lucror Analytics, we provide cutting-edge corporate data solutions tailored to fixed income professionals and organizations implementing quant-driven strategies. Our risk modelling data encompasses issuer and issue-level credit quality, bond fair value metrics, and proprietary scores designed to offer nuanced, actionable insights into global bond markets that help you stay ahead of the curve. Covering over 3,300 global issuers and over 80,000 bonds, we empower our clients with robust risk modelling data to make data-driven decisions with confidence and precision.

By leveraging our proprietary C-Score, V-Score, and V-Score I models, which utilize CDS and OAS data, we provide unparalleled granularity in credit analysis and valuation. Whether you are a portfolio manager, credit analyst, or institutional investor, Lucror’s risk modelling data solutions deliver actionable insights to enhance strategies, identify mispricing opportunities, and assess credit risk.

What Makes Lucror’s Risk Modelling Data Unique?

Proprietary Credit and Valuation Models Developed for Risk Modelling Our proprietary C-Score, V-Score, and V-Score I are designed to provide a deeper understanding of credit quality and bond valuation:

C-Score: A composite score (0-100) reflecting an issuer's credit quality based on market pricing signals such as CDS spreads. Responsive to near-real-time market changes, the C-Score offers granular differentiation within and across credit rating categories, helping investors identify mispricing opportunities.

V-Score: Measures the deviation of an issue’s option-adjusted spread (OAS) from the market fair value, indicating whether a bond is overvalued or undervalued relative to the market.

V-Score I: Similar to the V-Score but benchmarked against industry-specific fair value OAS, offering insights into relative valuation within an industry context.

These models provide foundational risk modelling data for fixed income strategies aimed at outperforming benchmarks.

Comprehensive Global Coverage Our risk modelling data covers over 3,300 issuers and 80,000 bonds across global markets, ensuring 90%+ overlap with prominent IG and HY benchmark indices. This extensive coverage provides valuable insights into issuers across sectors and geographies, enabling users to analyze issuer credit risk and market dynamics comprehensively.

Risk Modelling Data Customization and Flexibility We recognize that different users have unique requirements. Lucror Analytics offers tailored datasets delivered in customizable formats, frequencies, and levels of granularity, ensuring that our risk modelling data integrates seamlessly into your workflows.

High-Frequency, High-Quality Risk Modeling Data Our C-Score, V-Score, and V-Score I models and metrics are updated daily using end-of-day (EOD) data from S&P. This ensures that users have access to current and accurate risk modelling data, empowering timely and informed decision-making.

How Is the Risk Modelling Data for Sourced? Lucror Analytics employs a rigorous methodology to source, structure, transform and process data, ensuring reliability and actionable insights:

Proprietary Quantitative Risk Models: Our scores are derived from proprietary quant algorithms based on CDS spreads, OAS, and other issuer and bond data.

Global Data Partnerships: Our collaborations with S&P and other reputable data providers ensure comprehensive and accurate risk modelling datasets.

Cleaning and Structuring of Risk Modelling Data: Advanced processes ensure data integrity, transforming raw inputs into actionable credit risk insights.

Primary Use Cases

1. Risk Management Updated daily, Lucror’s risk modelling data provides dynamic insights into market and credit risks. Organizations can use this data to monitor shifts in credit quality, assess valuation anomalies, and adjust exposure proactively.

2. Quant-driven Portfolio Construction & Rebalancing Lucror’s C-Score provides a granular view of issuer credit quality, allowing portfolio managers to evaluate risks and identify mispricing opportunities. With CDS-driven insights and daily updates, clients can incorporate near-real-time issuer/bond movements into their credit assessments using risk modelling data.

3. Portfolio Optimization The V-Score and V-Score I allow portfolio managers to identify undervalued or overvalued bonds, supporting strategies that optimize returns relative to credit risk. By benchmarking valuations against market and industry standards, users can uncover potential mean-reversion opportunities and enhance portfolio performance with risk modelling data.

4. Strategic Decision-Making Our comprehensive risk modelling data enables financial institutions to make informed strategic decisions. Whether it’s assessing the fair value of bonds, analyzing industry-specific credit risk, or underst...

Vaisala CL51 ceilometer. Accurately measures ceiling or base height of cloud layers using pulsed diode lidar technology and single lens optics.

The CL51 model is designed for high-range, cirrus cloud height profiling that also includes detailed data on low and middle layer clouds as well as vertical visibility. It has a detection range up to 15 km.

This instrument is installed at the Trollhaugen Observatory at Troll Station in Dronning Maud Land Antarctica.

The instrument is at an elevation of 1560 m above sea level, 300 m above the Troll Airfield.

The images are plots from Vaisala's BLView software, with colours showing backscatter intensity, and dots indicating cloud base heights, covering a 24-hour period. See plot description from Vaisala. Heights in the plots (y-axis) are above the instrument, not relative to sea level or the airfield. Time in the plots (x-axis) is in UTC, ending on the date given at the top of the plot (plots that start/end at 00:00 show the date of the day just starting at the end of the plot, so a plot showing data from 00:00 to 00:00 and dated 01 desember shows data from 30 November at 00:00 UTC until 1 December at 00:00 UTC).

This dataset is a merged and unified one from seven individual datasets, making it the longest records ever and wide coverage in the US for flood studies. All individual databases and a unified database are provided to accommodate different user needs. It is anticipated that this database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, climatic studies concerning spatiotemporal patterns of floods given this long-term and U.S.-wide coverage, and flood susceptibility analysis for vulnerable geophysical locations.

Description of filenames:

1. cyberFlood_1104.csv – web-based crowdsourced flood database, developed at the University of Oklahoma (Wan et al., 2014). 203 flood events from 1998 to 2008 are retrieved with the latest version. Data accessed on 11/04/2020.

Data attributes: ID, Year, Month, Day, Duration, fatality, Severity, Cause, Lat, Long, Country Code, Continent Code

2. DFO.xlsx – the Dartmouth Flood Observatory flood database. It is a tabular form of global flood database, collected from news, government agencies, stream gauges, and remote sensing instruments from 1985 to the present. Data accessed on 10/27/2020.

Data attributes: ID, GlodeNumber, Country, OtherCountry, long, lat, Area, Began, Ended, Validation, Dead, Displaced, MainCause, Severity

3. emdat_public_2020_11_01_query_uid-MSWGVQ.xlsx – Emergency Events Database (EM-DAT). This flood report is managed by the Centre for Research on the Epidemiology of Disasters in Belgium, which contains all types of global natural disasters from 1900 to the present. Data accessed on 11/01/2020.

Data attributes: Dis No, Year, Seq, Disaster Group, Disaster Subgroup, Disaster Type, Disaster Subtype, Disaster Subsubtype, Event Nane, Entity Criteria, Country, ISO, Region, Continent, Location, Origin, Associated Disaster, Associated Disaster2, OFDA Response, Appeal, Declaration, Aid Contribution, Disaster Magnitude, Latitude, Longitude, Local Time, River Basin, Start Year, Start Month, Start Day, End Year, End Month, End Day, Total Death, No. Injured, No. Affected, No. Homeless, Total Affected, Reconstruction, Insured Damages, Total Damages, CPI

4. extracted_events_NOAA.csv – The national weather service storm reports. The NOAA NWS team collects weather-related natural hazards from 1950 to the present. Data accessed on 10/27/2020.

Data attributes: BEGIN_YEARMONTH, BEGIN_DAY, BEGIN_TIME, END_YEARMONTH, END_DAY, END_TIME, EPISODE_ID, EVENT_ID, STATE, STATE_FIPS, YEAR, MONTH_NAME, EVENT_TYPE, CZ_TYPE, CZ_FIPS, CZ_NAME, WFO, BEGIN_DATETIME, CZ_TIMEZONE, END_DATE_TIME, INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT, DAMAGE_PROPERTY, DAMAGE_CROPS, SOURCE, MAGNITUDE, MAGNITUDE_TYPE, FLOOD CAUSE, CATEGORY, TOR_F_SCALE< TOR_LENGTH, TOR_WIDTH, TOR_OTHER_WFO, TOR_OTHER_CZ_STATE, TOR_OTHER_CZ_FIPS, BEGIN_RANGE, BEGIN_AZIMUTH, BEGIN_LOCATION, END_RANGE, END_AZIMUTH, END_LOCATION, BEGIN_LAT, BEGIN_LON, END_LAT, END_LON, EPISODE_NARRATIVE, EVENT_NARRATIVE, DATA_SOURCE

5. FEDB_1118.csv – The University of Connecticut Flood Events Database. Floods retrieved from 6,301 stream gauges in the U.S. after flow separation from 2002 to 2013 (Shen et al., 2017). Data accessed on 11/18/2020.

Data attributes: STCD, StartTimeP, EndTimeP, StartTimeF, EndTimeF, Perc, Peak, RunoffCoef, IBF, Vp, Vb, Vt, Pmean, ETr, ELs, VarTr, VarLs, EQ, Q2, CovTrLs, Category, Geometry

6. GFM_events.csv – Global Flood Monitoring dataset. It is a crowdsourcing flood database derived from Twitter tweets over the globe since 2014. Data accessed on 11/9/2020.

Data attributes: event_id, location_ID, location_ID_url, name, type, country_location_ID, country_ISO3, start, end, time of detection

7. mPing_1030.csv – meteorological Phenomena Identification Near the Ground (mPing). The mPing app is a crowdsourcing, weather-reporting software jointly developed by NOAA National Severe Storms Laboratory (NSSL) and the University of Oklahoma (Elmore et al., 2014). Data accessed on 10/30/2020.

Data attributes: id, obtime, category, description, description_id, lon, lat

8. USFD_v1.0.csv – A merged United States Flood Database from 1900 to the present.

Data attributes: DATE_BEGIN, DATE_END, DURATION, LON, LAT, COUNTRY, STATE, AREA, FATALITY, DAMAGE, SEVERITY, SOURCE, CAUSE, SOURCE_DB, SOURCE_ID, DESCRIPTION, SLOPE, DEM, LULC, DISTANCE_RIVER, CONT_AREA, DEPTH, YEAR.

Details of attributes:

DATE_BEGIN: begin datetime of an event. yyyymmddHHMMSS

DATE_END: end datetime of an event. yyyymmddHHMMSS

DURATION: duration of an event in hours

LON: longitude in degrees

LAT: latitude in degrees

COUNTRY: United States of America

STATE: US state name

AREA: affected areas in km^2

FATALITY: number of fatalities

DAMAGE: economic damages in US dollars

SEVERITY: event severity, (1/1.5/2) according to DFO.

SOURCE: flood information source.

CAUSE: flood cause.

SOURCE_DB: source database from item 1-7.

SOURCE_ID: original ID in the source database.

DESCRIPTION: event description

SLOPE: calculated slope based on SRTM DEM 90m

DEM: Digital Elevation Model

LULC: Land Use Land Cover

DISTANCE_RIVER: distance to major river network in km,

CONT_AREA: contributing area (km^2), from MERIT Hydro

DEPTH: 500-yr flood depth

YEAR: year of the event.

The script to merge all sources and figure plots can be found in https://github.com/chrimerss/USFD.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.