Webpage dedicated to 2022 open data day for the City of Los Angeles

Chemical Measuments D2............................

Both events demonstrated a strong commitment to diversity and inclusivity, successfully integrating perspectives from various groups and sectors into the core discussions.

Programme for the Open Data Day event that took place on 6 March 2020 at UCT, hosted by the Digital Library Services team (headed by Niklas Zimmer). This event forms part and is commemorated every year with International Open Data Day. UCT was one of 7 South African Institutes/Organisations to register an event on the International Open Data Day website.It was a half-day event with 13 lightning talks presented by different speakers. The event was opened with a keynote address by Gabriella Razzano (founding member of OpenUp), titled 'The importance of open data for equal development in South Africa.'

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** 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 * 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 **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Developers using the DOL-wide API have access to a variety of queries providing usage metrics for their app's key.

These data files contain data from assessing the Impact of Full-day Kindergarten on Maternal Employment and Achievement.

This dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The daily observation data are the gauge data from the Global Summary of Day (GSOD) from the National Climatic Data Center (NCDC). This dataset includes daily mean visibility (VIS), sea level pressure (SLP), temperature (TEMP), dew-point temperature (DEWP), wind speed (WS) and other meteorological phenomena as well as fog, rain or drizzle, snow or ice pellets, hail and other extreme weather.

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). The Global Historical Climatology Network - Daily (GHCN-Daily) dataset addresses the need for historical daily records over global land areas. Like its monthly counterpart (GHCN-Monthly), GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The meteorological elements measured for the data set include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. GHCN-Daily serves as the official archive for daily data from the Global Climate Observing System (GCOS) Surface Network (GSN) and is particularly well suited for monitoring and assessment activities related to the frequency and magnitude of extremes. Sources for the GHCN-Daily data set include, but are not limited, to U.S. Cooperative Summary of the Day, U.S. Fort data, U.S. Climate Reference Network, Community Collaborative Rain, Hail and Snow Network, and numerous international sources. The dataset contains measurements from over 75,000 stations worldwide,about two thirds of which are for precipitation measurement only. Approximately 8500 are regularly updated with observations from within the last month. While most of these sites report precipitation, daily maximum and minimum temperatures are available at more than 25,000 of them, and over 24,000 contain records of snowfall and/or snow depth. The process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: screening the source data for stations whose identity is unknown or questionable; classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and mingling the data from the different sources. The first two of these steps are performed whenever a new source dataset or additional stations become available, while the actual mingling of data is part of the automated processing that creates GHCN-Daily on a regular basis. GHCN-Daily data are subject to a suite of quality assurance checks. The checks consist of several types of carefully evaluated tests that detect duplicated data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of flagged values was used to set the threshold for each procedure such that the tests false-positive rate is minimized. In addition, the tests are performed in a deliberate sequence in an effort to enhance the performance of the later checks by detecting errors with the checks applied earlier in the sequence.

LAT solar gamma-ray flux > 100 MeV, one point per solar exposure (i.e. average of the 20-40 minutes of solar observation every ~1.5 hours) calculated by two different methods.

This entry covers single-level data and soil-level data at the original time resolution (once a day, or once every 6 hours, depending on the variable). Seasonal forecasts provide a long-range outlook of changes in the Earth system over periods of a few weeks or months, as a result of predictable changes in some of the slow-varying components of the system. For example, ocean temperatures typically vary slowly, on timescales of weeks or months; as the ocean has an impact on the overlaying atmosphere, the variability of its properties (e.g. temperature) can modify both local and remote atmospheric conditions. Such modifications of the 'usual' atmospheric conditions are the essence of all long-range (e.g. seasonal) forecasts. This is different from a weather forecast, which gives a lot more precise detail - both in time and space - of the evolution of the state of the atmosphere over a few days into the future. Beyond a few days, the chaotic nature of the atmosphere limits the possibility to predict precise changes at local scales. This is one of the reasons long-range forecasts of atmospheric conditions have large uncertainties. To quantify such uncertainties, long-range forecasts use ensembles, and meaningful forecast products reflect a distributions of outcomes. Given the complex, non-linear interactions between the individual components of the Earth system, the best tools for long-range forecasting are climate models which include as many of the key components of the system and possible; typically, such models include representations of the atmosphere, ocean and land surface. These models are initialised with data describing the state of the system at the starting point of the forecast, and used to predict the evolution of this state in time. While uncertainties coming from imperfect knowledge of the initial conditions of the components of the Earth system can be described with the use of ensembles, uncertainty arising from approximations made in the models are very much dependent on the choice of model. A convenient way to quantify the effect of these approximations is to combine outputs from several models, independently developed, initialised and operated. To this effect, the C3S provides a multi-system seasonal forecast service, where data produced by state-of-the-art seasonal forecast systems developed, implemented and operated at forecast centres in several European countries is collected, processed and combined to enable user-relevant applications. The composition of the C3S seasonal multi-system and the full content of the database underpinning the service are described in the documentation. The data is grouped in several catalogue entries (CDS datasets), currently defined by the type of variable (single-level or multi-level, on pressure surfaces) and the level of post-processing applied (data at original time resolution, processing on temporal aggregation and post-processing related to bias adjustment). The data includes forecasts created in real-time each month starting from the publication of this entry and retrospective forecasts (hindcasts) initialised over periods in the past specified in the documentation for each origin and system.

High Frequency Indicator: The dataset contains day-wise compiled data from the year 2003 to till date on the Organization of the Petroleum Exporting Countries (OPEC) international basket price of crude oil

The OPEC basket or OPEC reference basket refers to the weighted mean or average of oil prices that OPEC member countries throughout the world maintain. The basket refers generally to a standard or set reference point for countries that analyze the oil prices and the consistency of the international oil market

A ranking of Office of Hearings Operations (OHO) hearing offices by the average number of hearings dispositions per administrative law judge (ALJ) per day. The average shown will be a combined average for all ALJs working in that hearing office.

United States Heat Index data was reported at 1.290 Day in 2020. This records a decrease from the previous number of 1.570 Day for 2019. United States Heat Index data is updated yearly, averaging 0.430 Day from Dec 1970 (Median) to 2020, with 51 observations. The data reached an all-time high of 2.310 Day in 2011 and a record low of 0.050 Day in 1976. United States Heat Index data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Environmental: Climate Risk. Total count of days per year where the daily mean Heat Index rose above 35°C. A Heat Index is a measure of how hot it feels once humidity is factored in with air temperature.;World Bank, Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org;;

This data set (NmHRIR3G) consists of daily composites constructed from Nimbus 1, Nimbus 2, and Nimbus 3 satellites High Resolution Infrared Radiometer (HRIR) data for the region between 60 N and 60 S. Measurements were obtained during 1964, 1966, and 1969. Data are available as GeoTIFFs and browse images. For the HDF5 formatted version of these data, see the Nimbus High Resolution Infrared Radiometer Remapped Digital Data Daily L3, HDF5 data set.

The Fermi GBM Daily Data database table contains entries for each day for which GBM data has been processed.

An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Petra Great Temple Excavations" data publication.

U.S. Daily Surface Data consists of several closely related data sets: DSI-3200, DSI-3202, DSI-3206, and DSI-3210. These are archived at the National Climatic Data Center (NCDC). U.S. Daily Surface Data is sometimes called cooperative data or COOP, named after the cooperative observers that recorded the data. In any one year there are about 8,000 stations operating. Most cooperative observers are state universities, state or federal agencies, or private individuals whose stations are managed and maintained by the National Weather Service. Each cooperative observer station may record as little as one parameter (precipitation), or several parameters. U.S. Daily Surface Data is also called Summary of the Day data. The original data was manuscript records, the earliest of which are from the 1800s. Records for approximately 23,000 stations have been archived from the beginning of record through the present. Official surface weather observation standards can be found in the Federal Meteorological Handbook.