Dig into granular industry data: what it is, how to use it and why you should pay attention to it.

After a long-running show was canceled, control charts are used to identify if and when viewing drops. The finest granularity daily viewing has high autocorrelation and control charts use residuals from a seasonal ARIMA model. For coarse granularity data (weekly and monthly viewing) an approximate AR model is derived to be consistent with the finest granularity model. With the proposed approach, a longer memory model is used in the granular data control charts that reduces the number of false alarms from control charts constructed treating granular data as a different measurement.

Breakthrough data in granular activated carbon columns for carcinogenic volatile organic compounds. This dataset is associated with the following publication: Kempisty, D., R.S. Summers, G. Abulikemu , N. Deshpande, J. Rebholz, K. Roberts , and J. Pressman. Granular Activated Carbon Adsorption of Carcinogenic Volatile Organic Compounds at Low Influent Concentrations. Journal AWWA. American Water Works Association, Denver, CO, USA, 1(2): e1128, (2019).

Date of last update: 6 May 2014

As part of the **thematic debate on the opening of public health data **launched by the Ministry of Social Affairs and Health in November 2013, the Etalab mission carried out the most comprehensive census of existing public databases and datasets in the field of health, and today publishes this mapping. More than 260 databases or datasets have been identified.

Each database identified has been evaluated on its current “opening level” against 4 criteria: freedom of access (who has access to the data?), the cost of access (is the data available free of charge?), the format of availability (is the data offered in formats facilitating re-use?), the legal conditions for re-use (is the re-use of data explicitly allowed?)

**Two granularity levels ** are also identified for each database or dataset: the granular level (data at the finest level that can be obtained depending on the origin of the data and the collection system), and the aggregate level (data obtained by grouping granular data according to one or more common characteristics).

We strongly encourage you to consult the Mapping Reading Guide, published below as a resource associated with the dataset. This document presents in particular the two levels of typology used.

If you notice an error or omission in the file, please report it to us using the red icon below.

The FR 2510 will collect granular exposure data on the assets, liabilities, and off-balance sheet holdings of U.S. G-SIBs (Global Systemically Important Banks), providing breakdowns by instrument, currency, maturity, and sector. The FR 2510 will also collect data covering detailed positions for each U.S. G-SIB’s top 35 countries of exposure, on an immediate-counterparty basis, as reported in the consolidated Country Exposure Report (FFIEC 009; OMB No. 7100-0035), broken out by instrument and counterparty sector, with limited further breakouts by remaining maturity, subject to a $2 billion minimum threshold for country exposure. Further, the FR 2510 will collect information on financial derivatives by instrument type and foreign exchange derivatives by currency. The FR 2510 will allow the Federal Reserve to conduct a more complete balance sheet analysis of U.S. G-SIBs. Additionally, the FR 2510 will provide the Federal Reserve with valuable systemic information through the collection of more granular data regarding common or correlated exposures and funding dependencies than is currently collected by existing reports by providing more information about U.S. G-SIBs’ consolidated exposures and funding positions to different countries according to instrument, counterparty sector, currency and remaining maturity.

9691 Global import shipment records of Granular Mixture with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Eswatini is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

We source our e-receipt data via two proprietary consumer apps: 1) Mailtime (email productivity app, YC2016) and RewardMe (seamless cashback app that automatically rewards consumers for their purchase data). This is how we remain GDPR compliant as we receive consent from our users to access their email inbox upon which we then aggregate and anonymise the transaction data to produce granular insights for our clientele.

E-reciept data has an extreme advantage over credit card data. Namely, it can be down to an SKU, itemised level and with other datapoint such as time of purchase, payment method used, discounts used, geolocation data, and user overlap can be calculated.

This dataset consists of example model outputs for PFAS removal by GAC. Explicit descriptions of the data can be found in the associated manuscript.

This dataset is associated with the following publication: Burkhardt, J., N. Burns, D. Mobley, J. Pressman, M. Magnuson, and T. Speth. Modeling PFAS Removal Using Granular Activated Carbon for Full-Scale System Design. JOURNAL OF ENVIRONMENTAL ENGINEERING. American Society of Civil Engineers (ASCE), Reston, VA, USA, 148(3): 04021086-1, (2022).

The Oil and Gas Infrastructure Mapping (OGIM) database is a global, spatially explicit, and granular database of oil and gas infrastructure, developed by Environmental Defense Fund (EDF) (www.edf.org) and MethaneSAT, LLC (www.methanesat.org) – a wholly owned subsidiary of EDF. The OGIM database is developed to support the quantification and source characterization of oil and gas methane emissions. The database development is based on the acquisition, analysis, curation, integration, and quality-assurance, performed at EDF and MethaneSAT, LLC, of publicly available geospatial data sources of oil and gas facilities reported by official government sources, industry, academic, and other non-government entities.

OGIM is a collection of data tables within a GeoPackage, an open-source geospatial database format. Each data table within the GeoPackage includes locations and facility attributes of oil and gas infrastructure types that are important sources of methane emissions, including oil and gas production wells, offshore production platforms, natural gas compressor stations, oil and natural gas processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. All location data have been transformed to a common spatial reference system (WGS 1984, EPSG:4326). The GeoPackage also includes a “Data Catalog” table which lists each primary data source utilized during OGIM database development. Each source in the Data Catalog is assigned a Source Reference ID (‘SRC_ID’) and each record in the OGIM database has a 'SRC_REF_ID' attribute that can be used to join the record to its original source(s).

OGIM v2.5.1 includes approximately 6.7 million features, including 4.5 million point locations of oil and gas wells and over 1.2 million kilometers of oil and gas pipelines. This work and the OGIM database, which we anticipate updating on a regular cadence, helps fill a crucial oil and gas geospatial data need, in support of the quantification and attribution of global oil and gas methane emissions at high resolution. Please see the PDF document in the ‘Files’ section of this page for a description of all attribute columns present within the OGIM database. Full details on database development and related analytics can be found in the following Earth System Science Data (ESSD) journal paper. Please cite the paper when using any version of the database:

Omara, M., Gautam, R., O'Brien, M., Himmelberger, A., Franco, A., Meisenhelder, K., Hauser, G., Lyon, D., Chulakadabba, A., Miller, C., Franklin, J., Wofsy, S., and Hamburg, S.: Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-15-3761-2023, 2023.

Important note: While the results section of the manuscript is specific to v1 of the OGIM, the methods described therein are the the same methods used to develop and update OGIM_v2.5.1. Additionally, while we describe our data sources in detail in the manuscript above, and include maps for all acquired datasets, this open-access version of the OGIM database does not include the locations of about 300 natural gas compressor stations in Russia. Future updates may include these datasets when appropriate permissions to make them publicly accessible are obtained.

OGIM_v2.5.1.gpkg. Key changes since v1.1:

• Significant updates have been made to the oil and natural gas wells layer. In most US states, v2.5.1 relies upon well location data reported by state agencies rather than the Dept. of Homeland Security’s Homeland Infrastructure Foundation-Level Data (HIFLD). Across all countries, about 50% of well records in v2.5.1 were published by their primary source 1-April-2024 or later.
  
  
• The earlier OGIM_v1.1 version included a layer of publicly sourced, spatially explicit oil and natural gas production volumes for the year 2022. This production layer is not present in v2.5.1 as we continue to develop and improve that data product.

OGIM v2.5.1 is based on public-domain datasets reported on or prior to April 2024. Each record in OGIM indicates a source date (SRC_DATE) when the original source of the data was last updated. Some records may have out-of-date information, for example, if facility status has changed since we last acquired the data. We are continuing to update the OGIM database as new public-domain datasets become available.

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Point of Contact at Environmental Defense Fund and MethaneSAT, LLC: Mark Omara (momara@edf.org) and Ritesh Gautam (rgautam@edf.org).

A highly granular database of nearly 500 capital flow management measures that cover 14 instruments and 49 countries at monthly frequency between 2008 and 2021.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Argentina is 1003.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Understand customer behaviour, competitive benchmarking, market share etc. using Measurable AI's email receipt dataset. We own a proprietary consumer panel whereby we can access the email accounts of over 2 million users. We are GDPR compliant as we expressly receive consumer consent via our two consumer apps we built in-house: 1) Mailtime (YC2016; an email productivity app), and 2) RewardMe (cash back app that automatically rewards users with cash dollars for their real purchase data; no need to upload receipts).

We then build email parsers to parse through all the transactional data and then aggregate and anonymise the datasets to produce granular insights for our data savvy clientele.

We provide SKU-level transaction data with actual amount spent, discounts, purchase frequency, time, geolocation data.

Indonesia Iron and Steel: Production: Iron and Steel Making: Pig Iron Granular data was reported at 92.236 IDR bn in 2009. This records a decrease from the previous number of 123.038 IDR bn for 2008. Indonesia Iron and Steel: Production: Iron and Steel Making: Pig Iron Granular data is updated yearly, averaging 104.591 IDR bn from Dec 2006 (Median) to 2009, with 4 observations. The data reached an all-time high of 123.038 IDR bn in 2008 and a record low of 73.642 IDR bn in 2006. Indonesia Iron and Steel: Production: Iron and Steel Making: Pig Iron Granular data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Iron and Steel Sector – Table ID.WAA003: Iron and Steel Production: Iron and Steel Making.

This data set contains 60 true triaxial discrete element method simulations performed on granular material used for data-driven causal relation discovery.

The similarity in mechanical properties of dense active matter and sheared amorphous solids has been noted in recent years without a rigorous examination of the underlying mechanism. We develop a mean-field model that predicts that their critical behavior--as measured by their avalanche statistics--should be equivalent in infinite dimensions up to a rescaling factor that depends on the correlation length of the applied field. We test these predictions in two dimensions (2D) using a numerical protocol, termed 'athermal quasistatic random displacement,' and find that these mean-field predictions are surprisingly accurate in low dimensions. We identify a general class of perturbations that smoothly interpolates between the uncorrelated localized forces that occur in the high-persistence limit of dense active matter and system-spanning correlated displacements that occur under applied shear. These results suggest a universal framework for predicting flow, deformation, and failure in active and sheared disordered materials. ... [Read More]

6148 Global import shipment records of Granular with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The lampricide formulation 3.2% Bayluscide® Granular Sea Lamprey Larvicide (gB) is applied at the water’s surface and falls through the water column to the benthic habitat of larval sea lamprey. Knowledge of how gB falls through the water column is critical to effective delivery of the time release formulation. The average sinking rate of gB was determined at a temperature of 15.0⁰C (13.8-16.0⁰C). Water depth did not have a statistically significant effect on gB sinking rate. The average sinking rate was found to be 7.01 cm/sec (0.230 ft/sec) which corresponds to a falling rate of 14.3 sec/meter (4.35 sec/ft). Average time to Bayluscide release is 218 seconds. A model was created to predict how big of a buffer region is required in different lotic

This is part of the data concerning experiments on barchan-barchan interaction at the grain scale. Here, you find images of all patterns described as well as images of diffusion behavior. Due to the size of files, images during mass exchange process were saved for every 10 images acquired and all images are in .mat format. Also, animated movies, Matlab codes and a txt file explaining how to read .mat files are available.

Our data portal allows you to download historical location-based electricity data with hourly granularity for free. Data includes consumption-based emissions factors from both direct operations and life cycle analysis (LCA) for the years 2021-2023. Electricity Maps wants to accelerate decarbonization by making carbon accounting easier and more accurate. The data portal empowers companies to do more accurate and granular carbon accounting by replacing yearly values with monthly, daily, or hourly.