The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) / World Data Center, Boulder maintains an active database of worldwide geomagnetic observatory data. Historically, magnetic observatories were established to monitor the secular change (variation), of the Earth's magnetic field, and this remains one of their most important functions. This generally involves absolute measurements sufficient in number to monitor instrumental drift and to produce annual means. While the current global network of geomagnetic observatories involves over 70 countries operating more than 200 observatories, the historic database includes observations from more than 600 observatories since the early 1800s. The magnetic observatory data are crucial to the studies of secular change, investigations into the Earth's interior, navigation, communication, and to global modeling efforts. The Earth's magnetic field is described by seven parameters. These are declination (D), inclination (I), horizontal intensity (H), vertical intensity (Z), total intensity (F) and the north (X) and east (Y) components of the horizontal intensity. By convention, declination is considered positive when measured east of north, inclination and vertical intensity positive down, X positive north, and Y positive east. The magnetic field observed on Earth is constantly changing.

The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) operated a 915 MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) tower from 21-May-1994 to 20-Sep-1994. The data set provides temperature profiles at 15 heights, containing the variables of virtual temperature, vertical velocity, the speed of sound, and w-bar.

Abstract This article is a research report developed in the Master’s Degree in Social and Institutional Psychology and its theme is the ramifications of the emptiness on a path through the world of work. The writing of narratives of the daily work operated to open questions about the (im)possibility of doing psychology in a place full of contradictions about the meaning of what the work itself should be there. Through the creation of a character, someone, it seeks to touch on a topic that has no definite boundary, it is more about of an atmosphere, a felt-meaning. The invention of someone is the artifice for the experimentation of lives that are not limited to that lived in the first person by the researcher, allowing the passage through an impersonal place, a place as indeterminate as someone, that makes it possible to think about things that are not hidden, but neither are they visible. The emptiness of meaning experienced in the daily work opens in images/scenes that, when recounted in the written form, transform the emptiness into experience.

The submitted data relate to sections 2.3 and 2.4 of: H. Moisl (2022) Dynamical systems implementation of intrinsic sentence meaning, Minds and Machines 32 (2022), which describe the processing architecture of the model of intrinsic sentence meaning proposed there. Six separate programs are used to generate the results presented in the article, whose interrelationships are described in the above-cited sections. The paper with which the data are associated proposes a model for implementation of intrinsic natural language sentence meaning in a physical language understanding system, where 'intrinsic' is understood as 'independent of meaning ascription by system-external observers'. The proposal is that intrinsic meaning can be implemented as a point attractor in the state space of a nonlinear dynamical system with feedback which is generated by temporally sequenced inputs. It is motivated by John Searle's well known (1980) critique of the then-standard and currently still influential Computational Theory of Mind (CTM), the essence of which was that CTM representations lack intrinsic meaning because that meaning is dependent on ascription by an observer. The proposed dynamical model comprises a collection of interacting artificial neural networks, and constitutes a radical simplification of the principle of compositional phrase structure which is at the heart of the current standard view of sentence semantics because it is computationally interpretable as a finite state machine.

In 2016, non-interpretive streamflow statistics were compiled for streamgages located throughout the Nation and stored in the StreamStatsDB database for use with StreamStats and other applications. Two previously published USGS computer programs that were designed to help calculate streamflow statistics were updated to better support StreamStats as part of this effort. These programs are named “GNWISQ” (Get National Water Information System Streamflow (Q) files) and “QSTATS” (Streamflow (Q) Statistics). Statistics for 20,438 streamgages that had 1 or more complete years of record during water years 1901 through 2015 were calculated from daily mean streamflow data; 19,415 of these streamgages were within the conterminous United States. About 89 percent of the 20,438 streamgages had 3 or more years of record, and 65 percent had 10 or more years of record. Drainage areas of the 20,438 streamgages ranged from 0.01 to 1,144,500 square miles. The magnitude of annual average streamflow yields (streamflow per square mile) for these streamgages varied by almost six orders of magnitude, from 0.000029 to 34 cubic feet per second per square mile. About 64 percent of these streamgages did not have any zero-flow days during their available period of record. The 18,122 streamgages with 3 or more years of record were included in the StreamStatsDB compilation so they would be available via the StreamStats interface for user-selected streamgages.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

Estimated annual average wave height (metres) created by a Pelamis Wave Model for Accessible Wave Energy Resource Atlas. Wave height values are measured as lower and upper values in metres as calculated by the Pelamis wave model. Annual average wave height covers an area known as the Irish Exclusive Economic Zone (EEZ). Data model produced in 2005. The Pelamis Wave Model was an oceanographic model using the Pelamis wave energy converter device. The Accessible Wave Energy Resource Atlas was produced to provide data and information on the accessible wave energy resource potential around Ireland. Wave model developed by ESB International (ESBI) as part of the Accessible Wave Energy Atlas Ireland published by the Marine Institute and Sustainable Energy Authority Ireland. Model completed for time period run.

On October 15, 2013, Louisville Mayor Greg Fischer announced the signing of an open data policy executive order in conjunction with his compelling talk at the 2013 Code for America Summit. In nonchalant cadence, the mayor announced his support for complete information disclosure by declaring, "It's data, man."Sunlight Foundation - New Louisville Open Data Policy Insists Open By Default is the Future Open Data Annual ReportsSection 5.A. Within one year of the effective Data of this Executive Order, and thereafter no later than September 1 of each year, the Open Data Management Team shall submit to the Mayor an annual Open Data Report.The Open Data Management team (also known as the Data Governance Team is currently led by the city's Data Officer Andrew McKinney in the Office of Civic Innovation and Technology. Previously (2014-16) it was led by the Director of IT.Full Executive OrderEXECUTIVE ORDER NO. 1, SERIES 2013AN EXECUTIVE ORDERCREATING AN OPEN DATA PLAN. WHEREAS, Metro Government is the catalyst for creating a world-class city that provides its citizens with safe and vibrant neighborhoods, great jobs, a strong system of education and innovation, and a high quality of life; andWHEREAS, it should be easy to do business with Metro Government. Online government interactions mean more convenient services for citizens and businesses and online government interactions improve the cost effectiveness and accuracy of government operations; andWHEREAS, an open government also makes certain that every aspect of the built environment also has reliable digital descriptions available to citizens and entrepreneurs for deep engagement mediated by smart devices; andWHEREAS, every citizen has the right to prompt, efficient service from Metro Government; andWHEREAS, the adoption of open standards improves transparency, access to public information and improved coordination and efficiencies among Departments and partner organizations across the public, nonprofit and private sectors; andWHEREAS, by publishing structured standardized data in machine readable formats the Louisville Metro Government seeks to encourage the local software community to develop software applications and tools to collect, organize, and share public record data in new and innovative ways; andWHEREAS, in commitment to the spirit of Open Government, Louisville Metro Government will consider public information to be open by default and will proactively publish data and data containing information, consistent with the Kentucky Open Meetings and Open Records Act; andNOW, THEREFORE, BE IT PROMULGATED BY EXECUTIVE ORDER OF THE HONORABLE GREG FISCHER, MAYOR OF LOUISVILLE/JEFFERSON COUNTY METRO GOVERNMENT AS FOLLOWS:Section 1. Definitions. As used in this Executive Order, the terms below shall have the following definitions:(A) “Open Data” means any public record as defined by the Kentucky Open Records Act, which could be made available online using Open Format data, as well as best practice Open Data structures and formats when possible. Open Data is not information that is treated exempt under KRS 61.878 by Metro Government.(B) “Open Data Report” is the annual report of the Open Data Management Team, which shall (i) summarize and comment on the state of Open Data availability in Metro Government Departments from the previous year; (ii) provide a plan for the next year to improve online public access to Open Data and maintain data quality. The Open Data Management Team shall present an initial Open Data Report to the Mayor within 180 days of this Executive Order.(C) “Open Format” is any widely accepted, nonproprietary, platform-independent, machine-readable method for formatting data, which permits automated processing of such data and is accessible to external search capabilities.(D) “Open Data Portal” means the Internet site established and maintained by or on behalf of Metro Government, located at portal.louisvilleky.gov/service/data or its successor website.(E) “Open Data Management Team” means a group consisting of representatives from each Department within Metro Government and chaired by the Chief Information Officer (CIO) that is responsible for coordinating implementation of an Open Data Policy and creating the Open Data Report.(F) “Department” means any Metro Government department, office, administrative unit, commission, board, advisory committee, or other division of Metro Government within the official jurisdiction of the executive branch.Section 2. Open Data Portal.(A) The Open Data Portal shall serve as the authoritative source for Open Data provided by Metro Government(B) Any Open Data made accessible on Metro Government’s Open Data Portal shall use an Open Format.Section 3. Open Data Management Team.(A) The Chief Information Officer (CIO) of Louisville Metro Government will work with the head of each Department to identify a Data Coordinator in each Department. Data Coordinators will serve as members of an Open Data Management Team facilitated by the CIO and Metro Technology Services. The Open Data Management Team will work to establish a robust, nationally recognized, platform that addresses digital infrastructure and Open Data.(B) The Open Data Management Team will develop an Open Data management policy that will adopt prevailing Open Format standards for Open Data, and develop agreements with regional partners to publish and maintain Open Data that is open and freely available while respecting exemptions allowed by the Kentucky Open Records Act or other federal or state law.Section 4. Department Open Data Catalogue.(A) Each Department shall be responsible for creating an Open Data catalogue, which will include comprehensive inventories of information possessed and/or managed by the Department.(B) Each Department’s Open Data catalogue will classify information holdings as currently “public” or “not yet public”; Departments will work with Metro Technology Services to develop strategies and timelines for publishing open data containing information in a way that is complete, reliable, and has a high level of detail.Section 5. Open Data Report and Policy Review.(A) Within one year of the effective date of this Executive Order, and thereafter no later than September 1 of each year, the Open Data Management Team shall submit to the Mayor an annual Open Data Report.(B) In acknowledgment that technology changes rapidly, in the future, the Open Data Policy should be reviewed and considered for revisions or additions that will continue to position Metro Government as a leader on issues of openness, efficiency, and technical best practices.Section 6. This Executive Order shall take effect as of October 11, 2013.Signed this 11th day of October, 2013, by Greg Fischer, Mayor of Louisville/Jefferson County Metro Government.GREG FISCHER, MAYOR

This dataset is a mapping between MEANS-InOut input data and Life Cycle Inventories from reference databases (Agribalyse, ecoinvent). The MEANS-InOut input data are agricultural production system inputs (fertilisers, plant protection products, agricultural operations, livestock feed, ingredients to be incorporated into livestock feed, etc.). Each input is associated with one or more LCI, which represent(s) the impacts of the production of this input, and the database from which the LCI(s) is from. This version of the dataset corresponds to the following versions of the databases: Agribalyse v3.1.1 and ecoinvent v3.9. The correspondence file (named mapping_data.tab) is associated with : a document describing the input types in the MEANS-InOut software (file: Input_type_description.pdf), a document describing how the value of the input flow of a LCI for an agricultural system studied in MEANS-InOut is obtained from the value taken by this input in MEANS-InOut. (file: LCI_value_construction.pdf) Ce jeu de données établit la correspondance entre les référentiels de MEANS-InOut et des Inventaires de Cycle de Vie de base de données de référence (Agribalyse, ecoinvent). Les référentiels de MEANS-InOut sont des intrants des systèmes de production agricole (engrais, produits phytosanitaires, opérations agricoles, aliments du bétail, ingrédients à incorporer dans les aliments composés...). A chaque intrant est associé un ou plusieurs ICV, qui représentent les impacts de la production de cet intrant, et la base de données dont le ou les ICV sont issus. Cette version du jeu de données fait la correspondance avec les versions suivantes des bases de données : Agribalyse v3.1.1 et ecoinvent v3.9. Au fichier de correspondances (fichier : mapping_data.tab), sont associés : un document qui décrit les types d'intrants du logiciel MEANS-InOut (fichier : Input_type_description.pdf), un document qui décrit comment est obtenue la valeur du flux des intrants d'un ICV d'un système agricole étudié dans MEANS-InOut à partir de la valeur prise par cet un intrant dans MEANS-InOut. (fichier : LCI_value_construction.pdf)

Abstract This article aims to advance the possibilities of theorization about the meanings of work, organizational bonds (commitment, entrenchment, and consent), and engagement by connecting these constructs in an integrative model. Therefore, it is assumed that the meanings attributed to work are an antecedent variable to commitment, entrenchment, consent, and work engagement. This proposition is considered timely and contributes to the theoretical field as it allows identifying possible associations between the constructs, which helps to understand certain behaviors at work. After analyzing the relationships between the concepts, an integrated model proposal is presented, not yet empirically tested. Finally, a research agenda is suggested.

This NOAA Climate Data Record (CDR) of Zonal Mean Ozone Binary Database of Profiles (BDBP) dataset is a vertically resolved, global, gap-free and zonal mean dataset that was created with a multiple-linear regression model. The dataset has a monthly resolution and spans the period 1979 to 2007. It provides global product in 5 degree zonal bands, and 70 vertical levels of the atmosphere. The regression is based on monthly mean ozone concentrations that were calculated from several different satellite instruments and global ozone soundings. Due to the regression model that was used to create the product, various basis function contributions are provided as unique levels or tiers. To understand the different contributions of basis functions, the data product is provided in five different "Tiers". - Tier 0: raw monthly mean data that was used in the regression model - Tier 1.1: Anthropogenic influences (as determined by the regression model) - Tier 1.2: Natural influences (as determined by the regression model) - Tier 1.3: Natural and volcanic influences (as determined by the regression model) - Tier 1.4: All influences (as determined by the regression model, CDR variable)

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Project Description Details The sources for this data are collected from https://www.maybelline.co.id/, https://www.loreal.co.id/. The research is primarily focused on examining Maybelline New York advertisements. Data were gathered using an observational methodology. The researcher examined the advertisements by searching for them on Google to download the complete text of the advertisements

(1) To increase sensitivity to the mood structure depicted in the Maybelline New York beauty advertisement. (2) It is harder to find interrogative statements than declarative ones. We can observe the many mood structure kinds and their applications to make things simpler. Research is conducted using descriptive qualitative methods. The research in this instance provides methodical, factual, and reliable information regarding the facts and causal connections of the phenomena under study.

This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.

An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

CERES_EBAF-TOA_Edition4.2.1 is the Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) Monthly means data in netCDF format Edition 4.2.1 data product. Data was collected using the CERES Scanner instruments on the Terra, Aqua, and NOAA-20 platforms. Data collection for this product is ongoing.CERES_EBAF-TOA_Edition4.2.1 data are monthly and climatological averages of TOA clear-sky (spatially complete) fluxes and all-sky fluxes, where the TOA net flux is constrained to the ocean heat storage. EBAF-TOA provides some basic cloud properties derived from high-resolution imager data alongside TOA fluxes. The Moderate-Resolution Imaging Spectroradiometer (MODIS) imagers Terra and Aqua and the Visible Infrared Imaging Radiometer Suite (VIIRS) are used for NOAA-20. Observed fluxes are obtained using cloud properties derived from narrow-band imagers onboard Earth Observing System (EOS) Terra and Aqua and NOAA-20 satellites and geostationary satellites to fully model the diurnal cycle of clouds. The computations are also based on meteorological assimilation data from the Goddard Earth Observing System (GEOS) Versions 5.4.1 models until March 2022 and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Unlike other CERES Level 3 clear-sky regional data sets that contain clear-sky data gaps, the clear-sky fluxes in the EBAF-TOA product are regionally complete. The EBAF-TOA product is the CERES project's best estimate of the fluxes based on all available satellite platforms and input data. Only Terra data is used from March 2000 to June 2002; Terra and Aqua are combined from July 2002 until March 2022; and only NOAA-20 is used after March 2022. A correction created from an overlap period with time periods when both Terra and Aqua are available is used to adjust the single satellite periods.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.

These raw data have not been subjected to the National Ocean Service's quality control or quality assurance procedures and do not meet the criteria and standards of official National Ocean Service data. They are released for limited public use as preliminary data to be used only with appropriate caution.

(:unav)...........................................

Learn more about the project and how to use the canopy assessment data by visiting the StoryMap!

Data Dictionary

This data dictionary provides detailed metadata for New Zealand's coastlines, available through the LINZ Data Service, with a particular focus on the Mean High Water Springs coastline.

ERA-Interim is the latest European Centre for Medium-Range Weather Forecasts (ECMWF) global atmospheric reanalysis of the period 1979 to August 2019. This follows on from the ERA-15 and ERA-40 re-analysis projects.

The dataset includes monthly mean of daily mean forecast pressure level data on a reduced N256 Gaussian grid.