This dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths. The MDS is a continuous data stream (every 10 seconds) of the atmospheric content of these key chemical species derived from the in-situ measurements collected along ATom flight paths (as reported in the comprehensive related dataset ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols). Values for chemical species measured by multiple instruments were selected from the instrument with better coverage and/or greater precision. Missing values were filled using interpolation for short gaps. For long gaps owing to instrument failure, values were estimated using multiple linear regressions from comparable parallel flights from other ATom campaigns. All species were flagged for instrument source and values were flagged for gap-filling status. In combination, MDS and RDS provide, in essence, a photochemical climatology for each air parcel along ATom flight paths containing the reactive species that control the loss of methane and the production and loss of ozone.

This dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin. Each of the six models produced ozone production and loss and methane loss reactivities initialized using the simulated data beginning with five different days in August (8-01, 8-06, 8-11, 8-16, 8-21). Modeled years for each individual model varied from 1997 to 2016.

This dataset provides information on greenhouse gases and human-produced air pollution, including atmospheric concentrations of carbon dioxide (CO2), methane (CH4), tropospheric ozone (O3), and black carbon (BC) aerosols, collected during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. This dataset includes merged data from all instruments plus additional data such as numbered profiles and distance flown. Merged data products have been created for seven different aggregation intervals (1 second, 10 seconds, and 5 instrument-specific intervals). In the case of data obtained over longer time intervals (e.g., flask data), the merge files provide (weighted) averages to match the sampling intervals. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.

This dataset provides flight track and aircraft navigation data from the NASA Atmospheric Tomography Mission (ATom). Flight track information is available for the four ATom campaigns: ATom-1, ATom-2, ATom-3, and ATom-4. Each ATom campaign consists of multiple individual flights and flight navigational information is recorded in 10-second intervals. Data available for each flight includes research flight number, date, and start and stop time of each 10-second interval. In addition, latitude, longitude, altitude, pressure and temperature is included at each 10-second interval. NASA's ATom campaign deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. During each campaign, flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. One intended use of this flight track data is to facilitate to mapping model results from global models onto the precise ATom flight tracks for comparison.

This database provides access and search capability for NIST critically evaluated data on atomic energy levels, wavelengths, and transition probabilities that are reasonably up-to-date. The Atomic Spectroscopy Data Center has carried out these critical compilations. The Data Center is located in the Physical Measurement Laboratory at the National Institute of Standards and Technology (NIST).

This dataset provides extensive calibration and in-flight performance data for two nucleation mode aerosol size spectrometer (NMASS) instruments utilized in the NASA Atmospheric Tomography Mission (ATom). Each NMASS has five condensation particle counters (CPCs) that detect particles above a different minimum size, determined by the maximum vapor supersaturation encountered by the particles. Operated in parallel, the CPCs provide continuous concentrations of particles in different cumulative size classes between 3 and 60 nm. Knowing the response function of each CPC, numerical inversion techniques were applied to recover size distributions from the continuous concentrations. Data provided include: NMASS counting efficiencies and diameters of calibration aerosols, inverted particle size distributions; comparisons of NMASS and Scanning Mobility Particle Sizer (SMPS) results; and performance at flows, temperatures, and pressures measured by both NMASSs and comparison with Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) concentrations collected on board the NASA DC-8 aircraft during an ATom flight in February 2017.

Soluble acidic gases and aerosols (SAGA) were collected with two related installations; a mist chamber/ion chromatography (MC/IC) system and a paired bulk aerosol system. The MC/IC system measures in situ atmospheric distributions of nitric acid (plus < 1 um NO3 aerosol) and fine (< 1 um) aerosol sulfate at an approximately 80-second interval. The paired bulk aerosol system collects particulates onto filters for subsequent analysis. Collected filters were first extracted with water to obtain the water-soluble (WS) constituents and then extracted again using methanol to collect the methanol soluble (MS) fraction. The light absorption of filtered extracts was measured from 300 to 700 nm. Ion chromatography on aqueous extracts of the bulk aerosol samples collected on Teflon filters were used to quantify soluble ions (Cl-, Br-, NO3-, SO42-, C2O42-, Na+, NH4+, K+, Ca+, and Mg+). The SAGA system is provided by the University of New Hampshire (UNH).

We present recent improvements within the growing field of Rydberg atom sensors. While initially started as a path towards absolute, independent measurements of electric fields, the research landscape has evolved into the realm of quantum sensors and receivers. We discuss the capabilities and limitations of Rydberg atom receivers, and we show how different atomic properties enhance or limit sensitivity and bandwidth.This data is for a review paper. Figures 6 (a) and 8 (a) and (b) are new data. The rest of the data is extracted from other NIST publications that have a data management plan. Related data are from the following papers.https://doi.org/10.1063/5.0069195https://doi.org/10.48550/arXiv.2402.00718https://doi.org/10.1116/5.0098057

Contains raw and reconstructed data from our paper "Extending Estimating Hydrogen Content in Atom Probe Tomography Experiments with 𝐻2 Molecule Formation". Contains a list of files in Excel format (.xlsx), raw data in .rhit and .hits files and reconstructions in .pos and .epos files. .rhit and .hits are proprietary formats by Cameca/Ametek, and their software (currently IVAS or APsuite 6) is needed to open these files. pos and epos are common file formats for reconstructions in the APT community, and a documentation can eg be found in the book "Atom Probe Microscopy" by Gault,Moody,Cairney,Ringer, ISBN 978-1-4614-3435-1. The epos files are complressed using 7-zip.

This CNIG data standard concerns local planning documents (LDPs) and land use plans (POSs that are PLU). This data standard provides a technical framework describing in detail how to dematerialise these town planning documents in a spatial database that can be used by a GIS tool and interoperable. This standard of data covers both the graphical plans of sectors and the information overlaying them. This CNIG data standard was developed on the basis of the specifications for the dematerialisation of planning documents created in 2012 by the CNIG, itself based on the consolidated version of the urban planning code dated 16 March 2012. The recommendations of these two documents are consistent even if their purpose is not the same. The CNIG data standard provides definitions and a structure for organising and storing spatial data from communal maps in an infrastructure, while the CNIG specifications are used to frame the digitisation of these data. The ‘Data Structure’ section presented in this CNIG standard provides additional recommendations for the storage of data files. These are specific choices for the common data infrastructure of the ministries responsible for agriculture and sustainable development, which do not apply outside their context.

This handbook provides a selection of the most important and frequently used atomic spectroscopic data in an easily accessible format. The compilation includes energy levels, ionization energies, wavelengths, line intensities, transition probabilities, and spectrum assignments for the neutral and singly-ionized atoms of all elements hydrogen through einsteinium (Z = 1-99), given in separate tables for each element. It includes approximately 12,000 spectral lines of all elements. Bibliographic references are provided for all data.

This dataset contains carbon monoxide (CO) observations at 10-second intervals from flights during the ATom-1 campaign in 2016 and simulated CO concentrations from the Goddard Earth Observing System version 5 (GEOS-5) model for the corresponding locations along the ATom flight tracks. The Atmospheric Tomography Mission (ATom) is a NASA Earth Venture Suborbital-2 mission studying the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. The airborne observations were collected using the Quantum Cascade Laser System (QCLS) instrument, a high-frequency laser spectroscopy instrument for in situ atmospheric gas sampling. This dataset provides a direct comparison of observational and simulated CO that will be used to inform future atmospheric modeling experiments. The dataset also contains simulated tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. This dataset contributes to one of the ATom mission objectives to create an observation-based chemical climatology of important atmospheric constituents and their reactivity in the remote troposphere.

This dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin. Each of the six models produced ozone production and loss and methane loss reactivities initialized using the simulated data beginning with five different days in August (8-01, 8-06, 8-11, 8-16, 8-21). Modeled years for each individual model varied from 1997 to 2016.

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using "message-passing" ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provides a coherent foundation to systematize our understanding of both atom-centered and message-passing, invariant and equivariant machine-learning schemes.

This record contains the data and code required to reproduce the results from the corresponding paper, computing message-passing inspired machine learning features built on top of density correlation. The data used in this article is a subset of other existing datasets, which can be found online:

• methane dataset: https://archive.materialscloud.org/record/2020.105
• NaCl dataset: https://github.com/dilkins/TENSOAP/tree/ea671154b3642b4ec879a4292a4dd4399ddbdea6/example/random_nacl
• QM7 and QM9 with dipole moments: https://archive.materialscloud.org/record/2020.56

Description of the INSPIRE Download Service (predefined Atom): Commercial, Mixed Territory, Withdrawal Changes to Partial Amendment 1 - The link(s) for downloading the records is/are dynamically generated from Get Map calls to a WMS interface

This dataset provides airborne in situ observations of submicron organic aerosol (OA) mass concentrations during the first (mid-2016) and second (early-2017) global deployments of the Atmospheric Tomography Mission (ATom), as well as modeled submicron OA mass concentrations along the flight tracks from global chemistry models that implement a variety of commonly used representations of OA sources and chemistry. In situ observations include non-refractory submicron aerosols measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS), aerosol volume concentrations measured by the Aerosol Microphysical Properties package (AMP), black carbon mass content measured by the Single Particle Soot Photometer (NOAA SP2), and refractory and non-refractory aerosol composition measured by the Particle Analysis By Laser Mass Spectrometry (PALMS). Both observed and modeled data are provided at a 60-second temporal resolution. The data are provided in netCDF format.

Description of the INSPIRE Download Service (predefined Atom): Statutes on the development plan "Im Fastnachtsstück - An den weißen Wacken I" (1st amendment) of 01.02.1995 - The link(s) for downloading the data sets is/are dynamically generated from Get Map calls to a WMS interface

Risk Prevention Plans (RPPs) are the key government instrument for risk prevention. Their objective is to control development in areas at risk. The development of a risk prevention plan generates a set of spatial data organised into several data sets. The same PPR may include spatial datasets containing: — main scopes of the RPP; — restricted areas of the plan once approved. RPP regulations generally distinguish between ‘construction ban areas’, so-called ‘red areas’, where the hazard level is high and where the general rule is the construction ban; ‘areas subject to requirements’, known as ‘blue zones’ where the hazard level is medium and projects are subject to requirements adapted to the type of issue and areas not directly exposed to risks but subject to prohibitions or prescriptions; — hazard areas represented on the map of hazards used for risk analysis by crossing with the stakes, specifying for each zone the level of the hazards to which it is exposed; — issues which are persons, property, activities and elements of cultural or environmental heritage threatened by a hazard and likely to be affected or damaged by it; — origins of risk, i.e. the entity of the real world which, through its presence, represents a potential risk. This entity may be characterised by a name, a reference to an external object or a geographical object that locates the actual entity causing the risk.

Each element in the same PPRN dataset is bound by the GASPAR format identifier “ddd[PREF|DDT|DDT|DREAL]aaaannnn” (AAAA and NNNN correspond to the reference year and the order number of the PPR procedure associated in GASPAR) to a single object in the PPRN document table described by the N_DOCUMENT_PPRN metadata sheet.

Risk Prevention Plans (RPPs) are the key government instrument for risk prevention. Their objective is to control development in areas at risk. The development of a risk prevention plan generates a set of spatial data organised into several data sets. The same PPR may include spatial datasets containing: — main scopes of the RPP; — restricted areas of the plan once approved. RPP regulations generally distinguish between ‘construction ban areas’, so-called ‘red areas’, where the hazard level is high and where the general rule is the construction ban; ‘areas subject to requirements’, known as ‘blue zones’ where the hazard level is medium and projects are subject to requirements adapted to the type of issue and areas not directly exposed to risks but subject to prohibitions or prescriptions; — hazard areas represented on the map of hazards used for risk analysis by crossing with the stakes, specifying for each zone the level of the hazards to which it is exposed; — issues which are persons, property, activities and elements of cultural or environmental heritage threatened by a hazard and likely to be affected or damaged by it; — origins of risk, i.e. the entity of the real world which, through its presence, represents a potential risk. This entity may be characterised by a name, a reference to an external object or a geographical object that locates the actual entity causing the risk.

Each element in the same PPRN dataset is bound by the GASPAR format identifier “ddd[PREF|DDT|DDT|DREAL]aaaannnn” (AAAA and NNNN correspond to the reference year and the order number of the PPR procedure associated in GASPAR) to a single object in the PPRN document table described by the N_DOCUMENT_PPRN metadata sheet.

Although Rydberg atom-based electric field sensing provides key advantages over traditional antenna-based detection, it remains limited by the need for a local oscillator (LO) for low-field and phase resolved detection. In this work, we demonstrate the general applicability of closed-loop quantum interferometric schemes for Rydberg field sensing, which eliminate the need for an LO. We reveal that the quantum-interferometrically defined phase and frequency of our scheme provides an internal reference that enables LO-free full 360 degree-resolved phase sensitivity. This internal reference can further be used analogously to a traditional LO for atom-based down-mixing to an intermediate frequency for lock-in-based phase detection, which we demonstrate by demodulating a four phase-state signal broadcast on the atoms.