We are pleased to announce the pre-release of the CRAFTS wide-band spectral datacubes. This encompasses the frequency range of 1323 to 1419 MHz and includes almost all the drift scans conducted under the CRAFTS project between July 31, 2021 and January 30, 2025. In total, there are 182 drift scans, amounting to about 880 hours of data covering ~3500 square degrees of the sky (blue regions in Fig.1). Please note that observations prior to July 31st, 2021 and a portion of later data (gray regions in Fig.1) are excluded in this pre-release due to damage by compressor RFI, being led by an external PI, or other specific considerations. The data has been processed into datacubes with the intention of facilitating extra-galactic spectral line research. The frequency range has been selected because data below 1323 MHz has a large chance to be affected by satellite radio frequency interference (RFI), while data above 1419 MHz is predominantly influenced by Galactic HI, which have already been incorporated into previously released narrow-band datacubes. The datasets are publicly available without collaboration required. Proper attribution through citation of the dataset DOI and related publications listed in the Reference section of the Readme document is appreciated.Detailed information about the dataset and subsequent releases can be found on the HIverse platform (https://hiverse.zero2x.org/wide). The HIverse platform features an integrated search engine, through which users can search by RA & Dec coordinates.

At Our World in Data, we are building an extensive dataset of inequality and poverty indicators, pulling together multiple sources to provide as comprehensive a view as possible.

To make it easier to navigate this wide range of data, below we provide links to a set of Data Explorers that allow you to explore a very detailed range of indicators and compare data across sources. The explorers draw from three prominent sources, each offering a global perspective on poverty and inequality: the World Bank Poverty and Inequality Platform, the Luxembourg Income Study, and the World Inequality Database. Information about the definitions and methods behind the data from each of these sources is provided at the bottom of this page.

The detailed data contained in the explorers collected below is intended for experts or researchers who are already quite familiar with the measures and concepts involved. Users with a more general interest are likely to benefit more from the Data Explorers shown in our topic pages on Inequality and Poverty. These provide an overview of the key indicators from this collection.

Study on the thermodynamic properties of a material is the substance a physical or chemical process based. A full set of chemical thermodynamic data for scientists and improve research efficiency and related industries improve production efficiency is of extremely important significance. Although since the late 80 's, start working on thermodynamic database construction in our country, but abroad, domestic thermodynamic database still has many many problems. More important is, whether foreign or domestic databases of chemical thermodynamics, temperature range is 200K-6000K or higher, without the 200K of chemical thermodynamics data. And often 200K scientific and engineering application of thermodynamic data, and is very important. Based on the formula of NASA received specific heat at constant pressure, entropy and enthalpy parameter data. Will be 200K the data input parameters to REFPROP under low temperature thermodynamic data. The obtained data with the real gas equation is used to calculate thermodynamic data, using the NASA interpolation-extrapolation of data for comparison, confirmed the data obtained by this method with good accuracy.

Our foreclosure data offering provides an extensive suite of real-time real estate data, available through both API integration and bulk data delivery. This rich dataset is designed to meet the needs of a variety of users, from real estate investors to foreclosure prevention services and market analysts. With over 31 data points available, this dataset covers multiple aspects of foreclosure processes, including auction details, loan information, foreclosure status, and trustee data. Below is a detailed description of the data points and their potential use cases.

Data Points Overview for Foreclosure Data:

1. Auction Data (9+ Data Points) Auction Location, Auction Time, Case Number, Bid Parameters

2. Loans/Lender Data (9+ Data Points) Lender Name, Original Loan Details, Unpaid Balances, Pre-Foreclosure Flags, Related Documents

3. Foreclosure Status Data (7+ Data Points) Recording Date, Release Date, Status Indicators and Codes

4. Trustee Data (6+ Data Points) Trustee Name, Trustee Address, Trustee Phone Number, Sale Number

Top Use Cases

1. Surface Investment Opportunities Websites and Applications: Integrate our foreclosure data into real estate platforms to provide users with up-to-date information on potential investment properties. This can enhance search functionality and deliver greater value by identifying promising foreclosure opportunities.

2. Foreclosure Prevention Services Sales and Marketing: Leverage foreclosure data to target homeowners in distress with tailored marketing efforts. By identifying properties in pre-foreclosure status, you can focus your outreach to offer services designed to prevent foreclosure, such as financial counseling or loan modification programs.

3. Market Analysis and Predictive Analytics Data-Driven Insights: Utilize the comprehensive dataset to perform in-depth market analysis and develop predictive models. This can help forecast foreclosure trends, assess market conditions, and make informed decisions based on historical and current foreclosure activity.

Access and Delivery

Our foreclosure data is accessible through two primary methods: - API Integration: Seamlessly integrate the data into your applications or platforms with our robust API, offering real-time access and automated updates. - Bulk Data Delivery: Obtain large datasets for offline analysis or integration into internal systems through bulk delivery options, providing flexibility in how you utilize the information.

This comprehensive data listing is designed to empower users with detailed and actionable foreclosure data, facilitating a range of applications from investment analysis to foreclosure prevention and market forecasting.

This data release includes time series data collected at the Bremerton Naval Complex, Bremerton WA. Groundwater levels and water quality parameters in two monitoring wells were recorded every 15 minutes during a 7-month deployment. Time series data were collected from June 29, 2018, to February 26, 2019. Field deployment details and quality assurance methods are included in the following paragraphs. Groundwater monitoring well MW-709 was monitored using two data loggers. The first data logger is a non-vented pressure transducer (In-Situ Rugged TROLL 100) that was deployed resting on the well bottom (BOT). The well measurement point (MP) elevation is 16.86 feet above NAVD88 with a total depth from MP of 30.00 feet and the transducer rested at -14.14 feet below NAVD88. Data collected at this location is referenced by National Water Information System (NWIS) site ID 473324122382202. Temperature and depth were recorded every 15-mintes for the entire deployment. Time-series data from this data logger are located in the text file “MW-709_BOT_time-series_data.txt”. Temperature ranged from 12.1 to 18.8 degrees Celsius. Pressure transducers, which measure the pressure above the transducer, were programmed in feet for all loggers used in this data set. The collected depth data was corrected for changes in barometric pressure by subtracting the atmospheric (barometric) pressure (feet of water) from pressure transducer measurements for each 15-minute value, with due consideration of units. Barometric-corrected depth data was converted to water level altitude by adding an -13.41 feet offset to each 15-minute value to reference the North American Vertical Datum of 1988 (NAVD88). Water level altitude ranged from 1.75 to 6.71 feet. The second data logger is a non-vented pressure transducer (Solinst LTC Levelogger) programmed to record temperature, depth, specific conductance and salinity. The data logger was suspended from a buoy with a deployment depth of 1.55 feet below the water surface (TOP). Data collected at this location is referenced by NWIS site ID 473324122382201.The buoy system was intended to maintain a constant deployment depth below the well water surface as tidally-influenced monitoring well water levels change. There was a lag in the buoy system response to well water level changes caused by the 150 mL plastic bottle getting stuck moving up and down inside the well casing resulting in water depth fluctuation ranging from 2.77 to 8.75 feet. Temperature readings during the calibration check procedures were determined less than poor (greater than one degree Celsius lower than a NIST-certified thermistor at five temperatures); therefore, the time-series data did not meet USGS publication criteria. Specific conductance readings during the calibration check procedures were determined less than poor (greater than 15 percent lower than a calibration standard); therefore, the time-series data did not meet USGS publication criteria. Water level altitude time-series data from this data logger are located in text file: “MW-709_TOP_time-series_data.txt”. Groundwater monitoring well MW-412 was monitored using two data loggers. The first was a non-vented pressure transducer (In-Situ Rugged TROLL 100) that was deployed resting on the well bottom (BOT). The well measurement point (MP) elevation is 14.66 feet above NAVD88 with a total depth from MP of 24.00 feet and the transducer rested at -9.34 feet below NAVD88. Data collected at this location is referenced by NWIS site ID 473323122382902. Temperature and depth were recorded every 15-minutes for the entire deployment. Time-series data from this data logger are located in the text file “MW-412_BOT_time-series_data.txt”. Temperature ranged from 11.1 to 14.8 degrees Celsius. Barometric-corrected depth data was converted to water level altitude by adding an -9.40 feet offset to each 15-minute value to reference the North American Vertical Datum of 1988 (NAVD88). Water level altitude ranged from -0.06 to 2.86 feet. The second data logger is a non-vented pressure transducer (In-Situ Aqua TROLL 200) programmed to record temperature, depth, specific conductance and salinity suspended from a buoy and deployed 1.55 feet below the water surface (TOP). Data collected at this location is referenced by NWIS site ID 473323122382901. The buoy system was intended to maintain a constant deployment depth below the well water surface as tidally influenced monitoring well water levels change. There was a lag in the buoy system response to well water level changes caused by the 150 mL plastic bottle getting stuck moving up and down inside the well casing resulting in water depth fluctuation ranging from 0.95 to 4.36 feet. Time-series data from this data logger are located in the text file: “MW-412_TOP_time-series_data.txt”. Survey of MW-412 and MW-709 top-of-casing altitudes was completed on 4/26/2019. Vertical (altitude) position data was collected with RTN-GPS at each monitoring well measurement point (MP). MW-412 measured MP altitude is 14.66 feet and MW-709 MP is 16.86 feet above the North American Vertical Datum of 1988 (NAVD88). Depth to water (DTW) measurements recorded on the 15-minute interval and altitude of MP was used for calculating the offset applied to 15-minute time-series barometric corrected depth data. Offset calculation used is: Offset = (MP - Altitude - DTW - 15-minute depth). Hourly barometric pressure data from 06/01/2018 to 03/30/2019 was acquired from the Bremerton Naval Airport weather station. Missing data was flagged as not available (NA). All groundwater data was recorded at 15-minute intervals, so 15-minute time-steps were generated from barometric hourly data by keeping each hour data point constant for the remaining three 15-minute time steps within that hour. Barometric pressure in units of hectopascals at mean sea level was first converted to millimeters of mercury (conversion factor 0.7500616827) then converted to feet of water (conversion factor 0.04460334762). Depth data was corrected for barometric pressure effects by subtracting barometric pressure from each 15-minute depth data. Time-series data of barometric pressure are located in the text file: “Barometric Pressure.txt”. Tidal water level 15-minute time-series data was acquired from the National Oceanic and Atmospheric Administration (NOAA) Tide Station: 9445958 Bremerton, WA. The NOAA Tides and Currents website (https://tidesandcurrents.noaa.gov/stationhome.html?id=9445958#tides) for this station was accessed on 07/02/2019 for download of 15-minute tidal data referenced to NAVD88 from June 2018 to February 2019. Data time series was adjusted for consistent Pacific Standard Time step. Tidal water level altitude ranged from -6.02 to 11.29 feet. Time-series data of tidal levels are located in the text file: “Tidal_WL_Altitude_time_series_data.txt”. Quality assurance and control measures for continuous water-quality data were performed per USGS protocols (Wagner and others, 2006), which included temperature checks at five temperatures against a NIST-certified thermistor and specific conductance checks against calibration standards spanning the range of environmental values. Other than the temperature and specific conductance readings that were outside of publication criteria from the logger at the top of well 709, all other parameters met quality control criteria for publication and no corrections were applied. During site visits, discrete measurements of water level and water-quality data were collected, including vertical profiles to identify changes in temperature and specific conductance associated with a seawater-groundwater interface. Discrete water-quality data are published in the USGS National Water Information System database by Site ID (https://nwis.waterdata.usgs.gov/usa/nwis/qwdata).

AbstractCamera-traps (CTs) are an increasingly popular tool for wildlife survey and monitoring. Estimating relative abundance in unmarked species is often done using detection rate as an index of relative abundance, which assumes a positive linear relationship with true abundance. This assumption may be violated if movement behavior varies with density, but the degree to which movement is density-dependent across taxa is unclear. The potential confounding of population-level relative abundance indices by movement depends on how regularly, and by what magnitude, movement rate and home-range size vary with density. We conducted a systematic review and meta-analysis to quantify relationships between movement rate, home range size, and density, across terrestrial mammalian taxa. We then simulated animal movements and CT sampling to test the effect of contrasting movement scenarios on CT detection rates. Overall, movement rate and home range size were negatively correlated with density and positively correlated with one another. The strength of the relationships varied significantly between taxa and populations. In simulations, detection rates were related to true abundance but underestimated change, particularly for slower moving species with small home ranges. In situations where animal space use changes markedly with density, we estimate that up to thirty percent of a true change in abundance may be missed due to the confounding effect of movement, making trend estimation more difficult. The common assumption that movement remains constant across densities is therefore violated across a wide range of mammal species. When studying unmarked species using CT detection rates, researchers and managers should consider that such indices of relative abundance reflect both density and movement. Practitioners interpreting changes in detection rates should be aware that observed differences may be biased low relative to true changes in abundance, and that further information on animal movement may be required to make robust inferences on population trends., Usage notesData files are contained in the zipped folder BroadleyEtAl_EcolEvol_Data.zip MetaPercent_HRMovt.csv, MetaPercent_DensMovt.csv, and MetaPercent_DensHR.csv, contain data used to graph changes in density, movement rate, and home range size. Indicated are the author, species, each parameter as a percentage of that of the reference population (where the reference is the population with the lowest value for the parameter displayed on the x-axis), and the x-axis parameter as a fold change. MetaForest_HRMovt.csv, MetaForest_DensMovt.csv, and MetaForest_DensHR.csv, contain data used to conduct the meta analysis (i.e., data for each study that provided sufficient statistical information for the given parameters). Indicated are the author and species of the study, percent change in the given parameters for the two populations considered, N, test statistic type and value, and the standardized effect size (as a correlation coefficient). SimulatedHitrateData.csv contains outputs from the movement simulations as described in the paper. The title of each column indicates the number of individuals, the scenario speed, the scenario home range size, and whether the data below represents the sum encounters with cameras, or the hitrate (detections/d). Each row represents an additional instance of the simulation.

The Intelligent Building Agents (IBA) project is part of the Embedded Intelligence in Buildings Program in the Engineering Laboratory at the National Institute of Standards and Technology (NIST). A key part of the IBA Project is the IBA Laboratory (IBAL), a unique facility consisting of a mixed system of off the shelf equipment, including chillers and air handling units, controlled by a data acquisition system and capable of supporting building system optimization research under realistic and reproducible operating conditions.The database contains the values of approximately 300 sensors/actuators in the IBAL, including both sensor measurements and control actions, as well as approximately 850 process data, which are typically related to control settings and decisions. Each of the sensors/actuators has associated metadata. The metadata, sensors/actuators, and process data are defined on the "metadata", "sensors", and "parameters" tabs in the definitions file. Data are collected every 10 s.The database contains two dashboards: 1) Experiments - select data from individual experiments and 2) Measurements - select individual sensor/actuator and parameter data. The Experiments Dashboard contains three sections. The "Experiment Data Plot" shows plots of the sensor/actuator data selected in the second section, "Experiment/Metadata". There are plots of both scaled and raw data (see the meta data file for the conversion from raw to scaled data). Underneath the plots is a "Download CSV" button; select that button and a csv file of the data in the plot is automatically generated. In "Experiment/Metadata", first select an "Experiment" from the options in the table on the left. A specific experiment or type of experiment can be found by entering terms in the search box. For example, searching for the word "Charge" will bring up experiments in which the ice thermal storage tank is charged. The table of experiments also includes the duration of the experiment in minutes.Once an experiment is selected, specific sensor/actuator data points can be selected from the "Measurements" table on the right. These data can be filtered by subsystem (e.g., primary loop, secondary loop, Chiller1) and/or measurement type (e.g., pressure, flow, temperature). These data will then be shown in the plots at the top. The final section, "Process", contains the process data, which are shown by the subsystem. These data are not shown in the plots but can be downloaded by selecting the "Download CSV" button in the "Process" section. The Measurements Dashboard contains three sections. The "Date Range" section is used to select the time range of the data. The "All Measurements" section is used to select specific sensor/actuator data. As in the Experiments Dashboard, these data can be filtered by subsystem and/or measurement type. The scaled and raw values of the selected data are then plotted in the "Historical Data Plot" section. The "Download CSV" button underneath the plots will automatically download the selected data.

Further improvements to lithium-ion and emerging battery technologies can be enabled by an improved understanding of the chemistry and working mechanisms of interphases that form at electrochemically active battery interfaces. However, it is difficult to collect and interpret spectra of interphases for several reasons, including the presence of a variety of compounds. To address this challenge, we herein present a vibrational spectroscopy and X-ray diffraction data library of ten compounds that have been identified as interphase constituents in lithium-ion or emerging battery chemistries. The data library includes attenuated total reflectance Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction data, collected in inert atmospheres provided by custom sample chambers. The data library presented in this work (and online repository) simplifies access to reference data that is otherwise either diffusely spread throughout the literature or non-existent, and provides energy storage researchers streamlined access to vital interphase-relevant data that can accelerate battery research efforts. Methods Data Collection Prior to any characterization, all pristine compounds were stored in an argon glovebox with base oxygen and water concentrations of ~0.1 ppm and ~0.5 ppm, respectively. The sources and purities of the studied chemicals are provided in Table 1 of the associated paper. ATR-FTIR Spectroscopy ATR-FTIR spectra were collected from 370 to 4000 cm-1 at a spectral resolution of 2 cm-1 using a Shimadzu IRTracer-100 instrument with an IRIS single reflection diamond accessory. Herein, we generally report data in the mid-IR range, with a low-energy cutoff of about 500 cm-1. This approach was taken because the mid-IR range is accessible to experimentalists and because we use some data below 500 cm-1 to aid in the generation of a baseline for subtraction (see Data Processing for further details). We encourage those particularly interested in data around and below ca. 450 cm-1 to consult the raw data available in the online data library. The ATR-FTIR instrument was housed in a nitrogen-filled glovebox with an oxygen concentration below 20 ppm. We note that to the best of our knowledge, none of the compounds in this study react with nitrogen at room temperature, the main offenders are oxygen and water, whose concentrations were analogous to levels in an Ar glovebox. Compounds were transferred into the ATR-FTIR enclosure in sealed vials and then immediately placed on a clean diamond crystal for the ATR-FTIR measurement. This transfer approach was effective at minimizing unwanted reactions as described in detail in the Technical Validation section below. Most data presented here is an average of 512 individual spectra (CH3COOLi, Li2CO3, 7LiF, 6LiF, Li2O, PEO) which was used to maximize the signal to noise ratio, while only 50 spectra were accumulated for some of the more reactive compounds (LiH, LiPF6, MnF2, NiF2) to minimize acquisition time and thereby reduce the likelihood of undesired reactions with trace amounts of oxygen or water. Raman Spectroscopy Raman spectra were collected using a 2 cm-square and 5 mm thick custom-made polyether ketone (PEEK) sample chamber with an optical window (2.5 cm-square and 1 mm thick glass microscope slide). The chamber, which kept samples in an inert argon environment during measurement, is illustrated in Fig. 1a. Prior to cell assembly, PEEK pieces and glass slides were sonicated with acetone and then isopropyl alcohol and baked at 40oC for at least 4 hours before being transferred into an argon glovebox for assembly. After each sample chamber was assembled, it was isolated in a heat-sealed bag before being transferred to a Renishaw Qontor microscope where Raman spectroscopy was conducted. A 488 nm excitation laser was used at a power ranging from 1 to 10 mW to collect data over 25 acquisitions from 100 to 3200 cm-1. An additional measurement of the lithium oxide sample was performed on the same instrument using a 633 nm laser (see Table 6). Unwanted contributions to the Raman spectra from the glass optical window were avoided by focusing the laser on the surface of the sample inside the chamber. X-Ray Diffraction The sample chambers used for XRD measurements were similarly assembled in an argon glovebox. Small quantities of each compound were placed on clean 2.5 cm-square and 1 mm thick glass microscope slides (cleaned and dried using the method described above) and covered with several sealing overlayers of polyimide tape (Kapton, Ted Pella, silicone adhesive, 70 µm thick) before being heat-sealed in individual plastic bags. The sample chambers were then transferred to a Bruker Phaser D2 instrument (wavelength, λ = 1.54 Å) where X-ray diffraction patterns were collected over a 2θ range of 10 to 90 degrees using an acquisition time of 0.2 seconds per step and a step size of 0.02 degrees per step. All samples remained in their sealed bags until right before the measurement was started. Kapton tape is not perfectly air-tight but was a sufficient barrier to enable the acquisition of data, which was completed within the first 15 minutes after the sample chambers were brought into ambient air. The XRD patterns were collected through the tape, rather than through the glass slide, to prevent significant XRD contributions from the glass. The relatively smooth XRD background from the amorphous tape was removed via processing as described in the Data Processing subsection. The Technical Validation section provides evidence that this approach successfully minimized unwanted reactions. Data Processing The collected raw data was processed to isolate features of the spectra and diffraction patterns that can be used to identify the presence of these compounds in complex data collected from interphases. Unwanted instrumental and background contributions were also removed through this processing. All FTIR measurements of inorganic compounds – and some organic ones – contained strong and broad absorption features below 600 cm-1 which increased in intensity as the wavenumber decreased. These features were so broad that the decreasing intensity side of the feature (e.g. see Palik and Hunter’s data on LiF in the Handbook of Optical Constants of Solids) was not observed above the instrument’s low-energy detection limit of 370 cm-1. Because most researchers have detectors with similar limits (or even higher in energy), the FTIR data was processed to focus on the mid-IR regions (above ca. 500 cm-1) that are most commonly accessible experimentally . As a result, low-wavenumber features (below our reporting window) were fit as part of the baseline when defining baseline profiles to be subtracted from the raw data across the entire measured range, even though they are technically not part of the background. Raw, unsubtracted data spanning the entire measured range is available in the data library. Panels 2a and 2b provide a representative baseline fitting of the raw data (which resulted in the removal of the downward-sloping part of the spectral feature that is observed below 600 cm-1) and the resulting subtracted data, respectively. Spikes in the Raman spectra attributable to cosmic ray excitation were removed immediately after collection and are not included in the raw data. Raw Raman spectra were processed through the subtraction of Gaussian and/or polynomial fits to eliminate background contributions that could have been caused by several phenomena including fluorescence, glass effects, and surface roughness. Because our instrument generated raw Raman data with unevenly spaced wavenumber values, we performed an interpolation was required to use a fast Fourier transform filter for data smoothing. Gaussian fits to determine peak positions from the data before and after interpolation confirmed that this transformation did not affect the location or shape of spectral features. Gaussian fits were used to subtract the amorphous background that the Kapton overlayer generated in XRD measurements. This background between 10 and 30 degrees appeared in all measurements through Kapton tape (but not in control measurements of bare metal foils) and was at lower 2θ than the first diffraction peak of most compounds, allowing subtraction of a consistent background in the few patterns (like that of lithium acetate) where the first diffraction peak was found below 30 degrees. This approach was deemed suitable because the there were no consistent and unidentifiable peaks in the processed XRD patterns which would have indicated a residual contribution from the Kapton overlayer. A fast Fourier filter was applied to reduce high frequency noise (with care taken to avoid distorting spectral features) and small vertical offsets were used in some cases to align the high-2θ baseline near zero. All data was normalized to take on values from 0 to 1. To facilitate comparison with data collected on other instruments, d-spacing values (calculated using Braggs Law with λ = 1.54 Å) are included in the online repository in addition to a 2θ x-axis.

This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format.

The data deposit includes results from 12 experiments that reacted carbon dioxide, seawater and limestone as a method of CO2 sequestration (as xlsx files). The data were obtained by Dr Huw Pullin, Cardiff University as part of a UKRI funded research project. Experiments were conducted under controlled temperatures (20degC), and CO2 pressures (5 and 50% v/v at 1 atm). The methods used are described in Xing et al., 2022 Chemical Engineering Journal. 431. 134096 DOI: 10.1016/j.cej.2021.134096

The NANOGrav 15-Year Data Set Public release "v2.0.1" 2025/01/30

OVERVIEW

This file contains "narrowband" and "wideband" TOAs and timing solutions for the NANOGrav 15-year data set, covering data taken from 2004 to mid-2020 using Arecibo, the Green Bank Telescope (GBT), and the Very Large Array (VLA) with ASP/GASP and PUPPI/GUPPI/YUPPI backend instrumentation. The observations, data reduction, and analysis procedures used to produce these data are described in detail in the accompanying paper, "The NANOGrav 15-year Data Set: Observations and Timing of 68 Millisecond Pulsars" (Agazie et al., 2023, ApJL 951 L9, DOI 10.3847/2041-8213/acda9a, arXiv:2306.16217).

This release is available at Zenodo (DOI 10.5281/zenodo.1477389).

All *.par and *.tim files are ASCII and are formatted for use with standard pulsar timing packages such as tempo2 and PINT, except for profile template files which are FITS format (narrowband) or python pickle files (wideband).

Correlation matrix files are available in three formats (*.txt, *.npz, and *.hdf5). See "description.txt" in both the narrowband and wideband ./correlations subdirectories for more details about these files.

Questions about the contents of this data set can be addressed to Joe Swiggum (swiggumj@gmail.com) or comments@nanograv.org.

DIRECTORY AND FILE STRUCTURE (FURTHER DETAILS BELOW)

./README
  This file.
./clock
  Files for tracing observatory-measured TOAs to clock standards.
./narrowband
  Directory containing the narrowband data set.
  Details are provided in README.narrowband in that directory.
./wideband
  Directory containing the wideband data set.
  Details are provided in README.wideband in that directory.
./correlations
  Directory containing the correlation matrix files for both the 
    narrowband and wideband data. Details are provided in the 
    /wideband/ and /narrowband/ subdirectories' description.txt 
    files. 

SOFTWARE

This data set requires up-to-date installations of PINT or tempo2. Our original analysis used PINT v0.9.1 and tempo2 v2022.01.1. Up to date versions of these packages, as well as usage information and documentation can be found at the following repositories:

PINT https://github.com/nanograv/PINT tempo2 https://bitbucket.org/psrsoft/tempo2

Note that we do not guarantee complete/correct functionality of these timing models in the older original tempo software package.

Please also ensure that the clock files you are using cover the full range of the data set. Using the provided clock files (see below) will ensure this.

All models included here are based on a generalized least squares (GLS) fit that includes a noise model with covariance between TOAs (ECORR/jitter parameters, if narrowband; RNAMP/RNIDX red noise parameters, if significant), as well as "traditional" EQUAD and EFAC parameters. Additional EFAC parameters for the wideband DM measurements are also included. All noise model parameters are included in the par files.

CLOCK FILES

The clock files used for our analysis are provided in the clock/ subdirectory. While the standard files distributed with tempo and tempo2 should be consistent with the clock files provided in the current release at the time of writing, this may be a source of inconsistent results in the future. Please see ./clock/README.clock directory for installation instructions.

PLANNED REVISIONS

The initial release of the data set contained all fundamental data products needed for pulsar timing analysis: Times of arrival (.tim files), timing models (.par files), standard template profiles, clock correction files, and noise modeling MCMC chains. In v2, parameter correlation matrices were added, as well as alternate versions of timing model parameter files ("NoRedNoise" and "predictive"). A future release will add a number of other useful derived products as mentioned in the paper, including post-fit timing residuals and dispersion measure time series.

CHANGE LOG

2023/09/19 Addition of "NoRedNoise" and "predictive" par files, correlation matrices, noise modeling chains (v2). 2023/07/01 Correction to tar.gz directory structure (v1.0.1). 2023/06/28 Initial public release (v1).

This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics.

KEK-KEKB Collider. Measurement of the cross sections and angular distributions for ETA ETA production in two-photon interactions for W values in the range 1.096 (threshold) to 3.3 GeV. The data cover the complete angular range for data below W of 2 GeV and |cos(theta*)|<0.9 elsewhere. The data represent an integrated luminosity of 383 fb-1 taken in and around the Upsilon region of 9.43 to 11.02 GeV in E+ E- collisions, which includes the largest part, 296 fb-1, at the Upsilon(4s). Numerical values have been supplied by S Uehara.

The data set is classified as "distortion simulation" and "distortion correction". In distortion simulation part, the file "GaAs_xmu.txt" is the K-edge XAS data of diluted GaAs plate collected at Beamline 4B9A of BSRF. The file "u_SiO2.txt" is the absorption cofficient of SiO2 in the range of 5000-13000 eV. the files "calxmu_0.1.txt" to "calxmu_2.0.txt" are the calculated K-edge XAS data under the capillary inner diameter in the range of 0.1 to 2.0. The calculation was based on the two files "GaAs_xmu.txt" and "u_SiO2.txt" as well as the python script provided in article's supporting information. The file "athena.prj" includes the preprocessing result of above XAS data, which can be opened using software "Athena". The files "artemis1.fpj" and "artemis2.fpj" include the EXAFS parameter fitting result of above data, which can be opened using software "Artemis". In distortion correction part, the files "GaAs-capillary-V0.76.txt", "GaAs-capillary-V1.00.txt" and "GaAs-capillary-V1.46.txt" are the K-edge XAS data of diluted GaAs powder filled in capillary with inner diameter 1 mm and the X-ray vertical size are 0.76, 1.00 and 1.46 seperately. All the data were collected at Beamline 4B9A of BSRF. The files "0.76_corrected.txt", "1.0_corrected.txt" and "1.46_corrected.txt" are the corrected K-edge XAS data. The correction was completed by the python script provided in article's supporting information. The file "athena.prj" includes the preprocessing result of above XAS data, which can be opened using software "Athena". The files "artemis1.fpj" and "artemis2.fpj" include the EXAFS parameter fitting result of above data, which can be opened using software "Artemis".

CERN-LHC. The invariant differential cross section of inclusive $\omega(782)$ meson production at midrapidity ($|y|<0.5$) in pp collisions at $\sqrt{s}=7$ TeV was measured with the ALICE detector at the LHC over a transverse momentum range of $2 < p_{\text{T}} < 17 \text{GeV}/c$. The $\omega$ meson was reconstructed via its $\omega\rightarrow\pi^+\pi^-\pi^0$ decay channel. The measured $\omega$ production cross section is compared to various calculations: PYTHIA 8.2 Monash 2013 describes the data, while PYTHIA 8.2 Tune 4C overestimates the data by about 50 percent. A recent NLO calculation, which includes a model describing the fragmentation of the whole vector-meson nonet, describes the data within uncertainties below $6$ GeV/c, while it overestimates the data by up to 50 percent for higher $ p_{\text{T}}$. The $\omega/\pi^0$ ratio is in agreement with previous measurements at lower collision energies and the PYTHIA calculations. In addition, the measurement is compatible with transverse mass scaling within the measured $p_{\text{T}}$~range and the ratio is constant with $C^{\omega/\pi^{0}}= 0.67 \pm 0.03 \text{(stat)} \pm 0.04 \text{(sys)}$ above a transverse momentum of $2.5$ GeV/$c$.

ORCID Public Data File 2024These files contain a snapshot of all public data in the ORCID Registry associated with an ORCID record that was created or claimed by an individual as of September 23, 2024. ORCID publishes this file once per year under a Creative Commons CC0 1.0 Universal public domain dedication. This means that, to the extent possible under law, ORCID has waived all copyright and related or neighbouring rights to the Public Data File. For more information on the file, see https://info.orcid.org/public-data-file-use-policy/The file contains the public information associated with each user's ORCID record. The data is available in XML format and is further divided into separate files for easier management. One file contains the full record summary for each record. The rest of the data is divided into 11 files which contain the activities for each record including full work data.Below is a more complete description of how the data is structured.Summaries file:ORCID_2024_10_summaries.tar.gzUncompressed size: 730,009 MBDescription: Contains all the existing summaries, when extracted, it will generate the following file structure: summaries/[3 digits checksum]/[iD].xmlExample: If you are looking for the summary of iD '0000-0002-7869-831X', decompress the file and you will find the summary under 'summaries/31X/0000-0002-7869-831X.xml'.Activities files:ORCID_2024_10_activities_0.tar.gzORCID_2024_10_activities_1.tar.gzORCID_2024_10_activities_2.tar.gzORCID_2024_10_activities_3.tar.gzORCID_2024_10_activities_4.tar.gzORCID_2024_10_activities_5.tar.gzORCID_2024_10_activities_6.tar.gzORCID_2024_10_activities_7.tar.gzORCID_2024_10_activities_8.tar.gzORCID_2024_10_activities_9.tar.gzORCID_2024_10_activities_X.tar.gzTotal uncompressed size: 3,141,554 MBDescription: Consists of 11 .tar.gz files, each file contains the public activities that belongs to an iD that contains a given checksum. The file hierarchy is as follows:[checksum]/[3 digits checksum]/[iD]/[activity type]/[iD]_[activity_type]_[putcode].xmlExamples:If you are looking for the public activities that belong to `0000-0002-7869-831X:Decompress the file 'ORCID_2024_10_activities_X.tar.gz'.You will find all the public activities under 'X/31X/0000-0002-7869-831X/' which are then sub-divided in folders for each activity type.If you are looking for all the employments that belong to '0000-0002-7869-831X':Decompress the file 'ORCID_2024_10_activities_X.tar.gz'Navigate to 'X/31X/0000-0002-7869-831X/employments'.If you are looking for the employment with put-code '7923980' that belongs to '0000-0002-7869-831X' :Decompress the file 'ORCID_2024_10_activities_X.tar.gz'.You will find that employment under 'X/31X/0000-0002-7869-831X/employments/0000-0002-7869-831X_employments_7923980.xml'.Companion Resources:ORCID Data Model2023 ORCID Public Data File2022 ORCID Public Data File2021 ORCID Public Data File2020 ORCID Public Data File2019 ORCID Public Data File2018 ORCID Public Data File2017 ORCID Public Data File2016 ORCID Public Data File2015 ORCID Public Data File2014 ORCID Public Data File2013 ORCID Public Data File

This repository hosts the dataset used in the LSSTC AGN Data Challenge (DC) 2021 (PI: Gordon Richards). More information about the data challenge can be found in the DC GitHub repository @ https://github.com/RichardsGroup/AGN_DataChallenge.

Dataset Versions:

1.0: The initial dataset used in the DC, as well as the blinded dataset (ObjectTable_Blinded.parquet) that was used to evaluate submissions. Note that the image cutouts are not included here due to the large size, but the script used to generate those cutouts using SDSS archive services is included in the DC GitHub repository.

1.1: The same dataset as in v1.0 but with the following updates:

Uncovered the true coordinates of each source in the dataset

Added E(B-V) for every source using the SFD1998 dust map

Added spectrum source information (i.e., SDSS fiber, plate, mjd) if available.

Caveat:

The optical (grizY) and NIR photometry of sources in the XMM-LSS field is a product of the HSC/VISTA pixel-level joint processing initiative led by Raphael Shirley and Manda Banerji. Thus, it is an early prototype dataset and is still subject to testing and characterization.

Citation:

The DC dataset released here is a compilation of data from various sources. If you find the DC dataset useful for your research and would like to acknowledge it, please also reference the original sources of the data. Below is a list of publications that you should consider citing.

X-ray in XMM-LSS (XMM-SERVS): 2018MNRAS.478.2132C

UV Photometry (GALEX): 2017ApJS..230...24B

Optical Photometry (in the object/source tables):

DES: 2021ApJS..255...20A

SDSS Stripe 82 Coadd: 2014ApJ...794..120A

HSC DR2: 2019PASJ...71..114A

Optical Light Curves (in the ForcedSource table):

SDSS DR7: 2009ApJS..182..543A

SDSS II Supernova Survey: 2008AJ....135..338F

Astrometry (i.e., parallax, proper motion):

Gaia EDR3: 2021A&A...649A...1G

NOIRLab Source Catalog DR2: 2021AJ....161..192N

NIR in XMM-LSS (VISTA/VIDEO): 2013MNRAS.428.1281J

NIR in Stripe 82 (UKIDSS):

2006MNRAS.367..454H

2007MNRAS.379.1599L

2008MNRAS.384..637H

2009MNRAS.394..675H

Optical u-band in XMM-LSS (CFHTLS): 2012yCat.2317....0H

MIR in XMM-LSS (Spitzer DeepDrill): 2021MNRAS.501..892L

MIR in Stripe 82 (SpIES): 2016ApJS..225....1T

FIR (Hershel/HELP): 2019MNRAS.490..634S

Radio (FIRST): 1994ASPC...61..165B

HighZ QSOs:

2016ApJ...819...24W

2016ApJ...829...33Y

SDSS Spectroscopy:

SDSS DR16: 2020ApJS..249....3A

SDSS DR16 Quasar Catalog: 2020ApJS..250....8L

This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests.

This publication offers the necessary data and scripts to replicate the findings of the article titled "Hidden resonances in non-Hermitian systems with scattering thresholds". Additionally convergence studies are provided. The article aims to offer a new perspective on resonances in the vicinity of scattering thresholds and provide access to hidden modes on different Riemann sheets.

Usage

All Matlab files can be run without solving scattering problems as the required data is stored in .mat files in the data directory. In order to run the simulations with JCMsuite you must delete the data directory and replace corresponding place holders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of JCMwave.

Requirements

• JCMsuite (tested with version 6.4.1)
• MATLAB (tested with version R2023b)

FEM convergence

We acquire the snapshots with the finite element method (FEM) solver JCMsuite. To estimate the error, the specular reflection has been collected at 24 equidistantly sampled points within the range of interest and at two additional sampling points on either side of the branch points (for further details we refer to the file convergence.m). The error is defined as \(\mathrm{min}\,\mathrm{abs}\left( R_0^n(\omega)-R_0^8(\omega)\right)\), where the superscript denotes the polynomial order of the FEM basis functions. Furthermore, the energy conservation (incoming energy minus reflection plus absorption) has been investigated.

All the data for the paper have been generated using \(n=5\). The error at the data points can therefore be expected to be below \(3\times10^{-7}\).

AAA convergence

The AAA algorithm adaptivly increases the degree \(m\) of the rational approximation until the error of the model with respect to all sample points falls below a given threshold \(t\) as long as \(m\) is smaller than half the number of sample points \(N\). We use \(t = 10^{-6}\) and \(t = 5\times 10^{-7}\) to make sure that it is larger than the error introduced through the FEM discretization. In the file AAAconvergence.m, error and model size are compared for different values of \(t\) and different numbers of support points. It can be observed that the error with respect to more than 500 reference points is smaller by orders of magnitude, while at the same time the size of the model is reduced and saturates quickly if the transformed variable \(\tilde{k}\) is used instead of \(k\). Here, 80 support points suffice for errors below \(10^{-6}\) for a spectrum containing three branch points and more than eight resonances (if hidden resonances are included).

Sampling scheme

We adopt a sampling scheme with additional samples in the vicinity of the branch points. This is achieved with equidistant samplings in the transformed space. For details we refer to the matlab scripts.