This dataset includes links to the PoroTomo DAS data in both SEG-Y and hdf5 (via h5py and HSDS with h5pyd) formats with tutorial notebooks for use. Data are hosted on Amazon Web Services (AWS) Simple Storage Service (S3) through the Open Energy Data Initiative (OEDI). Also included are links to the documentation for the dataset, Jupyter Notebook tutorials for working with the data as it is stored in AWS S3, and links to data viewers in OEDI for the horizontal (DASH) and vertical (DASV) DAS datasets. Horizontal DAS (DASH) data collection began 3/8/16, paused, and then started again on 3/11/2016 and ended 3/26/2016 using zigzag trenched fiber optic cabels. Vertical DAS (DASV) data collection began 3/17/2016 and ended 3/28/16 using a fiber optic cable through the first 363 m of a vertical well. These are raw data files from the DAS deployment at (DASH) and below (DASV) the surface during testing at the PoroTomo Natural Laboratory at Brady Hot Spring in Nevada. SEG-Y and hdf5 files are stored in 30 second files organized into directories by day. The hdf5 files available via HSDS are stored in daily files. Metadata includes information on the timing of recording gaps and a file count is included that lists the number of files from each day of recording. These data are available for download without login credentials through the free and publicly accessible Open Energy Data Initiative (OEDI) data viewer which allows users to browse and download individual or groups of files.

Over the last two observations, the revenue is forecast to significantly increase in all regions. From the selected regions, the ranking by revenue in the data center market is forecast to be led by Central & Western Europe with ***** billion euro. In contrast, the ranking is trailed by Eastern Europe with **** billion euro, recording a difference of ***** billion euro to Central & Western Europe. The Statista Market Insights cover a broad range of additional markets.

Overview This dataset was produced from the raw sodar .mnd files from the Lubbock, TX site during the WFIP1 campaign. Quality control and formatting have been applied to transform the numerous raw files into a single file to provide user friendliness and improved wind resource characterization at this location. Data Details Location: 33.60279, -102.02821 Elevation: 1017 m Output heights: Every 5 meters from 10 meters to 300 meters Data Quality Data from the raw files were filtered according to the following automated and manual procedures. Missing and rejected values were flagged as -999. High precipitation events as suggested by the vertical velocity values were subjected to quality control. If any vertical velocity value at any height for a given timestamp fell below a -1.5 m/s threshold, all variables at all heights at that timestamp were rejected. On a height-by-height basis, if the signal-to- noise ratio (SNR) for any of the u, v, or w wind components reached 9 or below, all variables for that height and timestamp were rejected. The raw files were also screened for nonphysical values such as wind speeds less than zero and directions outside 0-360 degrees. Finally, the data were visually examined for events of atypical sodar retrievals, such as excessive magnitudes in oscillations or periods of stagnancy.

This is the latest statistical publication of linked HES (Hospital Episode Statistics) and DID (Diagnostic Imaging Dataset) data held by the Health and Social Care Information Centre. The HES-DID linkage provides the ability to undertake national (within England) analysis along acute patient pathways to understand typical imaging requirements for given procedures, and/or the outcomes after particular imaging has been undertaken, thereby enabling a much deeper understanding of outcomes of imaging and to allow assessment of variation in practice. This publication aims to highlight to users the availability of this updated linkage and provide users of the data with some standard information to assess their analysis approach against. The two data sets have been linked using specific patient identifiers collected in HES and DID. The linkage allows the data sets to be linked from April 2012 when the DID data was first collected; however this report focuses on patients who were present in either data set for the period April 2015-February 2016 only. For DID this is provisional 2015/16 data. For HES this is provisional 2015/16 data. The linkage used for this publication was created on 06 June 2016 and released together with this publication on 07 July 2016.

This is version v3.3.0.2022f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data. The quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. The data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format "station_code"_HadISD_HadOBS_19310101-20230101_v3.3.1.2022f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height. To keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS. For more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/ References: When using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the "citable as" reference) : Dunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note. Dunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016. Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012 Smith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1 For a homogeneity assessment of HadISD please see this following reference Dunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. "Pairwise homogeneity assessment of HadISD." Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.

The data set description provides a detail account of the type of data that is used within the peer-reviewed literature. The data involves special instrumentation, such as hyperspectral imaging cameras to develop thousands of pixels, which form images, like on a television screen. Other data is used to develop absorbance spectra from infrared spectrometers and compared to reference data to confirm the presence of a desired, tested chemical. This dataset is associated with the following publication: Baseley, D., L. Wunderlich, G. Phillips, K. Gross, G. Perram, S. Willison, M. Magnuson, S. Lee, R. Phillips, and W. Harper Jr.. Hyperspectral Analysis for Standoff Detection of Dimethyl Methylphosphonate on Building Materials [HS7.52.01]. JOURNAL OF ENVIRONMENTAL MANAGEMENT. Elsevier Science Ltd, New York, NY, USA, 135-142, (2016).

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

This time series dataset includes viral COVID-19 laboratory test [Polymerase chain reaction (PCR)] results from over 1,000 U.S. laboratories and testing locations including commercial and reference laboratories, public health laboratories, hospital laboratories, and other testing locations. Data are reported to state and jurisdictional health departments in accordance with applicable state or local law and in accordance with the Coronavirus Aid, Relief, and Economic Security (CARES) Act (CARES Act Section 18115).

Data are provisional and subject to change.

Data presented here is representative of diagnostic specimens being tested - not individual people - and excludes serology tests where possible. Data presented might not represent the most current counts for the most recent 3 days due to the time it takes to report testing information. The data may also not include results from all potential testing sites within the jurisdiction (e.g., non-laboratory or point of care test sites) and therefore reflect the majority, but not all, of COVID-19 testing being conducted in the United States.

Sources: CDC COVID-19 Electronic Laboratory Reporting (CELR), Commercial Laboratories, State Public Health Labs, In-House Hospital Labs

Data for each state is sourced from either data submitted directly by the state health department via COVID-19 electronic laboratory reporting (CELR), or a combination of commercial labs, public health labs, and in-house hospital labs. Data is taken from CELR for states that either submit line level data or submit aggregate counts which do not include serology tests.

1: The Interstellar Boundary Explorer (IBEX) has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 16 releases of IBEX-HI and/or IBEX-LO data covering 2009-2019. 2: This data set is from the Release 7 (1 year-cadence) IBEX-Hi map data for the years 2009-2013 in the form of ram-directional ENA (hydrogen) fluxes with Compton-Getting correction (cg) of flux spectra for spacecraft motion and correction for ENA survival probability (sp) between 1 and 100 AU. 3. The data consist of all-sky maps in Solar Ecliptic Longitude (east and west) and Latitude angles for ENA (hydrogen) fluxes from IBEX-Hi energy bands 2-6 in numerical data form. Energy channels 2-6 have FWHM ranges of 0.52-0.95, 0.84-1.55, 1.36-2.50, 1.99-3.75, 3.13-6.00 keV, respectively. The corresponding center-point energies are 0.71, 1.11, 1.74, 2.73, and 4.29 keV. Details of the data and enabled science from Release 10 are given in the following journal publication: 4: McComas, D. J., et al. (2017), Seven Years of Imaging the Global Heliosphere with IBEX, Astrophys. J. Supp. Ser., 229(2), 41 (32 pp.), 5: http://doi.org/10.3847/1538-4365/aa66d8 6. The following codes are used to define dataset types:- cg = Compton-Getting corrections have been applied to the data to account for the speed of the spacecraft relative to the direction of arrival of the ENAs.- nocg = no Compton-Getting corrections- sp = survival probability corrections have been applied to the data to account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they stream through the heliosphere. This correction scales the data out from IBEX at 1 AU to ~100 AU. In the original data this mode is denoted as Tabular.- noSP - no survival probability corrections have been applied to the data.- omni = data from all directions.- ram = data was collected when the spacecraft was ramming into the incoming ENAs.- antiram = data was collected when the spacecraft was moving away from the incoming ENAs. 7. The following list associates Release 16 map numbers (1-22) with mission year (1-9), orbits (11-471b), and dates (12/25/2008-12/26/2019):- Map 1: Map2009A, year 1, orbits 11-34, dates 12/25/2008-06/25/2009- Map 2: Map2009B, year 1, orbits 35-58, dates 06/25/2009-12/25/2009- Map 3: Map2010A, year 2, orbits 59-82, dates 12/25/2009-06/26/2010- Map 4: Map2010B, year 2, orbits 83-106, dates 06/26/2010-12/26/2010- Map 5: Map2011A, year 3, orbits 107-130a, dates 12/26/2010-06/25/2011- Map 6: Map2011B, year 3, orbits 130b-150a, dates 06/25/2011-12/24/2011- Map 7: Map2012A, year 4, orbits 150b-170a, dates 12/24/2011-06/22/2012- Map 8: Map2012B, year 4, orbits 170b-190b, dates 06/22/2012-12/26/2012- Map 9: Map2013A, year 5, orbits 191a-210b, dates 12/26/2012-06/26/2013- Map 10: Map2013B, year 5, orbits 211a-230b, dates 06/26/2013-12/26/2013- Map 11: Map2014A, year 6, orbits 231a-250b, dates 12/26/2013-06/26/2014- Map 12: Map2014B, year 6, orbits 251a-270b, dates 06/26/2014-12/24/2014- Map 13: Map2015A, year 7, orbits 271a-290b, dates 12/24/2014-06/24/2015- Map 14: Map2015B, year 7, orbits 291a-310b, dates 06/24/2015-12/23/2015- Map 15: Map2016A, year 8, orbits 311a-330b, dates 12/24/2015-06/23/2016- Map 16: Map2016B, year 8, orbits 331a-351a, dates 06/24/2016-12/26/2016- Map 17: Map2017A, year 9, orbits 351b-371a, dates 12/26/2016-06/24/2017- Map 18: Map2017B, year 9, orbits 371b-391a, dates 06/25/2017-12/25/2017- Map 19: Map2018A, year 10, orbits 391b-411b, dates 12/25/2017-06/28/2018- Map 20: Map2018B, year 10, orbits 412a-431b, dates 06/29/2018-12/26/2018- Map 21: Map2019A, year 11, orbits 432a-451b, dates 12/27/2018-06/27/2019- Map 22: Map2019B, year 11, orbits 452a-471b, dates 06/28/2019-12/26/2019* 8: This particular data set, denoted in the original ascii files as hvset_tabular_ram_cg_yearN for N=1,5, includes pixel map data from ram direction (ram-directional), CG, SP, 1 year cadence.

United States Unemployment Rate: Tennessee data was reported at 3.700 % in Oct 2018. This stayed constant from the previous number of 3.700 % for Sep 2018. United States Unemployment Rate: Tennessee data is updated monthly, averaging 5.900 % from Jan 1976 (Median) to Oct 2018, with 514 observations. The data reached an all-time high of 13.800 % in Jan 1983 and a record low of 2.800 % in Apr 2018. United States Unemployment Rate: Tennessee data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G057: Unemployment Rate: By State.

United States US: Number of Births data was reported at 4,413,478.000 Person in 2050. This records an increase from the previous number of 4,397,629.000 Person for 2049. United States US: Number of Births data is updated yearly, averaging 4,195,844.000 Person from Jun 2001 (Median) to 2050, with 50 observations. The data reached an all-time high of 4,413,478.000 Person in 2050 and a record low of 3,921,308.000 Person in 2013. United States US: Number of Births data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.US Census Bureau: Demographic Projection.

This data set contains NASA DC-8 SAGAAERO Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 22 June 2012. These merge files were updated by NASA. The data have been merged to SAGAAero file timeline. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. In addition, a "grand merge" has been provided. This includes data from all the individual merged flights throughout the mission. This grand merge will follow the following naming convention: "dc3-mrgSAGAAero-dc8_merge_YYYYMMdd_R*_thruYYYYMMdd.ict" (with the comment "_thruYYYYMMdd" indicating the last flight date included). This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

These instructional videos walk users through the portal and its different features.

The Data Classification Market Report is Segmented by Component (Software and Services), Classification Method (Content-Based, Context-Based, and More), Organization Size (Large Enterprises and Small and Medium Enterprises (SMEs)), Application (Access Control and IAM, Governance and Compliance, and More), Industry Vertical (BFSI, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This is a graphic representation of the data stewards for the Public Land Survey System (PLSS). For BLM data sets the the data steward is identifed at the township level or smaller area The Data Steward is agency that will be responsible for updates of the PLSS. In the shared environment of the Natioanl Spatial Data Infrastructure (NSDI) the data steward for an area is the primary coordinator or agency responsible for making updates or causing updates to be made. Any questions about data content should be directed to the data steward.

Data Entry Service Market Outlook

The global data entry service market size is poised to experience significant growth, with the market expected to rise from USD 2.5 billion in 2023 to USD 4.8 billion by 2032, achieving a Compound Annual Growth Rate (CAGR) of 7.5% over the forecast period. This growth can be attributed to several factors including the increasing adoption of digital technologies, the rising demand for data accuracy and integrity, and the need for businesses to manage vast amounts of data efficiently.

One of the key growth factors driving the data entry service market is the rapid digital transformation across various industries. As businesses continue to digitize their operations, the volume of data generated has increased exponentially. This data needs to be accurately entered, processed, and managed to derive meaningful insights. The demand for data entry services has surged as companies seek to outsource these non-core activities, enabling them to focus on their primary business operations. Additionally, the widespread adoption of cloud-based solutions and big data analytics has further fueled the demand for efficient data management services.

Another significant driver of market growth is the increasing need for data accuracy and integrity. Inaccurate or incomplete data can lead to poor decision-making, financial losses, and a decrease in operational efficiency. Organizations are increasingly recognizing the importance of maintaining high-quality data and are investing in data entry services to ensure that their databases are accurate, up-to-date, and reliable. This is particularly crucial for industries such as healthcare, BFSI, and retail, where precise data is essential for regulatory compliance, customer relationship management, and operational efficiency.

The cost-effectiveness of outsourcing data entry services is also contributing to market growth. By outsourcing these tasks to specialized service providers, organizations can save on labor costs, reduce operational expenses, and improve productivity. Service providers often have access to advanced tools and technologies, as well as skilled professionals who can perform data entry tasks more efficiently and accurately. This not only leads to cost savings but also allows businesses to reallocate resources to more strategic activities, driving overall growth.

From a regional perspective, the Asia Pacific region is expected to witness the highest growth in the data entry service market during the forecast period. This can be attributed to the region's strong IT infrastructure, the presence of numerous outsourcing service providers, and the growing adoption of digital technologies across various industries. North America and Europe are also significant markets, driven by the high demand for data management services in sectors such as healthcare, BFSI, and retail. The Middle East & Africa and Latin America are anticipated to experience steady growth, supported by increasing investments in digital infrastructure and the rising awareness of the benefits of data entry services.

Service Type Analysis

The data entry service market can be segmented into various service types, including online data entry, offline data entry, data processing, data conversion, data cleansing, and others. Each of these service types plays a crucial role in ensuring the accuracy, integrity, and usability of data. Online data entry services involve entering data directly into an online system or database, which is essential for real-time data management and accessibility. This service type is particularly popular in industries such as e-commerce, where timely and accurate data entry is critical for inventory management and customer service.

Offline data entry services, on the other hand, involve entering data into offline systems or databases, which are later synchronized with online systems. This service type is often used in industries where internet connectivity may be unreliable or where data security is a primary concern. Offline data entry is also essential for processing historical data or data that is collected through physical forms and documents. The demand for offline data entry services is driven by the need for accurate and timely data entry in sectors such as manufacturing, government, and healthcare.

Data processing services involve the manipulation, transformation, and analysis of raw data to produce meaningful information. This includes tasks such as data validation, data sorting, data aggregation, and data analysis. Data processing is a critical componen

United States Avg Days on Market: Townhouse: Gadsden, AL data was reported at 102.000 Day in May 2020. This records an increase from the previous number of 32.000 Day for Oct 2019. United States Avg Days on Market: Townhouse: Gadsden, AL data is updated monthly, averaging 111.000 Day from Apr 2013 (Median) to May 2020, with 21 observations. The data reached an all-time high of 337.000 Day in Jan 2016 and a record low of 20.000 Day in Aug 2015. United States Avg Days on Market: Townhouse: Gadsden, AL data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB006: Average Days on Market: by Metropolitan Areas.

The data science and predictive analytics market size was over USD 19.07 billion in 2024 and is projected to reach USD 179.05 billion by 2037, witnessing around 18.8% CAGR during the forecast period i.e., between 2025-2037. North America industry is estimated to dominate majority revenue share of 35% by 2037, on account of high rate of adoption of cutting-edge technology in the region.

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 data set describes ground-water regions in the United States defined by the U.S. Geological Survey. These ground-water regions are useful for dividing the United States into areas of roughly similar hydrologic characterstics and water-use patterns. Most of these regions are very generalized and were developed from a illustration published at a scale of approximately 1:20 million. The data set also includes polygon features for unconsolidated watercourses taken from 1:7,500,000-scale U.S. Geological Survey map of productive aquifers.

This is the documentation of the tomographic X-ray data of a dynamic cross phantom made available at http://www.fips.fi/dataset.php. The data can be freely used for scientific purposes with appropriate references to the data and to this document in http://arxiv.org/. The data set consists of (1) the X-ray sinogram with 16 or 30 time frames (depending on resolution) of 2D slices of the cross phantom, made by crossing aluminum and graphite sticks in melted candle wax and (2) the corresponding static and dynamic measurement matrices modeling the linear operation of the X-ray transform. Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.