The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The Saudi Arabia POI Dataset is one of our worldwide POI datasets with over 98% coverage.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage is as follows:

Poi Field Data Coverage (%) poi_name 100 brand 2 poi_tel 54 formatted_address 100 main_category 100 latitude 100 longitude 100 neighborhood 1 source_url 21 email 2 opening_hours 55

The data may be visualized on a map at https://store.poidata.xyz/sa and a data sample may be downloaded at https://store.poidata.xyz/datafiles/sa_sample.csv

The data included in this publication depict the 2024 version of components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape.

National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. Additional methodology documentation is provided in a methods document (\Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.

The specific raster datasets in this publication include:

Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.

Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.

Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.

Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.

Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.

Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.

Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.

Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. The first edition of these data represented the first time wildfire risk to communities had been mapped nationally with consistent methodology. They provided foundational information for comparing the relative wildfire risk among populated communities in the United States. In this version, the 2nd edition, we use improved modeling and mapping methodology and updated input data to generate the current suite of products.See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information and an interactive web application for exploring some of the datasets published here. We deliver the data here as zip files by U.S. state (including AK and HI), and for the full extent of the continental U.S.

This data publication is a second edition and represents an update to any previous versions of Wildfire Risk to Communities risk datasets published by the USDA Forest Service. There are two companion data publications that are part of the WRC 2.0 data update: one that includes datasets of wildfire hazard and risk for populated areas of the nation, where housing units are currently present (Jaffe et al. 2024, https://doi.org/10.2737/RDS-2020-0060-2), and one that delineates wildfire risk reduction zones and provides tabular summaries of wildfire hazard and risk raster datasets (Dillon et al. 2024, https://doi.org/10.2737/RDS-2024-0030).

This dataset provides locations and technical specifications of wind turbines in the United States, almost all of which are utility-scale. Utility-scale turbines are ones that generate power and feed it into the grid, supplying a utility with energy. They are usually much larger than turbines that would feed a house or business. The regularly updated database contains wind turbine records that have been collected, digitized, and locationally verified. Turbine data were gathered from the Federal Aviation Administration's (FAA) Digital Obstacle File (DOF) and Obstruction Evaluation Airport Airspace Analysis (OE-AAA), American Clean Power (ACP) Association (formerly American Wind Energy Association (AWEA)), Lawrence Berkeley National Laboratory (LBNL), and the United States Geological Survey (USGS), and were merged and collapsed into a single dataset. Verification of the turbine positions was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. A locational error of plus or minus 10 meters for turbine locations was tolerated. Technical specifications for turbines were assigned based on the wind turbine make and models as provided by manufacturers and project developers directly, and via FAA datasets, information on the wind project developer or turbine manufacturer websites, or other online sources. Some facility and turbine information on make and model did not exist or was difficult to obtain. Thus, uncertainty may exist for certain turbine specifications. Similarly, some turbines were not yet built, not built at all, or for other reasons cannot be verified visually. Location and turbine specifications data quality are rated, and confidence is recorded for both. None of the data are field verified.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Two files contain the preliminary site data and water quality data required by the Bureau of Meteorology (BoM) under the conditions of the Water Act 2008. Please understand that in order to achieve these preliminary files, there has been quite a deal of work over a short amount of time. This has been greatly assisted by (and in fact would not have been possible without) the BoM’s financial assistance in terms of funding of Project NSW 6.1 - Remodelling, update and migration of the DECC water quality database. Note however, that due to the relatively short timeframe involved, a number of caveats still need to be placed on these preliminary files until a full QA/QC and data integrity and consistency check has been completed on the database. This is currently being implemented and it is recommended that additional contact is made with DECC prior to the release or use of this data. DECC will be continuing to refine and QA/QC this database and will inform BoM if this affects any data in these preliminary data files.

Some of this water data has been collected under an agreement with the Murray Darling Basin Commission (now the Murray Darling Authority). Part of this agreement deals with confidentiality regarding the identification of sites on individual landholder properties. In particular: “By providing locations at this (valley name or zone name only) accuracy there is reduced risk that future sampling at that location is confounded by intentional activities at the site. Types of impacts that might be envisaged include the unauthorised collection of rare or endangered fish or macroinvertebrate species at identifiable SRA sample sites, the undesired identification of SRA sites that exist on private property, comparisons of data collected at SRA sites to deduce some causal effect due to the landholding on which those sites exist and so on†. Any supply of/access to/reporting of this data should take such confidentialities into account.

With the Site data, latitude and longitudes or eastings and northings are still be checked/added for those sites without such data. An updated Site file will be forwarded to BoM once it is finalised.

Lastly, this data is supplied in good faith, exercising all due care and attention. No representation is made about the accuracy, completeness or suitability of the information for any particular purpose. DECC does not accept liability for any damage which may occur to any person or organization taking action or not on the basis of these data.

Dataset History

This data was provided to the Bureau of Meteorology under the water regulations from the NSW Department of Environment & Heritage

Dataset Citation

NSW - Department of Environment and Heritage (2009) NSW Department of Environment and Heritage Historic Water Quality Data. Bioregional Assessment Source Dataset. Viewed 07 April 2016, http://data.bioregionalassessments.gov.au/dataset/4c5f7318-2567-4614-aa35-46aa0eb045f2.

description: This data set provides industrial-scale onshore wind turbine locations, corresponding facility information, and turbine technical specifications, in the United States to March 2014. The database has nearly 49,000 wind turbine records that have been collected, digitized, locationally verified, and internally quality assured and quality controlled. Turbines from the Federal Aviation Administration Digital Obstacle File, product date March 2, 2014, were used as the primary source of turbine data points. Verification of the position of turbines was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacle Repository System (FAA ORS) numbers were visually identified and supplemental points were added to the collection. A locational error of plus or minus 10 meters for turbine positions was estimated. Wind farm facility names were identified from publicly available facility data sets. Facility names were then used in a web search of additional industry publications and press releases to attribute additional turbine information (such as manufacturer, model, and technical specifications of wind turbines). Wind farm facility location data from various wind and energy industry sources were used to search for and digitize turbines not in existing databases. Technical specifications assigned to were based on the make and model as described in literature, in the Federal Aviation Administration Digital Obstacle File, and information from the turbine manufacturers' websites. Some facility and turbine information did not exist or was difficult to obtain. Thus, uncertainty may be present. That uncertainty was rated and a confidence was recorded for both location and attribution data quality.; abstract: This data set provides industrial-scale onshore wind turbine locations, corresponding facility information, and turbine technical specifications, in the United States to March 2014. The database has nearly 49,000 wind turbine records that have been collected, digitized, locationally verified, and internally quality assured and quality controlled. Turbines from the Federal Aviation Administration Digital Obstacle File, product date March 2, 2014, were used as the primary source of turbine data points. Verification of the position of turbines was done by visual interpretation using high-resolution aerial imagery in ESRI ArcGIS Desktop. Turbines without Federal Aviation Administration Obstacle Repository System (FAA ORS) numbers were visually identified and supplemental points were added to the collection. A locational error of plus or minus 10 meters for turbine positions was estimated. Wind farm facility names were identified from publicly available facility data sets. Facility names were then used in a web search of additional industry publications and press releases to attribute additional turbine information (such as manufacturer, model, and technical specifications of wind turbines). Wind farm facility location data from various wind and energy industry sources were used to search for and digitize turbines not in existing databases. Technical specifications assigned to were based on the make and model as described in literature, in the Federal Aviation Administration Digital Obstacle File, and information from the turbine manufacturers' websites. Some facility and turbine information did not exist or was difficult to obtain. Thus, uncertainty may be present. That uncertainty was rated and a confidence was recorded for both location and attribution data quality.

A Derelict Site is defined in the Act as any land which detracts, or is likely to detract, to a material degree from the amenity, character or appearance of land in the neighbourhood of the land in question because of -(a) The existence of structures which are in a ruinous, derelict or dangerous condition(b) The neglected, unsightly or objectionable condition of the land or any structures on the land(c) The presence of litter, rubbish, debris or waste on the land.The Act places a duty on every owner and occupier of land to take all reasonable steps to ensure that the land does not become or continue to be a derelict site.Under the Act, the Council has the authority to:(a) Serve a Notice on the owner/occupier specifying works to be carried out to prevent or abate dereliction(b) Acquire by agreement or compulsorily any derelict site situated within its administrative area(c) Impose an annual levy on any derelict site, which is considered to be urban land, within its administrative area which stands entered on the Derelict Sites Register on the 1st January of that year. From January 2020, the levy shall be 7% of the market value of the land/site.To report a derelict site, contact the Enforcement and Licensing Section by email at info@sdublincoco.ie or by telephone at 01 4149000.

This hosted feature layer has been published in RI State Plane Feet NAD 83.Representative locations of structures and sites throughout Rhode Island. These data include addressed and unaddressed locations as well as occupied and unoccupied structures. These data were originally designed and developed for Rhode Island E 9-1-1 Uniform Emergency Telephone System (RI E 9-1-1) purposes. This dataset continues to be maintained to provide an accurate spatial reference for RI E 9-1-1 telecommunicators. Portions of this dataset were collected as early as 2001. Inaccuracies do exist in these data and are therefore under constant revision. Any discrepancies, inaccuracies or inconsistencies recognized in these data should be reported to the pertinent municipality who should alert RI E-911. Users are also encouraged to email ri911gis@akassociates911.com with any suggested updates for this actively maintained dataset.

This hosted view feature layer has been published in RI State Plane Feet NAD 83.For complete metadata record - https://www.rigis.org/datasets/edc::e-911-sites/aboutRepresentative locations of structures and sites coded as P9 Site type throughout Rhode Island. These data include addressed and unaddressed locations as well as occupied and unoccupied structures. These data were originally designed and developed for Rhode Island E 9-1-1 Uniform Emergency Telephone System (RI E 9-1-1) purposes. This dataset continues to be maintained to provide an accurate spatial reference for RI E 9-1-1 telecommunicators. Portions of this dataset were collected as early as 2001. Inaccuracies do exist in these data and are therefore under constant revision. Any discrepancies, inaccuracies or inconsistencies recognized in these data should be reported to the pertinent municipality who should alert RI E-911. Users are also encouraged to email ri911gis@akassociates911.com with any suggested updates for this actively maintained dataset.

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The Geoscience Australia (GA) Inorganic Geochemistry database (GEOCHEM) contains chemical analyses and analytical metadata from rocks and regolith materials. The majority of analysed samples are from mapping and sampling programs in Australia by GA and its predecessor organisations (BMR, AGSO), along with a considerable collection from the Australian Antarctic Territory. A small number of analyses exist from Papua New Guinea and offshore sampling programs. The data set is currently used for internal GA consumption and is served off an application within the GA portal. As an enhancement, this data would be altered in terms of its structure adding more information out of such analyses. In addition, the data would me made compliant following GGIC standards. The data would be published within internal GA as well as to external third parties, through OGC web services viz. WMS and WFS.

The World Bank working with EED Advisory Limited carried out the Burundi MTF energy survey to provide national-level insights into the status of access to electricity. The survey extended to identify 120 high-potential mini-grids sites in the country.

These shapefiles demonstrate;

i) 120 high-potential mini-grids sites with basic information including the number of health and education facilities in the community ii) Detailed household data of 6 villages among 120 potential mini-grids sites, including types of houses, the existence of lightning, solar, radio, tv, mobile, etc. iii) Detailed business data of 6villages among 120 potential mini-grids sites, including hours of operation for the business, the existence of lightning, solar, radio, tv, mobile, etc. iv) Potential customers data of 6 villages among 120 potential mini-grids sites, including the types of customers (household or business).

The format includes a zip file (which includes datasets of shapefile format and excel spreadsheets).

The statistic shows the number of data centers worldwide in 2015, 2017, and 2021. In 2017, it was estimated that the number of data centers globally had fallen to 8.4 million.

The Taymyr Peninsula constitutes the eastern delimitation of a possible Kara Sea basin ice sheet. The existence of such an ice sheet during the last global glacial maximum (LGM), i.e. during the Late Weichselian/Upper Zyryansk, is favoured by some Russian scientists. However, a growing number of studies point towards a more minimalistic view concerning the areal extent of Late Weichselian/Upper Zyryansk Siberian glaciation. Investigations carried out by us along the central Byrranga Mountains and in the Taymyr Lake basin south thereof, reject the possibility of a Late Weichselian/Upper Zyryansk glaciation of this area. Our conclusion is based on the following: Dating of a continuous lacustrine sediment sequence at Cape Sabler on the Taymyr Lake shows that it spans at least the period 39-17 ka BP. Even younger ages have been reported, suggesting that this lacustrine environment prevailed until shortly before the Holocene. The distribution of these sediments indicates the existence of a paleo-Taymyr lake reaching c. 60 m above present sea level. A reconnaissance of the central part of the Byrranga Mountains gave no evidence of any more recent glacial coverage. The only evidence of glaciation - an indirect one - is deltaic sequences around 100-120 m a.s.l., suggesting glacio-isostatic depression and a large input of glacial meltwater from the north. However, 14C and ESR datings of these marine sediments suggest that they are of Early Weichselian/Lower Zyryansk or older age. As they are not covered by till and show no glaciotectonic disturbances, they support our opinion that there was no Late Weichselian/Lower Zyryansk glaciation in this area. We thus suggest that the Taymyr Peninsula was most probably glaciated during the early part of the last glacial cycle (when there was only small- to mediumscale glaciation in Scandinavia), but not glaciated during the later part of that cycle (which had the maximum ice-sheet coverage over north-western Europe). This fits a climatic scenario suggesting that the Taymyr area, like most of Siberia, would come into precipitation shadow during times with large-scale ice-sheet coverage of Scandinavia and the rest of north-western Europe.

These data sets consist of digital data describing sites under the oversight of Maryland's Land Restoration Program (LRP). Within the LRP, three programs exist to investigate eligible properties with known or perceived controlled hazardous substance contamination, protect public health and the environment, accelerate cleanup of properties, and provide liability releases and finality to site cleanup: the Voluntary Cleanup Program (VCP), the Brownfields Initiative, and State Remediation Sites. This dataset describes and details each site relative to these programs.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://mdewin64.mde.state.md.us/arcgis/rest/services/MDE_LRP/LandRestorationProgram/MapServer/0

The Traffic Road Event Information System (TREIS) API lets you access real time data on events/incidents that affect traffic conditions across the network of national highways in New Zealand. An example of an event/incident could include road works, an accident, or weather related problems such as ice or snow. Note that this information is ONLY for notable events/incidents that may cause delays to road users or require caution, and only where the details have been verified by the Transport Agency or another official source.The TREIS API makes available three sets of data:Events / incidentsThis is a detailed list of confirmed events/incidents on the state highway with a specified location. For example, caution advised due to a land slip blocking one lane of traffic.General warnings – North IslandThese are the general warning messages that apply to a wider geographic area within the North Island. For example, widespread flooding and slips in the Hawke’s Bay area.General warnings – South IslandThe same as 2, but only for the South Island. For example, 'Otago And Southland winter conditions exist And motorists are advised to watch for ice and grit in shaded areas and on bridge decks and to carry chains especially when travelling the Alpine passes'.Additional detailsMore detail is available in 'NZ TREIS web services' which covers:specification of the TREIS web services API; andInformation on how to connect to the TREIS web services API.NZ TREIS web services [PDF 117 KB]This document is intended for data architects and developers that must integrate their applications with the TREIS web services API.

An open-source replication of the WebText dataset from OpenAI.

Tidal observers at primary tide gauges of the United States Coast and Geodetic Survey (now the NOAA National Ocean Service) routinely measured seawater temperature and density throughout most of the 20th century with select sites well before. All available records for 26 sites have been digitized, although more paper file records may still exist for others. Students at Florida Institute of Technology provided the key entry and quality control. The purpose was to study trends in sea water temperature and density in support of increasing the scientific understanding of low-frequency changes across a wide spatial domain of the coastal United States.