Spain Job Offers Scraped Data

Uncovering Qualifications and Requirements

By [source]

About this dataset

This dataset contains valuable web scraping information about job offers located in Spain, and gives details such as the offer name, company, location, and time of offer to potential employers. Having this knowledge is incredibly beneficial for any job seeker looking to target potential employers in Spain, understand the qualifications and requirements needed to be considered for a role and know approximately how long an offer is likely to stay on Linkedin. This dataset can also be extremely useful for recruiters who need a detailed overview of all job offers currently active in the Spanish market in order to filter out relevant vacancies. Lastly, professionals who have an eye on the Spanish job market can especially benefit from this dataset as it provides useful insights that can help optimise their search even more. This dataset consequently makes it easy for users interested in uncovering opportunities within Spain’s labour landscape with access detailed information about current job opportunities at their fingertips

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This guide will help those looking to use this dataset to discover the job market in Spain. The data provided in the dataset can be a great starting point for people who want to optimize their job search and uncover potential opportunities available.

• Understand What Is Being Measured:The dataset contains details such as a job offer name, company, and location along with other factors such as time of offer and type of schedule asked. It is important to understand what each column represents before using the data set.
• Number of Job Offers Available:This dataset provides an insight on how many job offers are available throughout Spain by showing which areas have a high number of jobs listed and what types of jobs are needed in certain areas or businesses. This information could be used for expanding your career or for searching for specific jobs within different regions in Spain that match your skillset or desired salary range .
• Required Qualifications & Skill Set:The type of schedule being asked by businesses is also mentioned, allowing users to understand if certain employers require multiple shifts, weekend work or hours outside the normal 9 - 5 depending on positions needed within companies located throughout the country . Additionally, understanding what skills sets are required not only quality you prioritize when learning new technologies or gaining qualifications but can give you an idea about what other soft skills may be required by businesses like team work , communication etc..
• Location Opportunities:This web scraping list allows users to gain access into potential companies located throughout Spain such as Madrid , Barcelona , Valencia etc.. By understanding where business demand exists across different regions one could look at taking up new roles with higher remuneration , specialize more closely in recruitments/searches tailored specifically towards various regions around Spain .

By following this guide, you should now have a robust understanding about how best utilize this dataset obtained from UOC along with an increased knowledge on identifying job opportunities available through webscraping for those seeking work experience/positions across multiple regions within the country

Research Ideas

• Analyzing the job market in Spain - Companies offering jobs can be compared and contrasted using this dataset, such as locations of where they are looking to hire, types of schedules they offer, length of job postings, etc. This information can let users to target potential employers instead of wasting time randomly applying for jobs online.
• Optimizing a Job Search- Web scraping allows users to quickly gather job postings from all sources on a daily basis and view relevant qualifications and requirements needed for each post in order to better optimize their job search process.
• Leveraging data insights – Insights collected by analyzing this web scraping dataset can be used for strategic advantage when creating LinkedIn or recruitment campaigns targeting Spanish markets based on the available applicants’ preferences – such as hours per week or area/position within particular companies typically offered in the datas set available from UOC

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

L...

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Dataset Card for Dataset Name

  Dataset Summary

[More Information Needed]

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation





  Curation Rationale

[More Information Needed]

  Source Data… See the full description on the dataset page: https://huggingface.co/datasets/gradio/new_saving_json.

Following my FOI Request Final Response 23371 can you please provide me with the same information by individual contractor code by month for the period January 22-Mar 22: Contractor Total NIC by month Individual sku’s dispensed by month with description, code and value (NIC) for each contractor code. Response Stock Keeping Unit (SKU) In the past we have not addressed your use of this term in our official responses to this repeated request, although we have discussed this in a clarification e-mail exchange. Please note that our data warehouse does not hold the SKU for dispensed products. Therefore, this information is not held. Remaining Information A copy of the information you have requested has been published on our website at https://opendata.nhsbsa.net/dataset/foi-25508 In some cases, in Microsoft Excel, E is interpreted in text as scientific notation, for example 01E00 shown as 1.00E+00. Please see the following guidance in these circumstances: • Open a new Excel – go to Data – From Text – select the csv file you require – leave format as delimited – next – Comma – next – highlight the CCG column – change to text – highlight column with the odd formatting – change to text – finish Time Period: January 2022 to March 2022 inclusive. Data Source: NHSBSA Information Services Data Warehouse

Python Code Instruction

Training Data with Instruction, Input, Output, and Prompt Columns

By Tarun Bisht (From Huggingface) [source]

About this dataset

The python_code_instructions_18k_alpaca dataset is a comprehensive training dataset specifically curated for researchers and developers involved in the analysis and comprehension of Python code instructions. It contains a vast collection of Python code snippets along with their corresponding instruction, input, output, and prompt information. By utilizing this dataset, users can gain valuable insights into various Python programming concepts and techniques.

The dataset is organized into columns to facilitate easy access to the required information. The instruction column holds the specific task or instruction that the Python code snippet is designed to perform. This allows users to understand the purpose or requirement of each code snippet at a glance.

The input column contains all necessary input data or parameters that are required for executing the Python code snippet accurately. These inputs provide context and enable users to comprehend how different variables or values impact the overall functioning of each code snippet.

Likewise, the output column presents expected results or outcomes that should be produced when executing each Python code snippet with its specified input values. This allows for validation and verification purposes, ensuring that each code snippet performs as intended.

In addition to instruction, input, and output details, this dataset also includes prompts. The prompt column provides additional context or information intended to assist users in better understanding the purpose or requirements of each particular Python code snippet.

By leveraging this comprehensive python_code_instructions_18k_alpaca training dataset, researchers and developers can delve into numerous real-world examples of Python programming challenges - helping them enhance their coding skills while gaining invaluable knowledge about effective implementation techniques across various domains

Research Ideas

• Code Instruction Analysis: This dataset can be used to analyze different types of Python code instructions and identify patterns or common practices. Researchers or developers can use this dataset to gain insights into effective ways of writing code instructions.
• Code Output Prediction: With the given input and instruction, this dataset can be used to train models for predicting the expected output of a Python code snippet. This can be useful in automating the testing process or verifying the correctness of the code.
• Prompt Generation: Developers often struggle with providing clear and concise prompts for their code snippets. This dataset can serve as a resource for generating prompts by analyzing existing examples and extracting key information or requirements from them

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:----------------|:------------------------------------------------------------------------------------------------------------------| | instruction | Specific tasks or instructions assigned to each Python code snippet. (Text) | | input | The input data or parameters required for executing the code instruction. (Text) | | output | The expected result or output that should be produced when executing the code instruction. (Text) | | prompt | Additional information or context to help understand the purpose or requirements of each code instruction. (Text) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Tarun Bisht (From Huggingface).

Ethical Data ManagementExecutive SummaryIn the age of data and information, it is imperative that the City of Virginia Beach strategically utilize its data assets. Through expanding data access, improving quality, maintaining pace with advanced technologies, and strengthening capabilities, IT will ensure that the city remains at the forefront of digital transformation and innovation. The Data and Information Management team works under the purpose:“To promote a data-driven culture at all levels of the decision making process by supporting and enabling business capabilities with relevant and accurate information that can be accessed securely anytime, anywhere, and from any platform.”To fulfill this mission, IT will implement and utilize new and advanced technologies, enhanced data management and infrastructure, and will expand internal capabilities and regional collaboration.Introduction and JustificationThe Information technology (IT) department’s resources are integral features of the social, political and economic welfare of the City of Virginia Beach residents. In regard to local administration, the IT department makes it possible for the Data and Information Management Team to provide the general public with high-quality services, generate and disseminate knowledge, and facilitate growth through improved productivity.For the Data and Information Management Team, it is important to maximize the quality and security of the City’s data; to develop and apply the coherent management of information resources and management policies that aim to keep the general public constantly informed, protect their rights as subjects, improve the productivity, efficiency, effectiveness and public return of its projects and to promote responsible innovation. Furthermore, as technology evolves, it is important for public institutions to manage their information systems in such a way as to identify and minimize the security and privacy risks associated with the new capacities of those systems.The responsible and ethical use of data strategy is part of the City’s Master Technology Plan 2.0 (MTP), which establishes the roadmap designed by improve data and information accessibility, quality, and capabilities throughout the entire City. The strategy is being put into practice in the shape of a plan that involves various programs. Although these programs was specifically conceived as a conceptual framework for achieving a cultural change in terms of the public perception of data, it basically covers all the aspects of the MTP that concern data, and in particular the open-data and data-commons strategies, data-driven projects, with the aim of providing better urban services and interoperability based on metadata schemes and open-data formats, permanent access and data use and reuse, with the minimum possible legal, economic and technological barriers within current legislation.Fundamental valuesThe City of Virginia Beach’s data is a strategic asset and a valuable resource that enables our local government carry out its mission and its programs effectively. Appropriate access to municipal data significantly improves the value of the information and the return on the investment involved in generating it. In accordance with the Master Technology Plan 2.0 and its emphasis on public innovation, the digital economy and empowering city residents, this data-management strategy is based on the following considerations.Within this context, this new management and use of data has to respect and comply with the essential values applicable to data. For the Data and Information Team, these values are:Shared municipal knowledge. Municipal data, in its broadest sense, has a significant social dimension and provides the general public with past, present and future knowledge concerning the government, the city, society, the economy and the environment.The strategic value of data. The team must manage data as a strategic value, with an innovative vision, in order to turn it into an intellectual asset for the organization.Geared towards results. Municipal data is also a means of ensuring the administration’s accountability and transparency, for managing services and investments and for maintaining and improving the performance of the economy, wealth and the general public’s well-being.Data as a common asset. City residents and the common good have to be the central focus of the City of Virginia Beach’s plans and technological platforms. Data is a source of wealth that empowers people who have access to it. Making it possible for city residents to control the data, minimizing the digital gap and preventing discriminatory or unethical practices is the essence of municipal technological sovereignty.Transparency and interoperability. Public institutions must be open, transparent and responsible towards the general public. Promoting openness and interoperability, subject to technical and legal requirements, increases the efficiency of operations, reduces costs, improves services, supports needs and increases public access to valuable municipal information. In this way, it also promotes public participation in government.Reuse and open-source licenses. Making municipal information accessible, usable by everyone by default, without having to ask for prior permission, and analyzable by anyone who wishes to do so can foster entrepreneurship, social and digital innovation, jobs and excellence in scientific research, as well as improving the lives of Virginia Beach residents and making a significant contribution to the city’s stability and prosperity.Quality and security. The city government must take firm steps to ensure and maximize the quality, objectivity, usefulness, integrity and security of municipal information before disclosing it, and maintain processes to effectuate requests for amendments to the publicly-available information.Responsible organization. Adding value to the data and turning it into an asset, with the aim of promoting accountability and citizens’ rights, requires new actions, new integrated procedures, so that the new platforms can grow in an organic, transparent and cross-departmental way. A comprehensive governance strategy makes it possible to promote this revision and avoid redundancies, increased costs, inefficiency and bad practices.Care throughout the data’s life cycle. Paying attention to the management of municipal registers, from when they are created to when they are destroyed or preserved, is an essential part of data management and of promoting public responsibility. Being careful with the data throughout its life cycle combined with activities that ensure continued access to digital materials for as long as necessary, help with the analytic exploitation of the data, but also with the responsible protection of historic municipal government registers and safeguarding the economic and legal rights of the municipal government and the city’s residents.Privacy “by design”. Protecting privacy is of maximum importance. The Data and Information Management Team has to consider and protect individual and collective privacy during the data life cycle, systematically and verifiably, as specified in the general regulation for data protection.Security. Municipal information is a strategic asset subject to risks, and it has to be managed in such a way as to minimize those risks. This includes privacy, data protection, algorithmic discrimination and cybersecurity risks that must be specifically established, promoting ethical and responsible data architecture, techniques for improving privacy and evaluating the social effects. Although security and privacy are two separate, independent fields, they are closely related, and it is essential for the units to take a coordinated approach in order to identify and manage cybersecurity and risks to privacy with applicable requirements and standards.Open Source. It is obligatory for the Data and Information Management Team to maintain its Open Data- Open Source platform. The platform allows citizens to access open data from multiple cities in a central location, regional universities and colleges to foster continuous education, and aids in the development of data analytics skills for citizens. Continuing to uphold the Open Source platform with allow the City to continually offer citizens the ability to provide valuable input on the structure and availability of its data. Strategic areasIn order to deploy the strategy for the responsible and ethical use of data, the following areas of action have been established, which we will detail below, together with the actions and emblematic projects associated with them.In general, the strategy pivots on the following general principals, which form the basis for the strategic areas described in this section.Data sovereigntyOpen data and transparencyThe exchange and reuse of dataPolitical decision-making informed by dataThe life cycle of data and continual or permanent accessData GovernanceData quality and accessibility are crucial for meaningful data analysis, and must be ensured through the implementation of data governance. IT will establish a Data Governance Board, a collaborative organizational capability made up of the city’s data and analytics champions, who will work together to develop policies and practices to treat and use data as a strategic asset.Data governance is the overall management of the availability, usability, integrity and security of data used in the city. Increased data quality will positively impact overall trust in data, resulting in increased use and adoption. The ownership, accessibility, security, and quality, of the data is defined and maintained by the Data Governance Board.To improve operational efficiency, an enterprise-wide data catalog will be created to inventory data and track metadata from various data sources to allow for rapid data asset discovery. Through the data catalog, the city will

The Mobile Source Emissions Regulatory Compliance Data Inventory data asset contains measured summary compliance information on light-duty, heavy-duty, and non-road engine manufacturers by model, as well as fee payment data required by Title II of the 1990 Amendments to the Clean Air Act, to certify engines for sale in the U.S. and collect compliance certification fees. Data submitted by manufacturers falls into 12 industries: Heavy Duty Compression Ignition, Marine Spark Ignition, Heavy Duty Spark Ignition, Marine Compression Ignition, Snowmobile, Motorcycle & ATV, Non-Road Compression Ignition, Non-Road Small Spark Ignition, Light-Duty, Evaporative Components, Non-Road Large Spark Ignition, and Locomotive. Title II also requires the collection of fees from manufacturers submitting for compliance certification. Manufacturers submit data on an annual basis, to document engine model changes for certification. Manufacturers also submit compliance information on already certified in-use vehicles randomly selected by the EPA (1) year into their life and (4) years into their life to ensure that emissions systems continue to function appropriately over time.The EPA performs targeted confirmatory tests on approximately 15% of vehicles submitted for certification. Confirmatory data on engines is associated with its corresponding submission data to verify the accuracy of manufacturer submission beyond standard business rules.Section 209 of the 1990 Amendments to the Clean Air Act grants the State of California the authority to set its own standards and perform its own compliance certification through the California Air Resources Board (CARB). Currently manufacturers submit compliance information separately to both the EPA and CARB. Currently, data harmonization occurs between EPA data and CARB data only for Motorcycle & ATV submissions.Submitted data comes in XML format or as documents, with the majority of submissions being sent in XML. Data includes descriptive information on the engine itself, as well as on manufacturer testing methods and results. Submissions may include information (CBI) such as information on estimated sales, new technologies, catalysts and calibration, or other data elements indicated by the submitter as confidential. CBI data is not publically available, but it is available within EPA under the restrictions of the Office of Transportation and Air Quality (OTAQ) CBI policy [RCS Link]. Pollution emission data covers a range of Criteria Air Pollutants (CAPs) including carbon monoxide, hydrocarbons, nitrogen oxides, and particulate matter. Datasets are segmented by vehicle/engine model and year, with corresponding emission, test, and certification data. Data assets are primarily stored in EPA's Verify system. Data collected from the Heavy Duty Compression Ignition, Marine Spark Ignition, Heavy Duty Spark Ignition, Marine Compression Ignition, and Snowmobile industries, however, are currently stored in legacy systems the will be migrated to Verify in the future.Coverage began in 1979, with early records being primarily paper documents that did not go through the same level of validation as the digital submissions that began in 2005.Mobile Source Emissions Compliance documents with metadata, certificate and summary decision information is made available to the public through EPA.gov via the OTAQ Document Index System (http://iaspub.epa.gov/otaqpub/).

Dataset Card for [Dataset Name]

  Dataset Summary

[More Information Needed]

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation





  Curation Rationale

[More Information Needed]

  Source Data… See the full description on the dataset page: https://huggingface.co/datasets/c17hawke/test-xml-data.

What is in this release?

In this release you will find data about software distributed and/or crafted publicly on the Internet. You will find information about its development, its distribution and its relationship with other software included as a dependency. You will not find any information about the individuals who create and maintain these projects.

Further information and documentation on this data set can be found at https://libraries.io/data

For enquiries please contact data@libraries.io

This dataset contains seven csv files:

Projects

A project is a piece of software available on any one of the 34 package managers supported by Libraries.io.

Versions

A Libraries.io version is an immutable published version of a Project from a package manager. Not all package managers have a concept of publishing versions, often relying directly on tags/branches from a revision control tool.

Tags

A tag is equivalent to a tag in a revision control system. Tags are sometimes used instead of Versions where a package manager does not use the concept of versions. Tags are often semantic version numbers.

Dependencies

Dependencies describe the relationship between a project and the software it builds upon. Dependencies belong to Version. Each Version can have different sets of dependencies. Dependencies point at a specific Version or range of versions of other projects.

Repositories

A Libraries.io repository represents a publically accessible source code repository from either github.com, gitlab.com or bitbucket.org. Repositories are distinct from Projects, they are not distributed via a package manager and typically an application for end users rather than component to build upon.

Repository dependencies

A repository dependency is a dependency upon a Version from a package manager has been specified in a manifest file, either as a manually added dependency committed by a user or listed as a generated dependency listed in a lockfile that has been automatically generated by a package manager and committed.

Projects with related Repository fields

This is an alternative projects export that denormalizes a projects related source code repository inline to reduce the need to join between two data sets.

Licence

This dataset is released under the Creative Commons Attribution-ShareAlike 4.0 International Licence.

This licence provides the user with the freedom to use, adapt and redistribute this data. In return the user must publish any derivative work under a similarly open licence, attributing Libraries.io as a data source. The full text of the licence is included in the data.

Access, Attribution and Citation

The dataset is available to download from Zenodo at https://zenodo.org/record/2536573.

Please attribute Libraries.io as a data source by including the words ‘Includes data from Libraries.io, a project from Tidelift’ and reference the Digital Object identifier: 10.5281/zenodo.3626071

The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 75% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

Snapshot img

Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

Request Free Sample

The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector,

The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. Restricted access data files are available with a data use agreement and brief online security training. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

Under Section 21 of the Act, we are not required to provide information in response to a request if it is already reasonably accessible to you. The information you requested is available from web link: https://opendata.nhsbsa.net/dataset/foi-23358 Data for January 2022 and February 2022 A copy of the information is attached. NHS Prescription Services process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors and Personal Administration with information then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings (other arrangements are in place for making payments to Dispensing Doctors and Personal Administration). This involves processing over 1 billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care users. Data source Source System - ISP (National MIS Files) Time period January 2022 and February 2022 The month refers to the month of the report. Please note Appliance Contractors data within the MIS Report shows data for the following month. (E.g. MIS Report for January 2022 will show February 2022 Appliance Contractor data) This dataset FOI25450 has 4 files – January and February 2022 MIS Pharmacy and January and February 2022 MIS Appliance Contractor. This report consists of a management information file detailing monthly Community Pharmacy and Appliance Payments by type of payment and contractor account. Payments include all drug costs, fees, patient charges, locally authorised payments, etc. Other details such as the numbers of items dispensed, patient’s charges collected are also included. The management information file reflects the contractor's payment and prescription data associated with the sustainability and transformation partnerships (STPs) structure at the relevant payment date. The data contained within the files can be interpreted correctly by using the ‘MIS Glossary’ available under ‘Management Information Spreadsheet (MIS) Report’ at https://www.nhsbsa.nhs.uk/information-services-portal-isp/isp-report-information . Disclosure Control The data in column METHADONE PAYMT and ADD FEE-2E within the Pharmacy dataset have been removed following Information Governance policy. February 2022 is the latest MIS report that is available Please note that this request and our response is published on our Freedom of Information disclosure log at:

Shoreline Construction Lines Dataset

Mapping of Shoreline Construction Lines

By Homeland Infrastructure Foundation [source]

About this dataset

Within this dataset, users can find numerous attributes that provide insight into various aspects of shoreline construction lines. The Category_o field categorizes these structures based on certain characteristics or purposes they serve. Additionally, each object in the dataset possesses a unique name or identifier represented by the Object_Nam column.

Another crucial piece of information captured in this dataset is the status of each shoreline construction line. The Status field indicates whether a particular structure is currently active or inactive. This helps users understand if it still serves its intended purpose or has been decommissioned.

Furthermore, the dataset includes data pertaining to multiple water levels associated with different shoreline construction lines. This information can be found in the Water_Leve column and provides relevant context for understanding how these artificial coastlines interact with various water bodies.

To aid cartographic representations and proper utilization of this data source for mapping purposes at different scales, there is also an attribute called Scale_Mini. This value denotes the minimum scale necessary to visualize a specific shoreline construction line accurately.

Data sources are important for reproducibility and quality assurance purposes in any GIS analysis project; hence identifying who provided and contributed to collecting this data can be critical in assessing its reliability. In this regard, individuals or organizations responsible for providing source data are specified in the column labeled Source_Ind.

Accompanying descriptive information about each source used to create these shoreline constructions lines can be found in the Source_D_1 field. This supplemental information provides additional context and details about the data's origin or collection methodology.

The dataset also includes a numerical attribute called SHAPE_Leng, representing the length of each shoreline construction line. This information complements the geographic and spatial attributes associated with these structures.

How to use the dataset

• Understanding the Categories:

  • The Category_o column classifies each shoreline construction line into different categories. This can range from seawalls and breakwaters to jetties and groins.
  • Use this information to identify specific types of shoreline constructions based on your analysis needs.

• Identifying Specific Objects:

  • The Object_Nam column provides unique names or identifiers for each shoreline construction line.
  • These identifiers help differentiate between different segments of construction lines in a region.

• Determining Status:

  • The Status column indicates whether a shoreline construction line is active or inactive.
  • Active constructions are still in use and may be actively maintained or monitored.
  • Inactive constructions are no longer operational or may have been demolished.

• Analyzing Water Levels:

  • The Water_Leve column describes the water level at which each shoreline construction line is located.
  • Different levels may impact the suitability or effectiveness of these structures based on tidal changes or flood zones.

• Exploring Additional Information:

  • The Informatio column contains additional details about each shoreline construction line.
  • This can include various attributes such as materials used, design specifications, ownership details, etc.

• Determining Minimum Visible Scale:
  -- The Scale_Mini column specifies the minimum scale at which you can observe the coastline's man-made structures clearly.

• Verifying Data Sources: -- In order to understand data reliability and credibility for further analysis,Source_Ind, Source_D_1, SHAPE_Leng,and Source_Dat columns provide information about the individual or organization that provided the source data and length, and date of the source data used to create the shoreline construction lines.

Utilize this dataset to perform various analyses related to shorelines, coastal developments, navigational channels, and impacts of man-made structures on marine ecosystems. The combination of categories, object names, status, water levels, additional information, minimum visible scale and reliable source information offers a comprehensive understanding of shoreline constructions across different regions.

Remember to refer back to the dataset documentation for any specific deta...

Dataset Card for Dataset Name

  Dataset Summary

[More Information Needed]

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation





  Curation Rationale

[More Information Needed]

  Source Data… See the full description on the dataset page: https://huggingface.co/datasets/GFA-D2/pilot_flags.

Dataset Card for [Dataset Name]

  Dataset Summary

[More Information Needed]

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation





  Curation Rationale

[More Information Needed]

  Source Data… See the full description on the dataset page: https://huggingface.co/datasets/Zaid/NewDataImage.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data was reported at 45.958 NA in 2022. This records a decrease from the previous number of 49.075 NA for 2021. Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data is updated yearly, averaging 49.892 NA from Dec 2016 (Median) to 2022, with 7 observations. The data reached an all-time high of 52.417 NA in 2018 and a record low of 45.958 NA in 2022. Algeria DZ: SPI: Pillar 4 Data Sources Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Algeria – Table DZ.World Bank.WDI: Governance: Policy and Institutions. The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

As new data sources have emerged, the data space which Pharmacovigilance (PV) processes can use has significantly expanded. However, still, the currently available tools do not widely exploit data sources beyond Spontaneous Report Systems built to collect Individual Case Safety Reports (ICSRs). This article presents an open-source platform enabling the integration of heterogeneous data sources to support the analysis of drug safety related information. Furthermore, the results of a comparative study as part of the project’s pilot phase are also presented. Data sources were integrated in the form of four “workspaces”: (a) Individual Case Safety Reports—obtained from OpenFDA, (b) Real-World Data (RWD) —using the OMOP-CDM data model, (c) social media data—collected via Twitter, and (d) scientific literature—retrieved from PubMed. Data intensive analytics are built for each workspace (e.g., disproportionality analysis metrics are used for OpenFDA data, descriptive statistics for OMOP-CDM data and twitter data streams etc.). Upon these workspaces, the end-user sets up “investigation scenarios” defined by Drug-Event Combinations (DEC). Specialized features like detailed reporting which could be used to support reports for regulatory purposes and also “quick views” are provided to facilitate use where detailed statistics might not be needed and a qualitative overview of the available information might be enough (e.g., clinical environment). The platform’s technical features are presented as Supplementary Material via a walkthrough of an example “investigation scenario”. The presented platform is evaluated via a comparative study against the EVDAS system, conducted by PV professionals. Results from the comparative study, show that there is indeed a need for relevant technical tools and the ability to draw recent data from heterogeneous data sources is appreciated. However, a reluctance by end-users is also outlined as they feel technical improvements and systematic training are required before the potential adoption of the presented software. As a whole, it is concluded that integrating such a platform in real-world setting is far from trivial, requiring significant effort on training and usability aspects.