This data set is part of an ongoing project to consolidate interagency fire perimeter data. The record is complete from the present back to 2020. The incorporation of all available historic data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.

video presented by a representative of Tuvalu explaining briefly the importance of the Inform Project

This guide brings together online resources that contain U.S. government documents. Some are freely available to anyone with Internet access. Others include subscription databases accessible with a DHS device.

This Google Data Analytics Capstone Project, Case Study 1, centers around the examination of Cyclistic's bike share data for a fictitious bike-share company. The primary objective of this project is to explore the bike share user’s ride patterns and behaviors in order to enhance marketing strategies and boost annual subscriptions. Leveraging data analysis techniques and tools, the project endeavors to reveal significant insights that can inform business decisions and enhance Cyclistic's overall performance.

Context

Cyclistic launched a successful bike-share offering in 2006. And has grown to a fleet of 5,824 bicycles. These bikes are geo-tracked and locked in a network of 692 stations across Chicago. The bikes can be unlocked from one station and returned to any other station in the network at any time. Cyclistic’s marketing strategy relied on building general awareness and appealing to broad consumer segments including flexible pricing plans. Cyclistic offers single-ride passes, full-day passes, and annual memberships. “Customers who purchase single-ride or full-day passes are referred to as CASUAL riders. Customers who purchase annual memberships are MEMBERS.

Business Objective

The main objective of this study is to analyze Cyclistic historical bike trip data to identify trends and the primary distinction in bike usage and behavior between two types of users.

"Casual" riders who pay for individual rides or full-day passes. "Members" who subscribe annually to access the service.

And identify how to convert casual riders into annual members by identifying key differences in how Cyclistic riders operate the service in Chicago.

Using the historical data to answer the following questions: 1. How do annual members and casual riders use Cyclistic bikes differently? 2. Why would casual riders buy Cyclistic annual memberships? 3. How can Cyclistic use digital media to influence casual riders to become members?

Data Source

Data used for this case study is 12 months of rider's trip data between May 2022 through April 2023. Data is publicly available via https://divvy-tripdata.s3.amazonaws.com/index.html provided by Motivate International Inc. under this license https://www.divvybikes.com/data-license-agreement/. The data is organized and contains necessary entities that can be sorted and filtered to gain insights. It is sequential and ROCCC (Reliable, Original, Comprehensive, Current, and Cited). However, there are a few duplicates and records that have N/A values. Hence the data will be cleaned for this project to align with business objectives.

Acknowledgement

This resource was retired on June 1, 2021 and is no longer updated. These data remain available to support research and development efforts.

Disaster Lit®: Database for Disaster Medicine and Public Health is a dataset of links to disaster medicine and public health documents available on the Internet at no cost. Documents include expert guidelines, research reports, conference proceedings, training classes, factsheets, websites, databases, and similar materials selected from over 700 organizations for a professional audience. Materials were selected from non-commercial publishing sources and supplement disaster-related resources from PubMed (biomedical journal literature) and MedlinePlus (health information for the public).

Dataset belonging to the paper: Data-Driven Machine Learning-Informed Framework for Model Predictive Control in Vehicles

labeled_seed.csv: Processed and labeled data of all maneuvers combined into a single file, sorted by label

raw_track_session.csv: Untouched CSV file from Racebox track session

unlabeled_exemplar.csv: Processed but unlabeled data of street and track data

The codes and data for: “Fact-checking” fact-checkers: A data-driven approach

The graph shows the changes in the impact factor of ^ and its corresponding percentile for the sake of comparison with the entire literature. Impact Factor is the most common scientometric index, which is defined by the number of citations of papers in two preceding years divided by the number of papers published in those years.

This dataset holds all materials for the Inform E-learning GIS course

This document provides an overview of the accompanying data files used in the manuscript entitled "Practical data-driven flood forecasting based on dynamical systems theory: Case studies from Japan."

db_kagetsu.csv: Past hourly data on Kagetsu gauging station and 4 precipitation stations (Tsurukochi, Kagetsu, Yokohata, and Mikuma) downloaded from the website of the Water Information System (http://www1.river.go.jp/).

db_hiwatashi.csv Past hourly data on 5 gauging stations (Takeshita, Otobou, Hirose, Ohjibashi, and Hiwatashi) and 14 precipitation stations (Sunoura, Nojiri, Kensetsutakaharu, Shika, Sano, Kirishima, Miike, Hiwatashi, Aoidake, Mimata, Kabayama, Takeshita, Hisokino, and Sueyoshi) downloaded from the website of the Water Information System (http://www1.river.go.jp/).

Circular 21/150

Inform Plus proposed 5 pillars

Component 1: Environmental Governance
Component 2: Monitoring and field data collection for environmental standards and standardised environmental indicators
Component 3: Data management utilising the Pacific Island Network Portal (PEP). Production of information products for decision makers based on existing data sets.
Component 4: Enhance and expand GIS use for data collection, analysis and presentation to inform decision makers

Snapshot img

Sales Intelligence Market Size 2025-2029

The sales intelligence market size is forecast to increase by USD 4.86 billion at a CAGR of 17.6% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for custom-made solutions that cater to the unique needs of businesses. This trend is fueled by the rapid advancements in cloud technology, enabling real-time access to comprehensive and accurate sales data from anywhere. However, the high initial cost of implementing sales intelligence solutions can act as a barrier to entry for smaller organizations. Furthermore, regulatory hurdles impact adoption in certain industries, requiring strict compliance with data privacy regulations. With the advent of cloud computing and SaaS customer relationship management (CRM) systems, businesses are able to store and access customer information more efficiently. Moreover, the exponential growth of marketing intelligence, driven by big data and natural language processing (NLP) technologies, enables organizations to gain valuable insights from customer interactions.
Despite these challenges, the market's potential is vast, with opportunities for growth in sectors such as healthcare, finance, and retail. Companies seeking to capitalize on these opportunities must navigate these challenges effectively, investing in cost-effective solutions and ensuring regulatory compliance. By doing so, they can gain a competitive edge through improved lead generation, enhanced customer insights, and streamlined sales processes.

What will be the Size of the Sales Intelligence Market during the forecast period?

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In today's business landscape, sales intelligence has become a critical driver of revenue growth. The go-to-market strategy of companies relies heavily on predictive lead scoring and sales pipeline analysis to prioritize opportunities and optimize resource allocation. Sales operations teams leverage revenue intelligence to gain insights into sales performance and identify trends. Data quality is paramount in sales analytics dashboards, ensuring accurate sales negotiation and closing. Sales teams collaborate using sales enablement platforms, which integrate CRM systems and provide sales performance reporting. Sales process mapping and sales engagement tools enable effective communication and productivity. Conversational AI and sales automation software streamline sales outreach and prospecting efforts. Messaging and alerting features help sales teams engage with potential customers effectively, while chatbots facilitate efficient communication.
Sales forecasting models and intent data inform sales management decisions, while salesforce automation and data governance ensure data security and compliance. Sales effectiveness is enhanced through sales negotiation training and sales enablement training. The sales market is dynamic, with trends shifting towards advanced analytics and AI-driven solutions. Companies must adapt to stay competitive, focusing on data-driven strategies and continuous improvement.

How is this Sales Intelligence Industry segmented?

The sales intelligence industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises


Component

  Software
  Services


Application

  Data management
  Lead management


End-user

  IT and Telecom
  Healthcare and life sciences
  BFSI
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period. In today's business landscape, sales intelligence platforms have become indispensable tools for organizations seeking to optimize their sales processes and gain a competitive edge. These solutions offer various features, including deal tracking, win-loss analysis, data mining, sales efficiency, customer journey mapping, sales process optimization, pipeline management, sales cycle analysis, revenue optimization, market research, data integration, customer segmentation, sales engagement, sales coaching, sales playbook, sales process automation, business intelligence (BI), predictive analytics, target account identification, lead generation, account-based marketing (ABM), sales strategy, sales velocity, real-time data, artificial intelligence (AI), sales insights, sales enablement content, sales enablement, sales funnel optimization, sales performance metrics, competitive intelligence, sales methodology, customer churn, and machine learning (ML) for sales forecasting and buyer persona deve

This dataset shows:

• the 500 customers that send the most applications to us
• the number and type of applications we receive and complete
• how many requests for information we send to customers

It includes all types of requests for information but excludes:

• telephone requests for information
• bankruptcy applications
• bulk applications
• applications that we've received but not yet completed

Geographic coverage

England and Wales

License statement

The data is available free of charge for use and re-use under the Open Government Licence (OGL). Make sure that you understand the terms of the OGL before using the data. If you use or publish this data, you must add the following statement:

Contains HM Land Registry data © Crown copyright and database rights [year of supply or data of publication]. This data is licensed under the Open Government Licence v3.0.

You must also provide a link in the data you publish to this explanation of the dataset.

Frequency of update

Every three months

Llc Vilena Inform Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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?

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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, gover

Llc Npo Inform Sistema Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Abstract

The World Bank Group is interested in gauging the views of clients and partners who are either involved in development in Peru or who observe activities related to social and economic development. The following survey will give the World Bank Group's team that works in Peru, greater insight into how the Bank's work is perceived. This is one tool the World Bank Group uses to assess the views of its stakeholders, and to develop more effective strategies that support development in Peru. A local independent firm was hired to oversee the logistics of this survey.

This survey was designed to achieve the following objectives: - Assist the World Bank Group in gaining a better understanding of how stakeholders in Peru perceive the Bank Group; - Obtain systematic feedback from stakeholders in Peru regarding: · Their views regarding the general environment in Peru; · Their overall attitudes toward the World Bank Group in Peru; · Overall impressions of the World Bank Group's effectiveness and results, knowledge work and activities, and communication and information sharing in Peru; · Perceptions of the World Bank Group's future role in Peru. - Use data to help inform Peru country team's strategy.

Geographic coverage

Metropolitan Lima Area, Outside of Metropolitan Lima Area

Analysis unit

Stakeholders in Peru

Universe

Stakeholders in Peru

Kind of data

Sample survey data [ssd]

Sampling procedure

In February-April 2014, 465 stakeholders of the World Bank Group in Peru were invited to provide their opinions on the WBG's work in the country by participating in a country opinion survey. Participants were drawn from the office of the President; the office of the Prime Minister; office of a minister; office of a parliamentarian; ministries, ministerial departments, or implementation agencies; consultants/contractors working on WBG-supported projects/programs; project management units (PMUs) overseeing implementation of a project; local government officials; bilateral and multilateral agencies; private sector organizations; private foundations; the financial sector/private banks; NGOs; community based organizations; the media; independent government institutions; trade unions; faith-based groups; academia/research institutes/think tanks; judiciary branch; and other organizations.

Mode of data collection

Other [oth]

Research instrument

The Questionnaire consists of following sections:

A. General Issues Facing Peru: Respondents were asked to indicate whether Peru is headed in the right direction, what they thought were the top three most important development priorities in the country, which areas would contribute most to reducing poverty and generating economic growth in Peru, and how "shared prosperity" would be best achieved.

B. Overall Attitudes toward the World Bank Group (WBG): Respondents were asked to rate their familiarity with the WBG and other regional development banks, their effectiveness in Peru, WBG staff preparedness to help Peru solve its development challenges, WBG's local presence, WBG's capacity building in Peru, their agreement with various statements regarding the WBG's work, and the extent to which the WBG is an effective development partner. Respondents were asked to indicate the WBG's greatest values and weaknesses, the most effective instruments in helping reduce poverty in Peru, in which sectoral areas the WBG should focus most of its resources (financial and knowledge services), and to what reasons respondents attributed failed or slow reform efforts. Respondents were also asked to respond to a few questions about capacity building and whether they believe the World Bank Group should have more or less local presence.

C. World Bank Group's Effectiveness and Results: Respondents were asked to rate the extent to which the WBG's work helps achieve development results in Peru, the extent to which the WBG meets Peru's needs for knowledge services and financial instruments, the importance for the WBG to be involved in thirty one development areas, and the WBG's level of effectiveness across these areas, such as education, public sector governance/reform, water and sanitation, and transport.

D. The World Bank Group's Knowledge Work and Activities: Respondents were asked to indicate how frequently they consult WBG's knowledge work and activities and to rate the effectiveness and quality of the WBG's knowledge work and activities, including how significant of a contribution it makes to development results and its technical quality. Respondents were also asked about the WBG reports, including which of them are the most useful, whether they raised substantive new information, and whether they provided them with useful information in terms of work they do.

E. Working with the World Bank Group: Respondents were asked to rate WBG's technical assistance/advisory work's contribution to solving development challenges and their level of agreement with a series of statements regarding working with the WBG, such as the WBG's "Safeguard Policy" requirements being reasonable, and disbursing funds promptly.

F. The Future Role of the World Bank Group in Peru: Respondents were asked to indicate what the WBG should do to make itself of greater value in Peru, and which services the Bank should offer more of in the country. They were asked whether WBG has moved to the right direction, and the future role international development cooperation should play in Peru.

G. Communication and Information Sharing: Respondents were asked to indicate how they get information about economic and social development issues, how they prefer to receive information from the WBG, and their usage and evaluation of the WBG's websites. Respondents were also asked about their awareness of the WBG's Access to Information policy, were asked to rate WBG's responsiveness to information requests, value of its social media channels, and levels of easiness to find information they needed.

H. Background Information: Respondents were asked to indicate their current position, specialization, whether they professionally collaborate with the WBG, their exposure to the WBG in Peru, which WBG agencies they work with, whether IFC and the Bank work well together, and their geographic location.

Response rate

A total of 197 stakeholders participated in the survey (42% response rate).

This dataset outlines a proposed set of core, minimal metadata elements that can be used to describe biomedical datasets, such as those resulting from research funded by the National Institutes of Health. It can inform efforts to better catalog or index such data to improve discoverability. The proposed metadata elements are based on an analysis of the metadata schemas used in a set of NIH-supported data sharing repositories. Common elements from these data repositories were identified, mapped to existing data-specific metadata standards from to existing multidisciplinary data repositories, DataCite and Dryad, and compared with metadata used in MEDLINE records to establish a sustainable and integrated metadata schema. From the mappings, we developed a preliminary set of minimal metadata elements that can be used to describe NIH-funded datasets. Please see the readme file for more details about the individual sheets within the spreadsheet.

B2B Information Services Market size was valued at USD 900.02 Million in 2023 and is projected to reach USD 1238.91 Million by 2030, growing at a CAGR of 6.6% during the forecast period 2024-2030.

Global B2B Information Services Market Drivers

The market drivers for the B2B Information Services Market can be influenced by various factors. These may include:

Digital Transformation: One important factor is the continuous digital transformation that is occurring across sectors. In order to simplify operations, businesses are depending more and more on digital platforms and technologies, and B2B information services are essential for supplying the data and insights that are required.

Data analytics and business intelligence: The need for B2B information services has increased due to the growing significance of data-driven decision-making. Businesses are looking for business intelligence and advanced analytics technologies to help them glean insights from massive amounts of data.

Globalisation of Businesses: As companies grow internationally, they need to have precise and thorough knowledge of foreign markets, laws, and rivals. Global coverage B2B information services are highly sought after.

Regulatory Compliance: Organisations must be up to date on compliance obligations due to the constantly shifting regulatory environment. Organisations can better manage complicated compliance challenges by using B2B information services that offer current regulatory information.

Risk Management: Effective risk management is a growing area of concern for businesses. To help businesses reduce operational risks, B2B information services that include risk assessment, market knowledge, and assistance with due diligence are crucial.

Artificial Intelligence and Machine Learning: By incorporating cutting-edge technologies like AI and machine learning into business-to-business information services, businesses can improve their capacity for data analysis, trend prediction, and automated decision-making.

Industry-specific Solutions: Customised B2B information services are becoming more and more popular. Examples of these include healthcare, banking, and manufacturing. These sector-specific solutions assist corporate strategies and offer focused insights.

Demand for Real-time Information: As corporate processes move more quickly, there is an increasing need for real-time information. The value of B2B information services that can provide pertinent and timely data is growing.

Cybersecurity Concerns: Businesses are being increasingly watchful of cybersecurity as cyber threats continue to change. Organisations need B2B information services that provide cybersecurity intelligence and threat assessments in order to safeguard their digital assets.

Economic and Market Trends: The requirement for ongoing observation of economic indicators and market trends stems from variations in the global economy and market dynamics. Businesses are assisted in making wise decisions by B2B information services that offer insights into these variables.