Historical dataset showing Marshall Islands electricity access by year from 1999 to 2023.

Historical dataset showing Tajikistan electricity access by year from 1999 to 2023.

This data set is a subset of a global river discharge data set by Coe and Olejniczak (1999). The subset was created for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America (i.e., 10° N to 25° S, 30° to 85° W).The global river discharge data set (Coe and Olejniczak 1999), formerly known as the "Climate, People, and Environment Program (CPEP) Global River Discharge Database," is a compilation of monthly mean discharge data for more than 2600 sites worldwide. The data were compiled from RivDIS Version 1.1 (Vorosmarty et al. 1998), the U.S. Geological Survey, and the Brazilian National Department of Water and Electrical Energy. The period of record for the sites varies from 3 years to greater than 100.The purpose of the global compilation is to provide detailed hydrographic information for the climate research community in as general a format as possible. Data are given in units of meters cubed per second (m**3/sec) and are in ASCII format. Data from stations that had less than 3 years of information or that had a basin area less than 5000 square kilometers were excluded from the global data set. Thus, the data sources may include more sites than the data set by Coe and Olejniczak (1999). Users should refer to the data originators for further documentation on the source data.More information, a map of discharge sites, and a clickable site data table can be found at ftp://daac.ornl.gov/data/lba/surf_hydro_and_water_chem/sage/comp/sagedischarge_readme.pdf.LBA was a cooperative international research initiative led by Brazil. NASA was a lead sponsor for several experiments. LBA was designed to create the new knowledge needed to understand the climatological, ecological, biogeochemical, and hydrological functioning of Amazonia; the impact of land use change on these functions; and the interactions between Amazonia and the Earth system. Further information about LBA can be found at http://www.daac.ornl.gov/LBA/misc_amazon.html.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 - 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIP01 VI data product is a daily global file at 0.05 degree (5600 meter (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIP01 VI product contains 11 Science Datasets (SDS), which includes the calculated VIs (NDVI and EVI2) as well as information on the quality assurance/pixel reliability, the input Visible/Near Infrared (VNIR) surface reflectance data, and viewing geometry. The Blue and Middle Infrared (MIR) surface reflectance data are only available for the MODIS era (2000 - 2014). Gaps in the daily product are filled using long term mean VI records derived from the more than 30 year time series of data, and are indicated as gap-filled in the Pixel Reliability SDS. A low resolution browse image showing NDVI as a color map is also available.Known Issues The Relative Azimuth Angle (RAA) for the input MODIS data is computed based on absolute values of the finer resolution pixels resulting in positive values and has minor usefulness. The RAA for the input AVHRR data contain values in the -360° to 360° range. The routine to restrict the values in the -180° to 180° range was accidentally missed and can be corrected using the following routine described in Section 4.2.1 of the User Guide and Algorithm Theoretical Basis Document: * SinRelativeAz=sin(RAA) * CosRelativeAz=cos(RAA) * Correct-RAA = atan2(SinRelativeAz,CosRelativeAz)

Success.ai’s Manufacturing Data for Electrical, Electronic & Industrial Manufacturing Leaders Globally delivers a robust dataset designed to empower businesses in connecting with decision-makers in the global manufacturing sector. Covering professionals and leaders in electrical, electronic, and industrial manufacturing, this dataset offers verified contact details, firmographic insights, and actionable professional data.

With access to over 700 million verified global profiles and insights from 70 million businesses, Success.ai ensures your outreach, market research, and business development efforts are powered by accurate, continuously updated, and AI-validated information. Backed by our Best Price Guarantee, this solution is essential for navigating the competitive manufacturing industry.

Why Choose Success.ai’s Manufacturing Data?

1. Verified Contact Data for Targeted Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of executives, operations leaders, and engineers in the electrical, electronic, and industrial manufacturing industries.
   • AI-driven validation ensures 99% accuracy, optimizing communication efforts and improving campaign efficiency.

2. Comprehensive Coverage of Global Manufacturing Leaders

   • Includes profiles from major manufacturing hubs across North America, Europe, Asia-Pacific, and other key regions.
   • Gain insights into operational practices, supply chain dynamics, and technological trends shaping the industry.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, business expansions, and emerging market opportunities.
   • Stay aligned with evolving market conditions to capitalize on new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with industry leaders, engineers, and decision-makers in the electrical, electronic, and industrial manufacturing sectors.
• 70M Business Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Decision-Maker Contacts: Engage with CEOs, operations managers, and procurement leads driving manufacturing strategies.
• Industry Insights: Understand trends in automation, supply chain optimization, and emerging technologies.

Key Features of the Dataset:

1. Leadership and Decision-Maker Profiles

   • Identify and connect with professionals responsible for engineering, production, and operational excellence in the manufacturing sector.
   • Target decision-makers driving innovation, vendor selection, and manufacturing efficiency.

2. Advanced Filters for Precision Campaigns

   • Filter companies by industry focus (electrical, electronic, industrial), geographic location, revenue size, or workforce composition.
   • Tailor campaigns to address specific challenges, such as cost reduction, sustainability, or digital transformation.

3. Firmographic and Geographic Insights

   • Access detailed business information, including operational scopes, manufacturing capacities, and regional distribution.
   • Pinpoint key players in emerging and established manufacturing hubs for strategic engagement.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with manufacturing stakeholders.

Strategic Use Cases:

1. Sales and Vendor Development

   • Offer tools, technologies, or raw materials tailored to the needs of manufacturers in the electrical, electronic, and industrial sectors.
   • Build relationships with procurement teams and operations managers seeking reliable suppliers or innovative solutions.

2. Market Research and Competitive Analysis

   • Analyze global manufacturing trends, from automation and AI to sustainable production practices, to refine your strategies.
   • Benchmark against competitors to identify growth opportunities, market gaps, and high-demand products.

3. Supply Chain Optimization and Risk Mitigation

   • Connect with supply chain leaders and operational managers to optimize logistics, improve vendor relationships, and mitigate risks.
   • Present solutions for efficiency, cost savings, or enhanced supply chain transparency.

4. Recruitment and Talent Development

   • Target HR professionals and hiring managers recruiting for roles in engineering, operations, or manufacturing management.
   • Provide staffing solutions, training platforms, or professional development tools tailored to the manufacturing industry.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-quality manufacturing data at competitive prices, ensuring strong ROI for your outreach, marketing, a...

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

The global vegetation type data of 1 x 1 degree latitude and longitude resolution were designed for use in studies of climate and climate change. Vegetation data were compiled in digital form from approximately 100 published sources. The raw data base distinguished about 180 vegetation types that have been collapsed to 32. The vegetation data were encoded using the UNESCO classification system. Additional information about this data set can be found at http://www.giss.nasa.gov/data/landuse/vegeem.html. ORNL DAAC maintains information on related data sets in the Vegetation Collection. Data Citation The data set should be cited as follows: Matthews, E. 1999. Global Vegetation Types, 1971-1982. Available on-line from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A.

This dataset contains raw, unprocessed data files pertaining to the management tool group focused on 'Customer Experience Management' (CEM) and 'Customer Relationship Management' (CRM), including related concepts like Customer Satisfaction Surveys and Measurement. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "customer relationship management" + "customer experience management" + "customer satisfaction" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Customer Relationship Management+Customer Experience Management+Customer Satisfaction Measurement+Customer Satisfaction Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("customer relationship management" OR "customer experience management" OR "customer satisfaction" OR "customer satisfaction measurement" OR CRM) AND ("management" OR "strategy" OR "approach" OR "system" OR "implementation" OR "evaluation") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Customer Satisfaction Surveys (1993); Customer Satisfaction (1996); Customer Satisfaction Measurement (1999, 2000); Customer Relationship Management (2002, 2006, 2008, 2010, 2012, 2017); CRM (2004, 2014); Customer Experience Management (2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 1994, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1996/784; 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Customer Satisfaction Surveys (1993); Customer Satisfaction (1996); Customer Satisfaction Measurement (1999, 2000); Customer Relationship Management (2002, 2006, 2008, 2010, 2012, 2017); CRM (2004, 2014); Customer Experience Management (2022). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., Ronan C. et al., various years: 1994, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2023). Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1993/500; 1996/784; 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067; 2017/1268; 2022/1068. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

Understanding how much inorganic fertilizer (referred to as fertilizer) is applied to different crops at national, regional and global levels is an essential component of fertilizer consumption analysis and demand projection. Good information on fertilizer use by crop (FUBC) is rarely available because it is difficult to collect and time-consuming to process and validate. To fill this gap, a first global FUBC report was published in 1992 for the 1990/1991 period, based on an expert survey conducted jointly by the Food and Agriculture Organization (FAO) of the UN, the International Fertilizer Development Center (IFDC) and the International Fertilizer Association (IFA). Since then, similar expert surveys have been carried out and published every two to four years in the main fertilizer-consuming countries. Since 2008 IFA has led these efforts and, to our knowledge, remains the only globally available data set on FUBC. This dataset includes data (in CSV format) from a survey carried out by IFA to represent the 2017–18 period as well as a collation of all historic FUBC data. Methods Latest fertilizer use by crop survey data During 2020-2022 IFA collected and standardized FUBC data for the 2017-18 period, primarily through a survey of various country correspondents. As of May 2022 this is the most recent survey for FUBC data. Country correspondents were selected based on their knowledge for estimating fertilizer use, average fertilizer application rates and areas of crops for N, P2O5 and K2O for their respective country, and access to any locally available farm data. Country correspondents were asked to complete the questionnaire with the greatest detail possible, or to provide data for the crop breakdown available in their country. The task of aligning the data with FAO crop area statistics was particularly challenging, and sometimes impossible. Even when correspondents were able to mostly follow the provided crop breakdown, crops that are minor in a country’s agriculture were often included in a group of crops or other crops. For example, for most EU countries, the data provided by Fertilizers Europe follow the crop breakdown that is specific to their own annual survey. In this crop breakdown, rice is grouped with rye, triticale and oats, soybean is grouped with sunflower and linseed, and cotton is not identified. Historic fertilizer use by crop survey data For historic FUBC data the following sources had data manually extracted from the original pdf documents into a standardized format: · FUBC report number 1: FAO et al. (1992) · FUBC report number 2: FAO et al. (1994) · FUBC report number 3: FAO et al. (1996) · FUBC report number 4: FAO et al. (1999) · FUBC report number 5: FAO et al. (2002) · FUBC report number 6: Heffer (2009) · FUBC report number 7: Heffer (2013) · FUBC report number 8: Heffer et al. (2017) References FAO, IFA, IFDC. 1992. Fertilizer use by crop 1. Rome, Italy: Food and Agriculture Organization of the United Nations, 82 p. FAO, IFA, IFDC. 1994. Fertilizer use by crop 2. Rome, Italy: Food and Agriculture Organization of the United Nations, 64 p. FAO, IFA, IFDC. 1996. Fertilizer use by crop 3. Rome, Italy: Food and Agriculture Organisation of the United Nations, 74 p. FAO, IFA, IFDC. 1999. Fertilizer use by crop 4. Rome, Italy: Food and Agriculture Organisation of the United Nations, 78 p. FAO, IFA, IFDC, IPI, PPI. 2002. Fertilizer use by crop 5. Rome, Italy.: Food and Agriculture Organization of the United Nations, 67 p. Heffer P. 2009. Assessment of Fertilizer Use by Crop at the Global Level 2006/07 – 2007/08. Paris, France: International Fertilizer Association, 11 p. https://www.ifastat.org/consumption/fertilizer-use-by-crop. Heffer P. 2013. Assessment of Fertilizer Use by Crop at the Global Level. Paris, France, 10 p. https://www.ifastat.org/consumption/fertilizer-use-by-crop. Heffer P, Gruere A, Roberts T. 2017. Assessment of fertiliser use by crop at the global level. Paris, France: International Fertilizer Association, Institute IPN, 19 p. https://www.ifastat.org/plant-nutrition.

This is a data set of mean monthly surface climate data over global land areas, excluding Antarctica, for nearly all of the twentieth century. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. All variables have mean monthly values for the period 1901-1995, several have data as recent as 1998, and more data will be added by the data originators. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al. (2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre. Access to the complete year-by-year monthly data set or to data more recent than posted here can be achieved by making a request with the Climate Impacts LINK Project at the Climatic Research Unit (email: d.viner@uea.ac.uk, web site: www.cru.uea.ac.uk/link ).

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

'A Fiducial site is a geographic location that is used as a benchmark for the long-term monitoring of processes, both natural and anthropogenic, associated with the causes and effects of global environmental change. The word fiducial carries multiple meanings with regard to the GFL. Fiducials are marks or points of reference applied to images to present a fixed standard of reference, so in the GFL they refer to the identification of a place on the Earth. The term fiducial may also be interpreted to refer to a long-term trust, where one may be holding something in trust for another. Both references aptly apply, as the GFL maintains a long-term record of data over specific places on the Earth to be available for scientific investigations. Fiducial sites are associated with Earth processes and environmentally sensitive areas that are being monitored so that scientists can better understand and model the dynamic systems and changes that are occurring. The sites are located around the globe and are distributed among environmental topics and processes within five major disciplines: Ocean Processes; Ice and Snow Dynamics; Atmospheric Processes; Land Use/Land Cover; and Geologic Processes. '

This dataset contains raw, unprocessed data files pertaining to the management tool 'Knowledge Management' (KM), including related concepts like Intellectual Capital Management and Knowledge Transfer. The data originates from five distinct sources, each reflecting different facets of the tool's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "knowledge management" + "knowledge management organizational" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Knowledge Management + Intellectual Capital Management + Knowledge Transfer Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: ("knowledge management" OR "intellectual capital management" OR "knowledge transfer") AND ("organizational" OR "management" OR "learning" OR "innovation" OR "sharing" OR "system") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Knowledge Management (1999, 2000, 2002, 2004, 2006, 2008, 2010). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2001, 2003, 2005, 2007, 2009, 2011). Note: Tool potentially not surveyed or reported after 2010 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Knowledge Management (1999, 2000, 2002, 2004, 2006, 2008, 2010). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2001, 2003, 2005, 2007, 2009, 2011). Note: Tool potentially not surveyed or reported after 2010 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

This is a data set of 30-year mean monthly surface climate data over global land areas, excluding Antarctica, for the period 1901-1960. The data set is gridded at 0.5 degree latitude/longitude resolution and includes seven variables: precipitation, mean temperature, diurnal temperature range, wet-day frequency, vapour pressure, cloud cover, and ground-frost frequency. In constructing the monthly grids the authors used an anomaly approach which attempts to maximize station data in space and time (New et al., 2000). In this technique, grids of monthly historic anomalies are derived relative to a standard normal period. Station measurement data for the years 1961-1990, extracted from the monthly data holdings of the Climatic Research Unit and the Global Historic Climatology Network (GHCN), served as the normal period (New et al., 1999). The anomaly grids were then combined with high-resolution mean monthly climatology to arrive at fields of estimated historical monthly surface climate. Data users are encouraged to see the companion file New et al.(2000) for a complete description of this technique and potential applications and limitations of the data set. For additional information, refer to the IPCC Data Distribution Centre.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datasets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of acquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis: - Analyze missing data: Project Tycho datasets do not include time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported. - Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

This dataset contains raw, unprocessed data files pertaining to the management practice 'Outsourcing'. The data originates from five distinct sources, each reflecting different facets of the practice's prominence and usage over time. Files preserve the original metrics and temporal granularity before any comparative normalization or harmonization. Data Sources & File Details: Google Trends File (Prefix: GT_): Metric: Relative Search Interest (RSI) Index (0-100 scale). Keywords Used: "outsourcing" + "outsourcing management" Time Period: January 2004 - January 2025 (Native Monthly Resolution). Scope: Global Web Search, broad categorization. Extraction Date: Data extracted January 2025. Notes: Index relative to peak interest within the period for these terms. Reflects public/professional search interest trends. Based on probabilistic sampling. Source URL: Google Trends Query Google Books Ngram Viewer File (Prefix: GB_): Metric: Annual Relative Frequency (% of total n-grams in the corpus). Keywords Used: Outsourcing Time Period: 1950 - 2022 (Annual Resolution). Corpus: English. Parameters: Case Insensitive OFF, Smoothing 0. Extraction Date: Data extracted January 2025. Notes: Reflects term usage frequency in Google's digitized book corpus. Subject to corpus limitations (English bias, coverage). Source URL: Ngram Viewer Query Crossref.org File (Prefix: CR_): Metric: Absolute count of publications per month matching keywords. Keywords Used: "outsourcing" AND ("business process" OR "supply chain" OR "management" OR "contracting" OR "operations" OR "strategy" OR "implementation") Time Period: 1950 - 2025 (Queried for monthly counts based on publication date metadata). Search Fields: Title, Abstract. Extraction Date: Data extracted January 2025. Notes: Reflects volume of relevant academic publications indexed by Crossref. Deduplicated using DOIs; records without DOIs omitted. Source URL: Crossref Search Query Bain & Co. Survey - Usability File (Prefix: BU_): Metric: Original Percentage (%) of executives reporting tool usage. Tool Names/Years Included: Outsourcing (1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed or reported after 2014 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067. Bain & Co. Survey - Satisfaction File (Prefix: BS_): Metric: Original Average Satisfaction Score (Scale 0-5). Tool Names/Years Included: Outsourcing (1999, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014). Respondent Profile: CEOs, CFOs, COOs, other senior leaders; global, multi-sector. Source: Bain & Company Management Tools & Trends publications (Rigby D., Bilodeau B., et al., various years: 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017). Note: Tool potentially not surveyed or reported after 2014 under this specific name. Data Compilation Period: July 2024 - January 2025. Notes: Data points correspond to specific survey years. Sample sizes: 1999/475; 2000/214; 2002/708; 2004/960; 2006/1221; 2008/1430; 2010/1230; 2012/1208; 2014/1067. Reflects subjective executive perception of utility. File Naming Convention: Files generally follow the pattern: PREFIX_Tool.csv, where the PREFIX indicates the data source: GT_: Google Trends GB_: Google Books Ngram CR_: Crossref.org (Count Data for this Raw Dataset) BU_: Bain & Company Survey (Usability) BS_: Bain & Company Survey (Satisfaction) The essential identification comes from the PREFIX and the Tool Name segment. This dataset resides within the 'Management Tool Source Data (Raw Extracts)' Dataverse.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Vegetation Index and Phenology (VIP) global datasets were created using surface reflectance data from the Advanced Very High Resolution Radiometer (AVHRR) N07, N09, N11, and N14 datasets (1981 – 1999) and Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra MOD09 surface reflectance data (2000 - 2014). The VIP Vegetation Index (VI) product was developed to provide consistent measurements of the Normalized Difference Vegetation Index (NDVI) and modified Enhanced Vegetation Index (EVI2) spanning more than 30 years of data from multiple sensors. The EVI2 is a backward extension of AVHRR. Vegetation indices such as NDVI and EVI2 are useful for assessing the biophysical properties of the land surface, and are used to characterize vegetation phenology. Phenology tracks the seasonal life cycle of vegetation, and provides information on the biotic response to environmental changes. The VIPPHEN data product is provided globally at 0.05 degree (5600 meters (m)) spatial resolution in geographic (Lat/Lon) grid format. The data are stored in Hierarchical Data Format-Earth Observing System (HDF-EOS) file format. The VIPPHEN phenology product contains 26 Science Datasets (SDS) which include phenological metrics such as the start, peak, and end of season as well as the rate of greening and senescence. The product also provides the maximum, average, and background calculated VIs. The VIPPHEN SDS are based on the daily VIP product series and are calculated using a 3-year moving window average to smooth out noise in the data. A reliability SDS is included to provide context on the quality of the input data.

United States GDP: PCE: SE: RC: Others: Mis: CAN: Internet Service Providers data was reported at 14.844 USD bn in Oct 2003. This records an increase from the previous number of 14.753 USD bn for Sep 2003. United States GDP: PCE: SE: RC: Others: Mis: CAN: Internet Service Providers data is updated monthly, averaging 2.104 USD bn from Jan 1988 (Median) to Oct 2003, with 190 observations. The data reached an all-time high of 14.844 USD bn in Oct 2003 and a record low of 0.012 USD bn in Jan 1988. United States GDP: PCE: SE: RC: Others: Mis: CAN: Internet Service Providers data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s USA – Table US.A203: NIPA 1999: Personal Consumption Expenditure.

Historical dataset showing Kosovo electricity access by year from 1990 to 1999.

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

Key Benefits of Success.ai’s LinkedIn Data: Our LinkedIn data solution offers more than just a dataset. With GDPR-compliant data, AI-enhanced accuracy, and a price match guarantee, Success.ai ensures you receive the highest-quality data at the best price in the market. Our datasets are delivered in Parquet format for easy integration into your systems, and with millions of profiles updated daily, you can trust that you’re always working with fresh, relevant data.

API Integration: Our datasets are easily accessible via API, allowing for seamless integration into your existing systems. This ensures that you can automate data retrieval and update processes, maintaining the flow of fresh, accurate information directly into your applications.

Global Reach and Industry Coverage: Our LinkedIn data covers professionals across all industries and sectors, providing you with detailed insights into businesses around the world. Our geographic coverage spans 259M profiles in the United States, 22M in the United Kingdom, 27M in India, and thousands of profiles in regions such as Europe, Latin America, and Asia Pacific. With LinkedIn company data, you can access profiles of top companies from the United States (6M+), United Kingdom (2M+), and beyond, helping you scale your outreach globally.

Why Choose Success.ai’s LinkedIn Data: Success.ai stands out for its tailored approach and white-glove service, making it easy for businesses to receive exactly the data they need without managing complex data platforms. Our dedicated Success Managers will curate and deliver your dataset based on your specific requirements, so you can focus on what matters most—reaching the right audience. Whether you’re sourcing employee data, LinkedIn profile data, or recruiting data, our service ensures a seamless experience with 99% data accuracy.

• Best Price Guarantee: We offer unbeatable pricing on LinkedIn data, and we’ll match any competitor.
• Global Scale: Access 700 million LinkedIn profiles and 70 million company records globally.
• AI-Verified Accuracy: Enjoy 99% data accuracy through our advanced AI and manual validation processes.
• Real-Time Data: Profiles are updated daily, ensuring you always have the most relevant insights.
• Tailored Solutions: Get custom-curated LinkedIn data delivered directly, without managing platforms.
• Ethically Sourced Data: Compliant with global privacy laws, ensuring responsible data usage.
• Comprehensive Profiles: Over 40 data points per profile, including job titles, skills, and company details.
• Wide Industry Coverage: Covering sectors from tech to finance across regions like the US, UK, Europe, and Asia.

Key Use Cases:

• Sales Prospecting and Lead Generation: Build targeted lead lists using LinkedIn company data and professional profiles, helping sales teams engage decision-makers at high-value accounts.
• Recruitment and Talent Sourcing: Use LinkedIn profile data to identify and reach top candidates globally. Our employee data includes work history, skills, and education, providing all the details you need for successful recruitment.
• Account-Based Marketing (ABM): Use our LinkedIn company data to tailor marketing campaigns to key accounts, making your outreach efforts more personalized and effective.
• Investment Research & Due Diligence: Identify companies with strong growth potential using LinkedIn company data. Access key data points such as funding history, employee count, and company trends to fuel investment decisions.
• Competitor Analysis: Stay ahead of your competition by tracking hiring trends, employee movement, and company growth through LinkedIn data. Use these insights to adjust your market strategy and improve your competitive positioning.
• CRM Data Enrichment: Enhance your CRM systems with real-time updates from Success.ai’s LinkedIn data, ensuring that your sales and marketing teams are always working with accurate and up-to-date information.
• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understandin...