In 2020, according to respondents surveyed, data masters typically leverage a variety of external data sources to enhance their insights. The most popular external data sources for data masters being publicly available competitor data, open data, and proprietary datasets from data aggregators, with **, **, and ** percent, respectively.

This table gives an overview of R & D expenditure per industry for a number of countries and for the Dutch provinces. R & D spending by companies gives an indication of the extent to which companies are willing to invest in developing new knowledge. In addition, this table shows foreign-funded R & D expenditure. R & D activities of companies funded by foreign parties provide a global picture of the international R & D money flows. Research and development stimulates the growth of innovative entrepreneurship (in the form of start-ups and fast growing companies).

Please note: In order to make an international comparison possible, the figures presented here use internationally comparable definitions, which sometimes differ from the definitions normally used by CBS. As a result, differences can occur between these figures and national figures published elsewhere on the CBS website.

Data available from 1990 to 2012.

Status of the figures: The external sources for this table provide regularly updated data for previous periods. For example, it often happens that countries still provide figures for older years. The reverse, that older figures are withdrawn, happens every now and then. These adjusted data are not marked as such in the table.

Changes as of 22 December 2017: No, table has been discontinued.

When are new figures coming? it’s not.

Interest has grown in analyzing primary internal data by using some independent external aggregated statistics for efficiency gain. However, when population heterogeneity exists, inappropriate incorporation may lead to a biased estimator. With multiple external sources under generalized estimation equations and possibly heterogeneous populations, we propose an integrated generalized moment method that can perform a data-driven selection of valid moment equations from external sources and make efficient parameter estimation simultaneously. Moment equation selection consistency and asymptotic normality are established for the proposed estimator. Further, when the sample sizes of all external sources are large compared to the internal sample size, asymptotically the proposed estimator is more efficient than the estimator based on the internal data only and is oracle-efficient in the sense that it is as efficient as the oracle estimator based on all valid moment equations. Simulation studies confirm the theoretical results and the efficiency of the proposed method empirically. An example is also included for illustration. Supplementary materials for this article are available online.

Abstract

Background: Adolescent girls in Kenya are disproportionately affected by early and unintended pregnancies, unsafe abortion and HIV infection. The In Their Hands (ITH) programme in Kenya aims to increase adolescents' use of high-quality sexual and reproductive health (SRH) services through targeted interventions. ITH Programme aims to promote use of contraception and testing for sexually transmitted infections (STIs) including HIV or pregnancy, for sexually active adolescent girls, 2) provide information, products and services on the adolescent girl's terms; and 3) promote communities support for girls and boys to access SRH services.

Objectives: The objectives of the evaluation are to assess: a) to what extent and how the new Adolescent Reproductive Health (ARH) partnership model and integrated system of delivery is working to meet its intended objectives and the needs of adolescents; b) adolescent user experiences across key quality dimensions and outcomes; c) how ITH programme has influenced adolescent voice, decision-making autonomy, power dynamics and provider accountability; d) how community support for adolescent reproductive and sexual health initiatives has changed as a result of this programme.

Methodology ITH programme is being implemented in two phases, a formative planning and experimentation in the first year from April 2017 to March 2018, and a national roll out and implementation from April 2018 to March 2020. This second phase is informed by an Annual Programme Review and thorough benchmarking and assessment which informed critical changes to performance and capacity so that ITH is fit for scale. It is expected that ITH will cover approximately 250,000 adolescent girls aged 15-19 in Kenya by April 2020. The programme is implemented by a consortium of Marie Stopes Kenya (MSK), Well Told Story, and Triggerise. ITH's key implementation strategies seek to increase adolescent motivation for service use, create a user-defined ecosystem and platform to provide girls with a network of accessible subsidized and discreet SRH services; and launch and sustain a national discourse campaign around adolescent sexuality and rights. The 3-year study will employ a mixed-methods approach with multiple data sources including secondary data, and qualitative and quantitative primary data with various stakeholders to explore their perceptions and attitudes towards adolescents SRH services. Quantitative data analysis will be done using STATA to provide descriptive statistics and statistical associations / correlations on key variables. All qualitative data will be analyzed using NVIVO software.

Study Duration: 36 months - between 2018 and 2020.

Geographic coverage

Narok and Homabay counties

Analysis unit

Households

Universe

All adolescent girls aged 15-19 years resident in the household.

Sampling procedure

The sampling of adolescents for the household survey was based on expected changes in adolescent's intention to use contraception in future. According to the Kenya Demographic and Health Survey 2014, 23.8% of adolescents and young women reported not intending to use contraception in future. This was used as a baseline proportion for the intervention as it aimed to increase demand and reduce the proportion of sexually active adolescents who did not intend to use contraception in the future. Assuming that the project was to achieve an impact of at least 2.4 percentage points in the intervention counties (i.e. a reduction by 10%), a design effect of 1.5 and a non- response rate of 10%, a sample size of 1885 was estimated using Cochran's sample size formula for categorical data was adequate to detect this difference between baseline and end line time points. Based on data from the 2009 Kenya census, there were approximately 0.46 adolescents girls per a household, which meant that the study was to include approximately 4876 households from the two counties at both baseline and end line surveys.

We collected data among a representative sample of adolescent girls living in both urban and rural ITH areas to understand adolescents' access to information, use of SRH services and SRH-related decision making autonomy before the implementation of the intervention. Depending on the number of ITH health facilities in the two study counties, Homa Bay and Narok that, we sampled 3 sub-Counties in Homa Bay: West Kasipul, Ndhiwa and Kasipul; and 3 sub-Counties in Narok, Narok Town, Narok South and Narok East purposively. In each of the ITH intervention counties, there were sub-counties that had been prioritized for the project and our data collection focused on these sub-counties selected for intervention. A stratified sampling procedure was used to select wards with in the sub-counties and villages from the wards. Then households were selected from each village after all households in the villages were listed. The purposive selection of sub-counties closer to ITH intervention facilities meant that urban and semi-urban areas were oversampled due to the concentration of health facilities in urban areas.

Qualitative Sampling

Focus Group Discussion participants were recruited from the villages where the ITH adolescent household survey was conducted in both counties. A convenience sample of consenting adults living in the villages were invited to participate in the FGDS. The discussion was conducted in local languages. A facilitator and note-taker trained on how to use the focus group guide, how to facilitate the group to elicit the information sought, and how to take detailed notes. All focus group discussions took place in the local language and were tape-recorded, and the consent process included permission to tape-record the session. Participants were identified only by their first names and participants were asked not to share what was discussed outside of the focus group. Participants were read an informed consent form and asked to give written consent. In-depth interviews were conducted with purposively selected sample of consenting adolescent girls who participated in the adolescent survey. We conducted a total of 45 In-depth interviews with adolescent girls (20 in Homa Bay County and 25 in Narok County respectively). In addition, 8 FGDs (4 each per county) were conducted with mothers of adolescent girls who are usual residents of the villages which had been identified for the interviews and another 4 FGDs (2 each per county) with CHVs.

Sampling deviation

N/A

Mode of data collection

Face-to-face [f2f] for quantitative data collection and Focus Group Discussions and In Depth Interviews for qualitative data collection

Research instrument

The questionnaire covered; socio-demographic and household information, SRH knowledge and sources of information, sexual activity and relationships, family planning knowledge, access, choice and use when needed, exposure to family planning messages and voice and decision making autonomy and quality of care for those who visited health facilities in the 12 months before the survey. The questionnaire was piloted before the data collection and the questions reviewed for appropriateness, comprehension and flow. The questionnaire was piloted among a sample of 42 adolescent girls (two each per field interviewer) 15-19 from a community outside the study counties.

The questionnaire was originally developed in English and later translated into Kiswahili. The questionnaire was programmed using ODK-based Survey CTO platform for data collection and management and was administered through face-to-face interview.

Cleaning operations

The survey tools were programmed using the ODK-based SurveyCTO platform for data collection and management. During programming, consistency checks were in-built into the data capture software which ensured that there were no cases of missing or implausible information/values entered into the database by the field interviewers. For example, the application included controls for variables ranges, skip patterns, duplicated individuals, and intra- and inter-module consistency checks. This reduced or eliminated errors usually introduced at the data capture stage. Once programmed, the survey tools were tested by the programming team who in conjunction with the project team conducted further testing on the application's usability, in-built consistency checks (skips, variable ranges, duplicating individuals etc.), and inter-module consistency checks. Any issues raised were documented and tracked on the Issue Tracker and followed up to full and timely resolution. After internal testing was done, the tools were availed to the project and field teams to perform user acceptance testing (UAT) so as to verify and validate that the electronic platform worked exactly as expected, in terms of usability, questions design, checks and skips etc.

Data cleaning was performed to ensure that data were free of errors and that indicators generated from these data were accurate and consistent. This process begun on the first day of data collection as the first records were uploaded into the database. The data manager used data collected during pilot testing to begin writing scripts in Stata 14 to check the variables in the data in 'real-time'. This ensured the resolutions of any inconsistencies that could be addressed by the data collection teams during the fieldwork activities. The Stata 14 scripts that perform real-time checks and clean data also wrote to a .rtf file that detailed every check performed against each variable, any inconsistencies encountered, and all steps that were taken to address these inconsistencies. The .rtf files also reported when a variable was

Snapshot img

Data Analytics Market Size 2025-2029

The data analytics market size is forecast to increase by USD 288.7 billion, at a CAGR of 14.7% between 2024 and 2029.

The market is driven by the extensive use of modern technology in company operations, enabling businesses to extract valuable insights from their data. The prevalence of the Internet and the increased use of linked and integrated technologies have facilitated the collection and analysis of vast amounts of data from various sources. This trend is expected to continue as companies seek to gain a competitive edge by making data-driven decisions. However, the integration of data from different sources poses significant challenges. Ensuring data accuracy, consistency, and security is crucial as companies deal with large volumes of data from various internal and external sources. Additionally, the complexity of data analytics tools and the need for specialized skills can hinder adoption, particularly for smaller organizations with limited resources. Companies must address these challenges by investing in robust data management systems, implementing rigorous data validation processes, and providing training and development opportunities for their employees. By doing so, they can effectively harness the power of data analytics to drive growth and improve operational efficiency.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleIn the dynamic and ever-evolving the market, entities such as explainable AI, time series analysis, data integration, data lakes, algorithm selection, feature engineering, marketing analytics, computer vision, data visualization, financial modeling, real-time analytics, data mining tools, and KPI dashboards continue to unfold and intertwine, shaping the industry's landscape. The application of these technologies spans various sectors, from risk management and fraud detection to conversion rate optimization and social media analytics. ETL processes, data warehousing, statistical software, data wrangling, and data storytelling are integral components of the data analytics ecosystem, enabling organizations to extract insights from their data. Cloud computing, deep learning, and data visualization tools further enhance the capabilities of data analytics platforms, allowing for advanced data-driven decision making and real-time analysis. Marketing analytics, clustering algorithms, and customer segmentation are essential for businesses seeking to optimize their marketing strategies and gain a competitive edge. Regression analysis, data visualization tools, and machine learning algorithms are instrumental in uncovering hidden patterns and trends, while predictive modeling and causal inference help organizations anticipate future outcomes and make informed decisions. Data governance, data quality, and bias detection are crucial aspects of the data analytics process, ensuring the accuracy, security, and ethical use of data. Supply chain analytics, healthcare analytics, and financial modeling are just a few examples of the diverse applications of data analytics, demonstrating the industry's far-reaching impact. Data pipelines, data mining, and model monitoring are essential for maintaining the continuous flow of data and ensuring the accuracy and reliability of analytics models. The integration of various data analytics tools and techniques continues to evolve, as the industry adapts to the ever-changing needs of businesses and consumers alike.

How is this Data Analytics Industry segmented?

The data analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ComponentServicesSoftwareHardwareDeploymentCloudOn-premisesTypePrescriptive AnalyticsPredictive AnalyticsCustomer AnalyticsDescriptive AnalyticsOthersApplicationSupply Chain ManagementEnterprise Resource PlanningDatabase ManagementHuman Resource ManagementOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKMiddle East and AfricaUAEAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Component Insights

The services segment is estimated to witness significant growth during the forecast period.The market is experiencing significant growth as businesses increasingly rely on advanced technologies to gain insights from their data. Natural language processing is a key component of this trend, enabling more sophisticated analysis of unstructured data. Fraud detection and data security solutions are also in high demand, as companies seek to protect against threats and maintain customer trust. Data analytics platforms, including cloud-based offeri

Motivation

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data has been periodically included in the dataset until the end of the COVID-19 pandemic.

We stopped updating the dataset after December 2022. Previous files have been fixed after a thorough sanity check.

License

See LICENSE.txt

Disclaimer

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted. Fields origin or destination are empty when no airport could be found.

Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.

Description of the dataset

One file per month is provided as a csv file with the following features:

callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)

number: the commercial number of the flight, when available (the matching with the callsign comes from public open API); this field may not be very reliable;

icao24: the transponder unique identification number;

registration: the aircraft tail number (when available);

typecode: the aircraft model type (when available);

origin: a four letter code for the origin airport of the flight (when available);

destination: a four letter code for the destination airport of the flight (when available);

firstseen: the UTC timestamp of the first message received by the OpenSky Network;

lastseen: the UTC timestamp of the last message received by the OpenSky Network;

day: the UTC day of the last message received by the OpenSky Network;

latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;

latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.

Examples

Possible visualisations and a more detailed description of the data are available at the following page:

Credit

If you use this dataset, please cite:

Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders "Crowdsourced air traffic data from the OpenSky Network 2019–2020" Earth System Science Data 13(2), 2021 https://doi.org/10.5194/essd-13-357-2021

A major challenge for rare disease clinical trials is the limited amount of available information for making robust statistical inference. While external data present information integration opportunities to enhance statistical inference, conventional data combining methods, for example, meta-analysis, usually do not adequately address study population differences. Matching methods, on the other hand, directly account for population characteristics but often lead to inefficient use of data by underutilizing unmatched data points. Aiming at a better bias-variance tradeoff, we propose an intuitive integrated inference framework to borrow information from all relevant data sources and make inference on the response of interest over a target population precisely characterized by the joint distribution of baseline covariates. The method is easily implemented and can be complemented by modern statistical learning or machine learning tools. Statistical inference is facilitated by the bootstrap. We argue that the integrated inference framework not only provides an intuitive and coherent perspective for a variety of clinical trial inference problems but also has broad application areas in clinical trial settings and beyond, as a quantitative data integration tool for making robust inference in a target population precise manner for policy and decision makers.

Training Data and External Testsets from the TopCoW Challenge

This Zenodo upload accompanies the TopCoW challenge summary paper (see Citation). It contains the training data released by the TopCoW challenge and four external testsets used in the paper's analysis and benchmark. We create this Zenodo record as a permanent release of the TopCoW challenge data. The external testsets, which came from public sources and have all been annotated in the same fashion as the TopCoW training data, serve as continued benchmark for anyone interested in reproducing the evaluations in our paper. The combined data also serve as a jumping-off point to generalize the TopCoW labels to other datasets.

For more details and analysis on the data in this upload, please refer to our challenge summary paper. If you use the data from this Zenodo upload, please cite the TopCoW challenge paper (see Citaion).

We also have a Zenodo "Software" upload with the best performing dockers from the TopCoW challenge. Check it out:

Best Performing TopCoW Segmentation Dockers

Contents of Data

Each zip contains the MRA and/or CTA images (in LPS+ orientation), and the labels: CoW segmentation masks, CoW region of interest (ROI), and CoW graphs. The data ZIPs typically have the following sub-folders:

• All the images are stored in the sub-folder `imagesTr`. The images are angiographic scans in nifti format, LPS+ orientation.
• The CoW segmentation masks are in the sub-folder `cow_seg_labelsTr`.
• Size and location for the CoW ROI in text file format are in `roi_loc_labelsTr`. The x,y,z of the ROI correspond to LPS+ orientation.
• Yml files indicating the presence of edges (0: absent, 1: present) of CoW graphs are stored in sub-folder `antpos_edges_labelsTr`.

Additionally, for each external testset, we also provide a `raw/` sub-folder with raw images and their braincase cropping and CoW labels:

• `raw/raw_imgs`: raw images from the public external datasets
• `raw/cow_seg_labels_for_raw_imgs`: CoW masks that have reversed the crop-orient steps to map back to the raw images (they are otherwise identical to the masks in `cow_seg_labelsTr` except the coordinate and orientation)
• `raw/braincase_ROI_after_LPS_reorient`: braincase ROI to crop the LPS-reoriented raw images

Note: The contents under `raw/` in external testsets are for transparency purpose and help demonstrate the steps to convert external dataset to be compatible with TopCoW training data. Conversely, the `raw/` data also serve as plug-and-play TopCoW annotations for the respective external datasets. But for TopCoW context, you do not need to use `raw/`.

(Optional: Raw to TopCoW Compatible)

The external testsets in this release were pre-processed in the same manner as the TopCoW training data. These steps include LPS+ reorientation, cropping to the braincase region, and defacing, as described in our paper. The original raw images from the external testsets are stored in `raw/`. You can use similar steps to convert any new dataset to be compatible with TopCoW training data. Here are some tips:

As highlighted in our Best Performing TopCoW Segmentation Dockers zenodo upload, the only pre-processing needed for TopCoW-compatibility for any new dataset is the LPS+ orientation. We have already uploaded the `reorient_nii.py` in the best-dockers zenodo link. You can use it to re-orient new images.

The external testsets in this release have been cropped to the braincase region using the braincase ROI text files in the sub-folder `raw/braincase_ROI_after_LPS_reorient`. Braincase cropping is optional for docker inference, but it will make the data more similar to the TopCoW training data. You can use our example code snippet here to crop the braincase region (after you have reoriented the raw image to LPS+):

https://github.com/CoWBenchmark/TopCoW_Eval_Metrics/blob/master/topcow24_eval/utils/crop_sitk.py

Info on Each ZIP

TopCoW2024_Data_Release.zip

• Training data release zip used in the TopCoW challenge
• License: "Open use. Must provide the source. Use for commercial purposes requires permission of the data owner." as defined by the OpenData Swiss
• Refer to the README and License in the zip for details and citation

CTA_ISLES2024_TUM.zip

• 26 CTA cases from the training data of the ISLES 2024 challenge. The cases were from TUM center.
• Original License: Creative Commons license CC BY-NC (Attribution-NonCommercial)
• On top of the TopCoW citation, please also cite the ISLES24 works (https://arxiv.org/abs/2408.11142" href="https://arxiv.org/abs/2408.11142" target="_blank" rel="noopener noreferrer">data descritptor and https://arxiv.org/abs/2408.10966" href="https://arxiv.org/abs/2408.10966" target="_blank" rel="noopener noreferrer">benchmarking preprints) when using our derived and annotated version

CTA_LargeIA.zip

• 20 CTA cases from the "LargeIA" dataset on Zenodo and paper. Our 20 selected cases do NOT have aneurysms in the CoW ROI.
• As requested by the LargeIA authors, please acquire the original images from their Zenodo link above.
• We provide the CoW masks corresponding to the raw images in the sub-folder `raw/cow_seg_labels_for_raw_imgs`.
• You can also use the `reorient_nii.py` and `crop_sitk.py` (or similar) to reorient the raw image to LPS and crop to the braincase region (the braincase ROI is provided in the `braincase_ROI_after_LPS_reorient` sub-folder under `raw/`). After LPS-reorientation and braincase cropping with the provided braincase ROI, the resulting cropped image should match the CoW masks provided in sub-folder `cow_seg_labelsTr`.
• On top of the TopCoW citation, please also cite their Patterns 2021 paper when using our derived and annotated version

MRA_Lausanne.zip

• 20 MRA cases from the OpenNeuro Lausanne TOF-MRA Aneurysm Cohort dataset. Our 20 selected cases do NOT have aneurysms as they are from the control group.
• Original License: CC0 “No Rights Reserved”
• On top of the TopCoW citation, please also cite their Neuroinformatics 2022 paper when using our derived and annotated version

MRA_IXI_HH.zip

• 20 MRA cases from the IXI dataset. Our 20 selected cases were from the HH hospital.
• Original License: Creative Commons CC BY-SA 3.0 license
• On top of the TopCoW citation, please also cite the IXI dataset website when using our derived and annotated version

Citation

The dataset in this Zenodo upload were released by the TopCoW challenge and used for benchmarking analysis. If you use the TopCoW released data, or our derived and annotated versions of the external testsets, please cite our TopCoW challenge summary paper:

Yang, K., Musio, F., Ma, Y., Juchler, N., Paetzold, J. C., Al-Maskari, R., ... & Menze, B. (2024). Benchmarking the cow with the topcow challenge: Topology-aware anatomical segmentation of the circle of willis for cta and mra. ArXiv, arXiv-2312.

On top of the TopCoW paper citation, please also cite the relevant external dataset publications from the above bullet-points in "Info on Each ZIP".

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new version of a feature is defined by any change to the

As ESG investing has gone mainstream in the public equity space, the spotlight has turned to fixed income investments and sovereign bonds in particular. Investors increasingly recognize that ESG factors, such as corruption, climate protection, and human rights, could impact the long-term solvency of government bond issuers.

ISS ESG’s Country Controversy Assessment enables investors to assess a country’s exposure to various controversies, including alignment with international norms and conventions, to effectively manage potential ESG risks and opportunities. The assessment covers various ESG factors across the following themes:

• Authoritarian Regime • Biodiversity • Child Labour • Climate Protection • Coal Power • Corruption • Death Penalty • Discrimination • Euthanasia • Freedom of Association • Freedom of Speech & Press • Global Peace Index • Human Rights • Labour Rights • Military Budget • Money Laundering • Nuclear Power • Nuclear Weapons • Whaling

Coverage for the Country Controversy Assessment includes more than 800 sovereign issuers:

• Approximately 100% coverage of global sovereign debt issued • More than 120 countries as well as Hong Kong and the European Union • All member countries of the European Union and the OECD

Analysts gather the controversy-related information from credible and acknowledged external sources, such as indices and blacklists, and the ISS ESG Country Rating to deliver high-quality, relevant and actionable data.

Examples of sources:

• Amnesty International • Financial Action Task Force • Germanwatch • Stockholm International Peace Research Institute

Data is used by a broad range of institutional investors, asset managers, asset owners, fund managers, banks, government institutions, universities and research firms.

This table provides an international overview of several aspects of how the government functions in relation to the investment climate. The functioning of the apparatus of government is about two roles, namely: (1) the government corrects markets that do not work well. It is expressed by the degree in which the government exerts influence on economic activity (for example by state control, sectoral and ad hoc state support and rules for starting up a business); (2) the government as a market party, for example as a supplier of online basic public services. Note: Comparable definitions are used to compare the figures presented internationally. The definitions sometimes differ from definitions used by Statistics Netherlands. The figures in this table could differ from Dutch figures presented elsewhere on the website of Statistics Netherlands. Data available from 1990 up to 2012. Status of the figures: The external sources of these data frequently supply adjusted figures on preceding periods. These adjusted data are not mentioned as such in the table. Changes as of 22 December 2017: No, table is stopped. When will new figures be published? Not.

Iterative Learning Control (ILC) is renowned for its capability to achieve precise tracking control for systems with repetitive actions at a fixed time interval. However, pursuing the dual objective of high-precision tracking and rapid convergence is a persistent challenge in the field of learning control. To address this problem, a novel ILC method is designed for a class of discrete-time linear systems subject to non-repetitive disturbances in this paper. Particularly, the updating term in ILC is constructed inspired by the principle of sliding mode control (SMC), which results in the learning process being divided into two distinct stages: a rapid reaching stage and a slow sliding stage. As a result, a balance between convergence speed and tracking performance can be ensured via the proposed ILC method. In addition, to attenuate the effects of non-repetitive disturbances, the disturbance compensation mechanism is integrated into the proposed ILC method. Moreover, the optimal value of the learning gain can be determined using the predicted root mean square (RMS) errors of subsequent iterations, eliminating the need for additional tuning actions. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed new ILC method. Note to Practitioners—For many mechanical components in mechatronic systems and robotics, the motions are repeatable. Iterative learning control (ILC) is a well-established technique ideally suited for enhancing the performance of such repetitive tasks without excessive requirements on sensor-feedback quality or control-loop bandwidth. However, most existing ILC approaches in the literature primarily focus on improving convergence accuracy, while little attention is paid to convergence speed in the iteration domain, especially in the presence of disturbances. This paper addresses the limitations of classical ILC schemes, and draws inspiration from the sliding mode control (SMC) technique. To be specific, a novel SMC-based ILC algorithm is proposed that allows to achieve a good balance between the fast convergence and precise tracking performance, especially in case of iteration variant disturbances. Also, it will be shown how the optimal learning gains can be determined. Base on the examples of multi-axis gantry robot and injection molding process, simulations support the theoretical results, and meanwhile show the effectiveness and advantage of the proposed ILC strategy.

The ckanext-importlib extension provides a library to facilitate automated or continuous dataset imports into CKAN using the API. It particularly addresses challenges associated with repetitive imports, such as checking for existing datasets based on a unique ID stored in extras, and managing related resources. The extension offers tools designed to support scenarios where CKAN data is continuously updated from external sources. Key Features: Dataset Existence Checks: Facilitates checking for the existence of datasets based on a unique identifier stored as an extra field, allowing for updates rather than duplications during re-imports. It can optionally consider an additional "source" extra field during this check. Resource Grouping: Provides functionality, exemplified by ResourceSeriesLoader, to manage grouped resources within datasets, specifically designed for handling time series data. Name Clashing Avoidance: Aims to avoid naming conflicts when deriving unique dataset names from titles by implementing mechanisms to prevent clashes. Framework Design: While not as flexible as initially intended, the extension architecture provides a framework for handling continuous data imports into CKAN and can be extended based on specific project requirements. Example Importer: The extension was designed as a generalized framework, based around the specific implementations for data.gov.uk ONS importer which serves an example in how to use the library. Use Cases: Continuous Data Feeds: Organizations that need to continuously import and update data from external sources can use ckanext-importlib to automate the process, ensuring data in CKAN remains current and accurate. Managing Time Series Data: Datasets with multiple data files, such as time series, can use the resource management features to ensure that these resources are properly linked and organized within the dataset. Data Synchronization: When mirroring or integrating datasets from other systems into CKAN, this extension simplifies the process of checking for existing data and updating it as needed, thereby maintaining data integrity. Technical Integration: To get the extension running, installation of dependencies mentioned within the source code repository is needed. This includes CKAN and it's dependencies followed by those specifically listed in pip-requirements.txt specific to this extension. Benefits & Impact: The ckanext-importlib streamlines the process of continually importing datasets into CKAN and improves system efficiency and data integrity. By providing mechanisms to handle updates and avoid data duplication, it ensures that effort isn't wasted on handling data discrepancies.

Overview The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control. National Geospatial Data Asset This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee. Dataset Source Details Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground. Cartographic Visualization The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below. Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://res1datad-o-tgeodatad-o-tstated-o-tgov.vcapture.xyz/guidance/index.html Contact Direct inquiries to internationalboundaries@state.gov. Direct download: https://res1datad-o-tgeodatad-o-tstated-o-tgov.vcapture.xyz/LSIB.zip Attribute Structure The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB. Core Attributes The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields. County Code and Country Name Fields “CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard. The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user. Descriptive Fields The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line. ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively. The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps. The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line. Use of Core Attributes in Cartographic Visualization Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between: International Boundaries (Rank 1); Other Lines of International Separation (Rank 2); and Special Lines (Rank 3). Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction. The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling. Use of the “CC1,” “CC1_GENC3,” “CC2,” “CC2_GENC3,” “RANK,” or “NOTES” fields for cartographic labeling purposes is prohibited. Extension Attributes Certain elements of the attributes within the LSIB dataset extend data functionality to make the data more interoperable or to provide clearer linkages to other datasets. The fields “CC1_GENC3” and “CC2_GENC” contain the corresponding three-character GENC code to the “CC1” and “CC2” attributes. The code “QX2” is the three-character counterpart of the code “Q2,” which denotes a line in the LSIB representing a boundary associated with a geographic area not contained within the GENC standard. To allow for linkage between individual lines in the LSIB and World Polygons dataset, the “CC1_WPID” and “CC2_WPID” fields contain a Universally Unique Identifier (UUID), version 4, which provides a stable description of each geographic entity in a boundary pair relationship. Each UUID corresponds to a geographic entity listed in the World Polygons dataset. These fields allow for linkage between individual lines in the LSIB and the overall World Polygons dataset. Five additional fields in the LSIB expand on the UUID concept and either describe features that have changed across space and time or indicate relationships between previous versions of the feature. The “LSIB_ID” attribute is a UUID value that defines a specific instance of a feature. Any change to the feature in a lineset requires a new “LSIB_ID.” The “ANTECIDS,” or antecedent ID, is a UUID that references line geometries from which a given line is descended in time. It is used when there is a feature that is entirely new, not when there is a new version of a previous feature. This is generally used to reference countries that have dissolved. The “PREVIDS,” or Previous ID, is a UUID field that contains old versions of a line. This is an additive field, that houses all Previous IDs. A new

This table shows the international developments in the capital stock and the investments. Beside the picture of the total economy, a category has been made for ICT (information and communication technology). The table is related both to the physical capital stock and its renewal or extension by means of (foreign) capital investments, and to the money that is necessary to finance the investments, in particular the venture capital. The scope of the capital and the investments in a country are mainly defined by the propensity of entrepreneurs to invest. Investment behaviour is partly defined by the investment climate. Note: Comparable definitions are used to compare the figures presented internationally. The definitions sometimes differ from definitions used by Statistics Netherlands. The figures in this table could differ from Dutch figures presented elsewhere on the website of Statistics Netherlands. Data available from 1990 up to 2012. Status of the figures: The external source of these data frequently supplies adjusted figures on preceding periods. For example, it often happens that countries still provide figures on older years. The reverse, older figures being withdrawn, also happens now and then. These adjusted data are not mentioned as such in the table. Changes as of 22 December 2017: No, table is stopped. When will new figures be published? Not.

The EV telemetry dataset provides comprehensive insights into various aspects of electric vehicle (EV) performance and user behavior, all sourced with informed consent from EV drivers who opt in to share their data. This dataset captures a wide range of information related to battery performance, including state of charge (SOC), state of health (SOH), temperature, voltage, and current metrics. These data points are crucial for understanding how the battery behaves under different conditions, offering valuable insights into factors like battery degradation, charging cycles, and overall efficiency.

In addition to battery data, the dataset also collects charging behavior information, such as charging time, charging power, and location of charging stations. This helps in understanding user preferences, the frequency and duration of charging sessions, and the relationship between different types of charging infrastructure and vehicle performance. Charging patterns across different regions can also provide valuable insights into infrastructure gaps, enabling better planning and placement of charging stations.

The dataset also includes real-time data from the vehicle’s onboard systems, which provides a detailed view of the driving habits and vehicle usage. This data includes vehicle speed, acceleration, braking patterns, energy consumption during travel, and route information. By analyzing these metrics, it is possible to evaluate how driving behavior affects energy efficiency and battery health.

Moreover, data from external sources, such as the surrounding environmental conditions, weather data, and grid data, may also be integrated into the dataset, providing a more comprehensive understanding of how external factors influence battery performance and charging behavior. For example, temperature can significantly impact both the performance of the EV battery and the charging process, and analyzing this relationship can lead to insights on how to optimize charging schedules and battery management systems.

The combination of battery metrics, charging behavior, driving patterns, and environmental data offers a holistic view of EV performance, enabling better decision-making in areas like grid optimization, energy distribution, and charging infrastructure design. The dataset not only supports more efficient energy management but also helps to drive innovations in battery technology, vehicle-to-grid (V2G) integration, and sustainable transportation solutions. Through careful analysis of this rich data, stakeholders can gain critical insights into how to optimize EV usage, enhance battery lifespan, and improve overall grid efficiency, all while ensuring that users maintain full control over their personal data through clear, informed consent.

The size of the Cloud Storage market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 24.00% during the forecast period.Cloud storage is the technology by which the user can store data remotely and access it over the internet. Instead of storing data on the local computer's device, for example in the form of a hard drive or a USB drive, the cloud storage places the data on remote servers that could be accessed via any device connected with internet, providing it with flexibility and convenience.It is applied in every walk of life, beginning with personal usage in the storage of files, data backup, and sharing photos and documents to business applications for data back-up, disaster recovery, remote collaboration, and sharing files. Big data analytics, machine learning, and artificial intelligence form another major application domain since the scalability and accessibility that is needed for processing and storing big datasets are offered by cloud storage.This market for cloud storage has been experiencing rapid growth owing to increasing adoption of cloud computing, increased demand for data storage and backup, growing needs for remote work, and collaboration. Increasing business and individual dependence on the cloud-based solutions will propel the global market for cloud storage further in the following years. Recent developments include: October 2022: IBM announced the addition of Red Hat associate teams and Red Hat storage product roadmaps to the IBM Storage business unit, bringing consistent data and application storage across on-premises infrastructure and cloud. With this addition, the company will integrate the Red Hat OpenShift Data Foundation (ODF) storage technologies as the foundation for IBM Spectrum Fusion., April 2022: Alibaba Cloud and VMware have released the next iteration of their jointly developed public cloud service, Alibaba Cloud VMware Service, to assist organizations throughout China in accelerating their digital transformation. The service promises to help Chinese organizations migrate and modernize applications more quickly, shifting workloads between on-premises VMware infrastructures and Alibaba Cloud at scale.. Key drivers for this market are: Increase in Cloud Adoption Across Organizations, Growing Demand for Low-cost Storage and .Faster Data Accessibility. Potential restraints include: High Dependence on External Sources to Balance the Skill Deficit, Vendor Lock In; Compliance Issues, Migration Complexity, And Security Risks. Notable trends are: BFSI Expected to Hold a Significant Share.

Analysis of ‘US Health Insurance Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/teertha/ushealthinsurancedataset on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

The venerable insurance industry is no stranger to data driven decision making. Yet in today's rapidly transforming digital landscape, Insurance is struggling to adapt and benefit from new technologies compared to other industries, even within the BFSI sphere (compared to the Banking sector for example.) Extremely complex underwriting rule-sets that are radically different in different product lines, many non-KYC environments with a lack of centralized customer information base, complex relationship with consumers in traditional risk underwriting where sometimes customer centricity runs reverse to business profit, inertia of regulatory compliance - are some of the unique challenges faced by Insurance Business.

Despite this, emergent technologies like AI and Block Chain have brought a radical change in Insurance, and Data Analytics sits at the core of this transformation. We can identify 4 key factors behind the emergence of Analytics as a crucial part of InsurTech:

• Big Data: The explosion of unstructured data in the form of images, videos, text, emails, social media
• AI: The recent advances in Machine Learning and Deep Learning that can enable businesses to gain insight, do predictive analytics and build cost and time - efficient innovative solutions
• Real time Processing: Ability of real time information processing through various data feeds (for ex. social media, news)
• Increased Computing Power: a complex ecosystem of new analytics vendors and solutions that enable carriers to combine data sources, external insights, and advanced modeling techniques in order to glean insights that were not possible before.

This dataset can be helpful in a simple yet illuminating study in understanding the risk underwriting in Health Insurance, the interplay of various attributes of the insured and see how they affect the insurance premium.

Content

This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. There are no missing or undefined values in the dataset.

Inspiration

This relatively simple dataset should be an excellent starting point for EDA, Statistical Analysis and Hypothesis testing and training Linear Regression models for predicting Insurance Premium Charges.

Proposed Tasks: - Exploratory Data Analytics - Statistical hypothesis testing - Statistical Modeling - Linear Regression

--- Original source retains full ownership of the source dataset ---

Points of interest (POI) includes a wide array of features. These features are usually locations that are of interest to the public. The data is collected from a variety of sources, including web searches, data from external partners, and data from internal departments.Data categories range from institutional to public housing to early years centres. Some examples of the categories contained within the data are:Arts, museum, and cultural spacesEmergency responder stations: fire, police, and paramedicsInstitutional buildings: city/town halls, court houses, librariesHospitals, medical centres, and walk-in clinicsHousing: public housing, co-operative housing, sheltersFood banksLong term care homes and retirement homesPost officesRecreation centres and other municipal meeting places: arenas, pools, community centres, meeting hallsSettlement services and other related services for immigrants and newcomersShopping centres: plazas, big box centres, and mallsTransportation: airports, major bus stations, and passenger rail stationsPlease note that the Interim Place shelters must not be shared publicly or externally.

The California Monitoring Plan (CMP) salmonid monitoring areas and associated population data are part of an ongoing effort to summarize existing and past salmonid monitoring efforts in the areas identified by Adams et al. 2011. These data are compiled and maintained by the California Department of Fish and Wildlife with the cooperation of monitoring practitioners. Updates and associated outreach are intended to occur on an annual basis. Data were created from several sources and existing datasets: some monitoring areas were accurately depicted using the USGS National Hydrography Dataset (NHD), other monitoring areas were approximated using the monitoring point location and the USGS StreamStats tool to depict the watershed area above that point. The areas are intended to represent the approximate extent of sampling within sub-basins, watershed areas, or regions. For example, the spatial extent of monitoring using a fixed count station is approximated by accounting for all anadromous fish habitat upstream of the sampling location. Therefore, the area is approximated by entering the monitoring location coordinates into the StreamStats tool. The resulting shapefile is then examined to ensure the watershed area did not include habitat above dams or barriers to migration. Areas were clipped when needed. The data user should recognize that errors may have occurred during production of this dataset, changes may have occurred to the external sources used post transfer, and for other possible reasons. The population metrics summarized in the associated tabular data may be regarded as spatially limited, temporally limited, and not considered a complete estimate for the population being described. The data user is advised to refer to the annual reports cited in the Source field from the tabular data for additional details regarding monitoring within the area spatially depicted.Abbreviation Definitions: SGS = Spawning Ground Survey, RM = River Mile, RST = Rotary Screw Trap, RKM