archive containing one file per project: Each file is a comma separated table with the following:* project name* developer's name* developer's ID (as assigned by CVSAnalY https://github.com/MetricsGrimoire/CVSAnalY)

The Canadian Institute for Cybersecurity has published several datasets for network intrusion detection. Four of them: CIC-IDS2017, CIC-DoS2017, CSE-CIC-IDS2018 and CIC-DDoS2019 are collated here into one collection, cleaned up and with harmonized labeling.

The intent behind this collection is simple: to have a larger, more varied set of NIDS samples for more powerful analyses by researchers. Too often, researchers still rely on the individual datasets even though the full set is compatible out-of-the-box. The parts have been created for the same purpose and they have been processed with the same feature extraction tool chain.

This collection also takes into account 2 articles in which flawed features were discovered. Those features have been removed from the dataset. See the cleanup notebook for more information.

If you make use of this combined version, please credit the original authors. The relevant publications are cited here on Kaggle alongside the individual datasets and they are also readily available at the CIC's official dataset distribution page

This dataset includes magnetotelluric transfer functions in the form of EDI files for 16 stations collected by the USGS and 40 stations collected by Quantec Geoscience for Lawerence Berkeley National Lab around the Mountain Home area in Idaho. A 3D electrical resistivity model is included that images resistive and conductive bodies in the subsurface that maybe important for geothermal characterization. The model was created using ModEM using the high performance computer Yeti at the USGS.

Indonesia ID: External Debt: NFL: CB: Commerical Banks data was reported at -47.526 USD mn in 2019. This stayed constant from the previous number of -47.526 USD mn for 2018. Indonesia ID: External Debt: NFL: CB: Commerical Banks data is updated yearly, averaging -31.281 USD mn from Dec 1975 (Median) to 2019, with 36 observations. The data reached an all-time high of 575.000 USD mn in 1975 and a record low of -1.007 USD bn in 2002. Indonesia ID: External Debt: NFL: CB: Commerical Banks data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank.IDS: External Debt: Net Flows and Net Transfers: Annual. Central bank bilateral debt includes Central bank debt with commercial banks. The central bank is the financial institution (or institutions) that exercises control over key aspects of the financial system. The monetary authority, normally the agency that issues currency and holds the country’s international reserves. Net flows (or net lending or net disbursements) received by the borrower during the year are disbursements minus principal repayments. Data are in current U.S. dollars.

US Congress Legislators Historical Data

A Detailed Dataset on Past and Present US Legislators' Profiles and Terms

By GovTrack [source]

About this dataset

This dataset provides comprehensive information on current and historical US legislators and their terms. The data includes diverse details, such as biographical data - names, gender, religion - along with IDs from multiple systems like Bioguide ID, FEC ID, GovTrack ID along with an official full name according to the House or Senate. It also captures alternate names used officially by legislators if they undergo a legal name change.

Moreover, the dataset also contains legislator identifiers from other websites such as OpenSecrets.org (an alphanumeric ID), VoteSmart.org (numeric stored as integer), VoteView.com (numeric stored as integer), C-SPAN's video website(numeric stored as integer), Wikipedia page names (alphanumeric), Ballotpedia page names(alphanumeric) and maplight.org(numeric).

Regarding the terms of each election held for legislators, key information found in this package includes state inclination in two-letter USPS abbreviation format alongside district numbers for representatives' service areas. For senators' specifics - there are inputs about their election class(1 2 or 3). Additionally captured are details around leadership roles – titles within parties plus dates of service.

Also included is rich contextual tell-tale about a legislator's political associations – party affiliations at both start & end dates indicating any switches during legislative term tenures.

The dataset extends itself beyond just being an academic resource; it helps build intuitive connections via RSS feeds URLs while offering details around their Washington DC office contact points – address suitably detailed room-wise plus phone/fax numbers alongside web URLs besides standalone contact page pointers.

Lastly but uniquely marks out official social media presence which includes Twitter handles/IDs & Facebook usernames/IDs further improving handle-based access for tools driven by API communication suggesting its utility not confined to structured academic research alone but extending to unstructured data handling digital companies specializing in sentiment analysis over multiple platforms/sources offering end-to-end integration or maybe be it organizations cross vérifying objective details over federal election claims by mapping FEC IDs to social media campaigns.

The dataset serves a wide array of researchers, policy analysts, political theorists, and technology centric analytics businesses. Conversely it can also help the curious public in learning about historical & current political landscapes in the US while checking their representatives' official web presence thereby fostering community engagement not just around elections but also during legislative tenures

How to use the dataset

This comprehensive dataset contains information on current and historical US Legislators and their terms. It can be used in a multitude of ways, such as academic research, journalism, policy making or for general interest. Here's a guide on how you can use this data:

Broad Overview:

Firstly, it's helpful to examine the broad layout of the data by taking an overall look at all files in the set: legislators-current.csv, legislators-historical.csv, legislators-current-terms.csv and legislators-historical-terms.csv.

The 'current' and 'historical' datasets pertain to sitting members of congress or those from past terms respectively.

The legislator files contain biographical information such as names (including possible name changes), gender and religion of each member whereas the term files hold details about their political careers including term type (senate or representative), state represented, district if relevant along with party affiliation.

Biographical Research:

You could use this data to create biographies for every legislator by collating personal information from first\_name, middle\_name, last\_name, suffix\_name, gender (gender_bio), birth date (birthday_bio) along with other identifying fields such as wikipedia_id and ballotpedia_id.

For instance - if you wanted to understand representation across genders over time, leverage the field gender_bio.

Political Trends Analysis:

Each legislator's movements through political roles over time is documented meticulously in these datasets. By filtering on specific IDs (like Thomas ID) you can get a chronological overview of their progression. Use this feature to understand shifting political trends within states or districts.

Through cross-referencing this dataset with...

Ids Logistics Pilipinas Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The "Coffee Shop Data" dataset is a comprehensive collection designed for a wide array of data analysis, providing a deep dive into the operations of a coffee shop. This database was created as my first data analyst project, aimed at extracting meaningful insights from everyday coffee shop operations. The dataset consists of several tables, each focusing on different aspects of the business:

Orders: Records of customer orders, including order IDs, timestamps, item IDs, quantities, customer names, and whether the order was for dine-in or takeout. Items: Details of menu items, including item IDs, SKUs, names, categories, sizes, and prices. Recipes: Information on how each menu item is made, listing required ingredients and quantities. Ingredients: A list of ingredients used in the coffee shop, including their IDs, names, weights, measurements, and prices. Inventory: Current stock levels of each ingredient. Staff: Information on coffee shop staff, including their IDs, names, positions, and salary rates. Shift: Details of work shifts, including shift IDs, days, start times, and end times. Rota: Staff work schedules, linking staff members to specific shifts.

What You Can Achieve with This Database:

Leveraging this dataset, you can perform a variety of analyses to understand and improve coffee shop operations. Here's what I accomplished in my first data analyst project using this data:

• Total Orders: Counted all customer orders to gauge business activity.
• Total Sales: Calculated the total revenue generated.
• Total Items: Summarized the variety and number of items sold.
• Average Order Value: Determined the average revenue per order.
• Sales by Category: Analyzed revenue generation by item category.
• Top Selling Items: Identified the most popular items.
• Orders by Hour: Examined the distribution of orders throughout the day.
• Sales by Hour: Analyzed hourly revenue trends.
• Orders In or Out: Differentiated between dine-in and takeout orders.
• Total Quantity by Ingredient: Calculate the total usage of each ingredient.
• Total Cost of Ingredients: Estimated the overall cost of ingredients used.
• Calculate Cost of Coffee: Determined the cost to produce each coffee item.
• Percentage Stock Remaining by Ingredients: Assessed stock levels as a percentage of total capacity.
• List of Ingredients to Re-order: Identified ingredients needing replenishment based on inventory levels.
• Total Staff Cost: Calculated the total expenditure on staff salaries.
• Total Hours Worked: Summed up the hours staff worked.
• Hours Worked by Staff Member: Broke down hours worked by individual employees.
• Cost per Staff Member: Analyzed salary expenses per employee.

This dataset not only showcases the complexities of managing a coffee shop but also serves as an invaluable resource for anyone interested in data analysis, business optimization, or understanding the finer details of the food service industry.

Please note that the data contained within this dataset is not real and was generated with the assistance of ChatGPT to simulate a realistic coffee shop environment.

Why do I do this?

When working on my thesis, i struggled to work with this dataset because of massive amount of infinite and nan data (also my god, this dataset is huge) that hinders my progress. So I made this dataset and make it public so that people dont suffer like me :)

Content

The content is pretty much the same with the one you can found here. It just cleaned from any nan and infinity data. I also scaled it 20%.

Acknowledgements

I acknowlegdge that this dataset is not mine. Please refer to this for further information.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Afghanistan was 1,000 individuals. Gender-matched sampling was used during the final stage of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata set, or they can be combined to cover the entire nation. The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship. The purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature name is identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

International Distribution Services reported -25M in Net Income for its fiscal semester ending in September of 2024. Data for International Distribution Services | IDS - Net Income including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Introduction

The 802.11 standard includes several management features and corresponding frame types. One of them are probe requests (PR). They are sent by mobile devices in the unassociated state to search the nearby area for existing wireless networks. The frame part of PRs consists of variable length fields called information elements (IE). IE fields represent the capabilities of a mobile device, such as data rates.
The dataset includes PRs collected in a controlled rural environment and in a semi-controlled indoor environment under different measurement scenarios.
It can be used for various use cases, e.g., analysing MAC randomization, determining the number of people in a given location at a given time or in different time periods, analysing trends in population movement (streets, shopping malls, etc.) in different time periods, etc.

Measurement setup

The system for collecting PRs consists of a Raspberry Pi 4 (RPi) with an additional WiFi dongle to capture Wi-Fi signal traffic in monitoring mode. Passive PR monitoring is performed by listening to 802.11 traffic and filtering out PR packets on a single WiFi channel. The following information about each PR received is collected: MAC address, Supported data rates, extended supported rates, HT capabilities, extended capabilities, data under extended tag and vendor specific tag, interworking, VHT capabilities, RSSI, SSID and timestamp when PR was received. The collected data was forwarded to a remote database via a secure VPN connection. A Python script was written using the Pyshark package for data collection, preprocessing and transmission.

Data preprocessing

The gateway collects PRs for each consecutive predefined scan interval (10 seconds). During this time interval, the data are preprocessed before being transmitted to the database. For each detected PR in the scan interval, IEs fields are saved in the following JSON structure: PR_IE_data = { 'DATA_RTS': {'SUPP': DATA_supp , 'EXT': DATA_ext}, 'HT_CAP': DATA_htcap, 'EXT_CAP': {'length': DATA_len, 'data': DATA_extcap}, 'VHT_CAP': DATA_vhtcap, 'INTERWORKING': DATA_inter, 'EXT_TAG': {'ID_1': DATA_1_ext, 'ID_2': DATA_2_ext ...}, 'VENDOR_SPEC': {VENDOR_1:{ 'ID_1': DATA_1_vendor1, 'ID_2': DATA_2_vendor1 ...}, VENDOR_2:{ 'ID_1': DATA_1_vendor2, 'ID_2': DATA_2_vendor2 ...} ...} }

Supported data rates and extended supported rates are represented as arrays of values that encode information about the rates supported by a mobile device. The rest of the IEs data is represented in hexadecimal format. Vendor Specific Tag is structured differently than the other IEs. This field can contain multiple vendor IDs with multiple data IDs with corresponding data. Similarly, the extended tag can contain multiple data IDs with corresponding data.
Missing IE fields in the captured PR are not included in PR_IE_DATA.

When a new MAC address is detected in the current scan time interval, the data from PR is stored in the following structure:

{'MAC': MAC_address, 'SSIDs': [ SSID ], 'PROBE_REQs': [PR_data] },

where PR_data is structured as follows: { 'TIME': [ DATA_time ], 'RSSI': [ DATA_rssi ], 'DATA': PR_IE_data }.

This data structure allows storing only TOA and RSSI for all PRs originating from the same MAC address and containing the same PR_IE_data. All SSIDs from the same MAC address are also stored.
The data of the newly detected PR is compared with the already stored data of the same MAC in the current scan time interval.
If identical PR's IE data from the same MAC address is already stored, then only data for the keys TIME and RSSI are appended. If no identical PR's IE data has yet been received from the same MAC address, then PR_data structure of the new PR for that MAC address is appended to PROBE_REQs key.
The preprocessing procedure is shown in Figure ./Figures/Preprocessing_procedure.png
At the end of each scan time interval, all processed data is sent to the database along with additional metadata about the collected data e.g. wireless gateway serial number and scan start and end timestamps. For an example of a single PR captured, see the ./Single_PR_capture_example.json file.

Environments description

We performed measurements in a controlled rural outdoor environment and in a semi-controlled indoor environment of the Jozef Stefan Institute. See the Excel spreadsheet Measurement_informations.xlsx for a list of mobile devices tested.

Indoor environment

We used 3 RPi's for the acquisition of PRs in the Jozef Stefan Institute. They were placed indoors in the hallways as shown in the ./Figures/RPi_locations_JSI.png. Measurements were performed on weekend to minimize additional uncontrolled traffic from users' mobile devices. While there is some overlap in WiFi coverage between the devices at the location 2 and 3, the device at location 1 has no overlap with the other two devices.

Rural environment outdoors

The three RPi's used to collect PRs were placed at three different locations with non-overlapping WiFi coverage, as shown in ./Figures/RPi_locations_rural_env.png. Before starting the measurement campaign, all measured devices were turned off and the environment was checked for active WiFi devices. We did not detect any unknown active devices sending WiFi packets in the RPi's coverage area, so the deployment can be considered fully controlled. All known WiFi enabled devices that were used to collect and send data to the database used a global MAC address, so they can be easily excluded in the preprocessing phase. MAC addresses of these devices can be found in the ./Measurement_informations.xlsx spreadsheet. Note: The Huawei P20 device with ID 4.3 was not included in the test in this environment.

Scenarios description

We performed three different scenarios of measurements.

Individual device measurements

For each device, we collected PRs for one minute with the screen on, followed by PRs collected for one minute with the screen off. In the indoor environment the WiFi interfaces of the other devices not being tested were disabled. In rural environment other devices were turned off. Start and end timestamps of the recorded data for each device can be found in the ./Measurement_informations.xlsx spreadsheet under the Indoor environment of Jozef Stefan Institute sheet and the Rural environment sheet.

Three groups test

In this measurement scenario, the devices were divided into three groups. The first group contained devices from different manufacturers. The second group contained devices from only one manufacturer (Samsung). Half of the third group consisted of devices from the same manufacturer (Huawei), and the other half of devices from different manufacturers. The distribution of devices among the groups can be found in the ./Measurement_informations.xlsx spreadsheet.

The same data collection procedure was used for all three groups. Data for each group were collected in both environments at three different RPis locations, as shown in ./Figures/RPi_locations_JSI.png and ./Figures/RPi_locations_rural_env.png.
At each location, PRs were collected from each group for 10 minutes with the screen on. Then all three groups switched locations and the process was repeated. Thus, the dataset contains measurements from all three RPi locations of all three groups of devices in both measurement environments. The group movements and the timestamps for the start and end of the collection of PRs at each loacation can be found in spreadsheet ./Measurement_informations.xlsx.

One group test

In the last measurement scenario, all devices were grouped together. In rural evironement we first collected PRs for 10 minutes while the screen was on, and then for another 10 minutes while the screen was off. In indoor environment data were collected at first location with screens on for 10 minutes. Then all devices were moved to the location of the next RPi and PRs were collected for 5 minutes with the screen on and then for another 5 minutes with the screen off.

Folder structure

The root directory contains two files in JSON format for each of the environments where the measurements took place (Data_indoor_environment.json and Data_rural_environment.json). Both files contain collected PRs for the entire day that the measurements were taken (12:00 AM to 12:00 PM) to get a sense of the behaviour of the unknown devices in each environment. The spreadsheet ./Measurement_informations.xlsx. contains three sheets. Devices description contains general information about the tested devices, RPis, and the assigned group for each device. The sheets Indoor environment of Jozef Stefan Institute and Rural environment contain the corresponding timestamps for the start and end of each measurement scenario. For the scenario where the devices were divided into groups, additional information about the movements between locations is included. The location names are based on the RPi gateway ID and may differ from those on the figures showing the locations of the RPIs for each environment. The ./Figures folder contains the figures already mentioned above.

Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions or anomalies on CANs. Producing vehicular CAN data with a variety of intrusions is a difficult task for most researchers as it requires expensive assets and deep expertise. To illuminate this task, we introduce the first comprehensive guide to the existing open CAN intrusion detection system (IDS) datasets. We categorize attacks on CANs including fabrication (adding frames, e.g., flooding or targeting and ID), suspension (removing an ID’s frames), and masquerade attacks (spoofed frames sent in lieu of suspended ones). We provide a quality analysis of each dataset; an enumeration of each datasets’ attacks, benefits, and drawbacks; categorization as real vs. simulated CAN data and real vs. simulated attacks; whether the data is raw CAN data or signal-translated; number of vehicles/CANs; quantity in terms of time; and finally a suggested use case of each dataset. State-of-the-art public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, lacking fidelity. In general, the physical effects of attacks on the vehicle are not verified in the available datasets. Only one dataset provides signal-translated data but is missing a corresponding “raw” binary version. This issue pigeon-holes CAN IDS research into testing on limited and often inappropriate data (usually with attacks that are too easily detectable to truly test the method). The scarcity of appropriate data has stymied comparability and reproducibility of results for researchers. As our primary contribution, we present the Real ORNL Automotive Dynamometer (ROAD) CAN IDS dataset, consisting of over 3.5 hours of one vehicle’s CAN data. ROAD contains ambient data recorded during a diverse set of activities, and attacks of increasing stealth with multiple variants and instances of real (i.e. non-simulated) fuzzing, fabrication, unique advanced attacks, and simulated masquerade attacks. To facilitate a benchmark for CAN IDS methods that require signal-translated inputs, we also provide the signal time series format for many of the CAN captures. Our contributions aim to facilitate appropriate benchmarking and needed comparability in the CAN IDS research field.

Minh Quang Ids Trading And Industries Joint Stock Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

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Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Northwest Territories, Yukon, and Nunavut (representing approximately 0.3 percent of the Canadian population) were excluded.

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Canada is 1007.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

1. Summary:

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission, planned to launch in 2022, will vastly expand observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission will provide a range of relevant data products. One product the SWOT mission will provide are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The SWOT vector data products will be most broadly useful if they allow multitemporal analysis of river nodes and reaches covering the same river areas. Doing so requires defining SWOT reaches and nodes a priori, so that SWOT data can be assigned to them. The SWOt River Database (SWORD) combines multiple global river- and satellite-related datasets to define the nodes and reaches that will constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.

This dataset is public for a manuscript under review in Water Resources Research (WRR).

2. Data Formats:

The SWORD database is provided in netCDF and shapefile formats. All files start with a two-digit continent identifier (“af” – Africa, “as” – Asia / Siberia, “eu” – Europe / Middle East, “na” – North America, “oc” – Oceania, “sa” – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats.

NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v1.nc (i.e. na_sword_v1.nc).

SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v1.shp (i.e. na_sword_nodes_hb74_v1.shp; na_sword_reaches_hb74_v1.shp).

3. Attribute Description:

This list contains the primary attributes contained in the SWORD netCDFs and shapefiles.

• x: Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees).
• y: Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees).
• node_id: ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node).
• node_length (node files only): Node length measured along the GRWL centerline points (units: meters).
• reach_id: ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach).
• reach_length (reach files only): Reach length measured along the GRWL centerline points (units: meters).
• wse: Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters).
• wse_var: WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters).
• width: Average width for a node or reach (units: meters).
• width_var: Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters).
• facc: Maximum flow accumulation value for a node or reach. Flow accumulation values are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) (units: square kilometers).
• n_chan_max: Maximum number of channels for each node or reach.
• n_chan_mod: Mode of the number of channels for each node or reach.
• obstr_type: Type of obstruction for each node or reach based on the Globale Obstruction Database (GROD, Whittemore et al., 2020) and HydroFALLS data (http://wp.geog.mcgill.ca/hydrolab/hydrofalls). Obstr_type values: 0 - No Dam, 1 - Dam, 2 - Channel Dam, 3 - Lock, 4 - Low Permeable Dam, 5 - Waterfall.
• grod_id: The unique GROD ID for each node or reach with obstr_type values 1-4.
• hfalls_id: The unique HydroFALLS ID for each node or reach with obstr_type value 5.
• dist_out: Distance from the river outlet for each node or reach (units: meters).
• type: Type identifier for a node or reach: 1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach/node.
• lakeflag (reach files only): GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – tidally influenced river, 3 – canal.
• slope (reach files only): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer).
• n_nodes (reach files only): Number of nodes associated with each reach.
• n_rch_up (reach files only): Number of upstream reaches for each reach.
• n_rch_down (reach files only): Number of downstream reaches for each reach.
• rch_id_up (reach files only): Reach IDs of the upstream neighboring reaches.
• rch_id_dn (reach files only): Reach IDs of the downstream neighboring reaches.
• swot_obs (reach files only): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle.
• swot_orbits (reach files only): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle.

4. References:

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585-588.

JPL Internal Document (2020b). Surface Water and Ocean Topography Mission Level 2 KaRIn high rate river single pass vector product, JPL D-56413, Rev. A, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56413_SWOT_Product_Description_L2_HR_RiverSP_20200825a.pdf

Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.

Whittemore, A., Ross, M. R., Dolan, W., Langhorst, T., Yang, X., Pawar, S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., & Pavelsky, T. (2020). A Participatory Science Approach to Expanding Instream Infrastructure Inventories. Earth's Future, 8(11), e2020EF001558.

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., & Pavelsky, T. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research. https://doi.org/10.1029/2019WR024873.

HydroFALLS: http://wp.geog.mcgill.ca/hydrolab/hydrofalls/

Indonesia ID: External Debt: INT: Interest Payments: General Government Sector data was reported at 4.307 USD bn in 2031. This records a decrease from the previous number of 5.720 USD bn for 2030. Indonesia ID: External Debt: INT: Interest Payments: General Government Sector data is updated yearly, averaging 2.664 USD bn from Dec 1970 (Median) to 2031, with 62 observations. The data reached an all-time high of 9.026 USD bn in 2024 and a record low of 21.732 USD mn in 1970. Indonesia ID: External Debt: INT: Interest Payments: General Government Sector data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Indonesia – Table ID.World Bank.IDS: External Debt: Disbursements and Interest Payment: Annual. General government long-term debt are aggregated. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Interest payments are actual amounts of interest paid by the borrower in currency, goods, or services in the year specified. Long-term external debt is defined as debt that has an original or extended maturity of more than one year and that is owed to nonresidents by residents of an economy and repayable in currency, goods, or services. Data are in current U.S. dollars.

About this dataset

Context

The dataset tabulates the Challis population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Challis. The dataset can be utilized to understand the population distribution of Challis by age. For example, using this dataset, we can identify the largest age group in Challis.

Key observations

The largest age group in Challis, ID was for the group of age 45-49 years with a population of 109 (12.23%), according to the 2021 American Community Survey. At the same time, the smallest age group in Challis, ID was the 75-79 years with a population of 5 (0.56%). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Challis is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Challis total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Challis Population by Age. You can refer the same here

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