On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

The dataset is provided in the form of an excel files with 5 tabs. The first three excel tabs constitute demonstration data on the set up of consumer wearable device for exposure and health monitoring in population studies while the two last excel tabs include the full dataset with actual data collected using the consumer wearable devices in Cyprus and Greece respectively during the Spring of 2020. The data from the last two tabs were used to assess the compliance of asthmatic schoolchildren (n=108) from both countries to public health intervention levels in response to COVID-19 pandemic (lockdown and social distancing measures), using wearable sensors to continuously track personal location and physical activity. Asthmatic children were recruited from primary schools in Cyprus and Greece (Heraklion district, Crete) and were enrolled in the LIFE-MEDEA public health intervention project (Clinical.Trials.gov Identifier: NCT03503812). The LIFE-MEDEA project aimed to evaluate the efficacy of behavioral recommendations to reduce exposure to particulate matter during desert dust storm (DDS) events and thus mitigate disease-specific adverse health effects in vulnerable groups of patients. However, during the COVID-19 pandemic, the collected data were analysed using a mixed effect model adjusted for confounders to estimate the changes in 'fraction time spent at home' and 'total steps/day' during the enforcement of gradually more stringent lockdown measures. Results of this analysis were first presented in the manuscript titled “Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic” published by Scientific Reports (https://doi.org/10.1038/s41598-021-85358-4). The dataset from LIFE-MEDEA participants (asthmatic children) from Cyprus and Greece, include variables: Study ID, gender, age, study year, ambient temperature, ambient humidity, recording day, percentage of time staying at home, steps per day, callendar day, calendar week, date, lockdown status (phase 1, 2, or 3) due to COVID-19 pandemic, and if the date was during the weekend (binary variable). All data were collected following approvals from relevant authorities at both Cyprus and Greece, according to national legislation. In Cyprus, approvals have been obtained from the Cyprus National Bioethics Committee (EEBK EΠ 2017.01.141), by the Data Protection Commissioner (No. 3.28.223) and Ministry of Education (No 7.15.01.23.5). In Greece, approvals have been obtained from the Scientific Committee (25/04/2018, No: 1748) and the Governing Board of the University General Hospital of Heraklion (25/22/08/2018).

Overall, wearable sensors, often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessment in clinical studies. Nevertheless, the real-life application of these technologies in studies involving many participants for a significant observation period may be hindered by several practical challenges. Using a small subset of the LIFE-MEDEA dataset, in the first excel tab of dataset, we provide demonstration data from a small subset of asthmatic children (n=17) that participated in the LIFE MEDEA study that were equipped with a smartwatch for the assessment of physical activity (heart rate, pedometer, accelerometer) and location (exposure to indoor or outdoor microenvironment using GPS signal). Participants were required to wear the smartwatch, equipped with a data collection application, daily, and data were transmitted via a wireless network to a centrally administered data collection platform. The main technical challenges identified ranged from restricting access to standard smartwatch features such as gaming, internet browser, camera, and audio recording applications, to technical challenges such as loss of GPS signal, especially in indoor environments, and internal smartwatch settings interfering with the data collection application. The dataset includes information on the percentage of time with collected data before and after the implementation of a protocol that relied on setting up the smartwatch device using publicly available Application Lockers and Device Automation applications to address most of these challenges. In addition, the dataset includes example single-day observations that demonstrate how the inclusion of a Wi-Fi received signal strength indicator, significantly improved indoor localization and largely minimised GPS signal misclassification (excel tab 2). Finally excel tab 3, shows the tasks Overall, the implementation of these protocols during the roll-out of the LIFE MEDEA study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality. The protocol and the representative results have been submitted for publication to the Journal of Visualised experiments (submission: JoVE63275). The Variables included in the first three excel tabs were the following: Participant ID (Unique serial number for patient participating in the study), % Time Before (Percentage of time with data before protocol implementation), % Time After (Percentage of time with data after protocol implementation), Timestamp (Date and time of event occurrence), Indoor/Outdoor (Categorical- Classification of GPS signals to Indoor and Outdoor and null(missing value) based on distance from participant home), Filling algorithm (Imputation algorithm), SSID (Wireless network name connected to the smartwatch), Wi-Fi Signal Strength (Connection strength via Wi-Fi between smartwatch and home’s wireless network. (0 maximum strength), IMEI (International mobile equipment identity. Device serial number), GPS_LAT (Latitude), GPS_LONG (Longitude), Accuracy of GPS coordinates (Accuracy in meters of GPS coordinates), Timestamp of GPS coordinates (Obtained GPS coordinates Date and time), Battery Percentage (Battery life), Charger (Connected to the charger status).

Important notes on data collection methodology: Global positioning system (GPS) and physical activity data were recorded using LEMFO-LM25 smartwatch device which was equipped with the embrace™ data collection application. The smartwatch worked as a stand-alone device that was able to transmit data across 5-minute intervals to a cloud-based database via Wi-Fi data transfer. The software was able to synchronize the data collected from the different sensors, so the data are transferred to the cloud with the same timestamp. Data synchronization with the cloud-based database is performed automatically when the smartwatch contacts the Wi-Fi network inside the participants’ homes. According to the study aims, GPS coordinates were used to estimate the fraction of time spent in or out of the participants' residences. The time spent outside was defined as the duration of time with a GPS signal outside a 100-meter radius around the participant’s residence, to account for the signal accuracy in commercially available GPS receivers. Additionally, to address the limitation that signal accuracy in urban and especially indoor environments is diminished, 5-minute intervals with missing GPS signals were classified as either “indoor classification” or “outdoor classification” based on the most recent available GPS recording. The implementation of this GPS data filling algorithm allowed replacing the missing 5-minute intervals with estimated values. Via the described protocol, and through the use of a Device Automation application, information on WiFi connectivity, WiFi signal strength, battery capacity, and whether the device was charging or not was also made available. Data on these additional variables were not automatically synchronised with the cloud-based database but had to be manually downloaded from each smartwatch via Bluetooth after the end of the study period.

This CD-ROM product is an authoritative reference source of 15 key financial ratios by industry groupings compiled from the North American Industry Classification System (NAICS 2007). It is based on up-to-date, reliable and comprehensive data on Canadian businesses, derived from Statistics Canada databases of financial statements for three reference years. The CD-ROM enables users to compare their enterprise's performance to that of their industry and to address issues such as profitability, efficiency and business risk. Financial Performance Indicators can also be used for inter-industry comparisons. Volume 1 covers large enterprises in both the financial and non-financial sectors, at the national level, with annual operating revenue of $25 million or more. Volume 2 covers medium-sized enterprises in the non-financial sector, at the national level, with annual operating revenue of $5 million to less than $25 million. Volume 3 covers small enterprises in the non-financial sector, at the national, provincial, territorial, Atlantic region and Prairie region levels, with annual operating revenue of $30,000 to less than $5 million. Note: FPICB has been discontinued as of 2/23/2015. Statistics Canada continues to provide information on Canadian businesses through alternative data sources. Information on specific financial ratios will continue to be available through the annual Financial and Taxation Statistics for Enterprises program: CANSIM table 180-0003 ; the Quarterly Survey of Financial Statements: CANSIM tables 187-0001 and 187-0002 ; and the Small Business Profiles, which present financial data for small businesses in Canada, available on Industry Canada's website: Financial Performance Data.

The data set contains the information presented in Figure 6 of the paper, which shows the spin up and spin down times recored for saline solutions in the Eye-on-a-Chip device, for rotational velocities of 5 to 17 radians per second. Also shown in the figure are the viscous time scale and the Ekman timescale for the experimental conditions.

The experimental values were obtained by analysis of videos. The saline solutions contained small, almost-neutrally buoyant particles which allowed the local velocity to be determined. Examples of the videos are provided as Supplementary Information for the journal article.

The journal article will be published Open Access, and further information about data collection and data processing is given there.

The data are in a Microsoft Excel worksheet (.xlsx).

This dataset contains information on Government of Canada tender information published according to the Financial Administration Act. It includes data for all Schedule I, Schedule II and Schedule III departments, agencies, Crown corporations, and other entities (unless specifically exempt) who must comply with the Government of Canada trade agreement obligations. CanadaBuys is the authoritative source of this information. Visit the How procurement works page on the CanadaBuys website to learn more. All data files in this collection share a common column structure, and the procurement category field (labelled as “procurementCategory-categorieApprovisionnement”) can be used to filter by the following four major categories of tenders: Tenders for construction, which will have a value of “CNST” Tenders for goods, which will have a value of “GD” Tenders for services, which will have a value of “SRV” Tenders for services related to goods, which will have a value of “SRVTGD” A tender may be associated with one or more of the above procurement categories. Note: Some records contain long tender description values that may cause issues when viewed in certain spreadsheet programs, such as Microsoft Excel. When the information doesn’t fit within the cell’s character limit, the program will insert extra rows that don’t conform to the expected column formatting. (Though, all other records will still be displayed properly, in their own rows.) To quickly remove the “spill-over data” caused by this display error in Excel, select the publication date field (labelled as “publicationDate-datePublication”), then click the Filter button on the Data menu ribbon. You can then use the filter pull-down list to remove any blank or non-date values from this field, which will hide the rows that only contain “spill-over” description information. The following list describes the resources associated with this CanadaBuys tender notices dataset. Additional information on Government of Canada tenders can also be found on the Tender notices tab of the CanadaBuys tender opportunities page. NOTE: While the CanadaBuys online portal includes tender opportunities from across multiple levels of government, the data files in this related dataset only include notices from federal government organizations. (1) CanadaBuys data dictionary: This XML file offers descriptions of each data field in the tender notices files linked below, as well as other procurement-related datasets CanadaBuys produces. Use this as a guide for understanding the data elements in these files. This dictionary is updated as needed to reflect changes to the data elements. (2) New tender notices: This file contains up to date information on all new tender notices that are published to CanadaBuys throughout a given day. The file is updated every two hours, from 6:15 am until 10:15 pm (UTC-0500) to include new tenders as they are published. All tenders in this file will have a publication date matching the current day (displayed in the field labelled “publicationDate-datePublication”), or the day prior for systems that feed into this file on a nightly basis. (3) Open tender notices: This file contains up to date information on all tender notices that are open for bidding on CanadaBuys, including any amendments made to these tender notices during their lifecycles. The file is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include newly published open tenders. All tenders in this file will have a status of open (displayed in the field labelled “tenderStatus-tenderStatut-eng”). (4) All CanadaBuys tender notices, 2022-08-08 onwards: This file contains up to date information on all tender notices published through CanadaBuys. This includes any tender notices that were open for bids on or after August 8, 2022, when CanadaBuys launched as the system of record for all Tender Notices for the Government of Canada. This file includes any amendments made to these tender notices during their lifecycles. It is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include any updates or amendments, as needed. Tender notices in this file can have any publication date on or after August 8, 2022 (displayed in the field labelled “publicationDate-datePublication”), and can have a status of open, cancelled or expired (displayed in the field labelled “tenderStatus-tenderStatut-eng”). (5) Legacy tender notices, 2009 to 2022-08 (prior to CanadaBuys): This file contains details of the tender notices that were launched prior to the implementation of CanadaBuys, which became the system of record for all tender notices for the Government of Canada on August 8, 2022. This datafile is refreshed monthly. The over 70,000 tenders in this file have publication dates from August 5, 2022 and before (displayed in the field labelled “publicationDate-datePublication”) and have a status of cancelled or expired (displayed in the field labelled “tenderStatus-tenderStatut-eng”). Note: Procurement data was structured differently in the legacy applications previously used to administer Government of Canada tender notices. Efforts have been made to manipulate these historical records into the structure used by the CanadaBuys data files, to make them easier to analyse and compare with new records. This process is not perfect since simple one-to-one mappings can’t be made in many cases. You can access these historical records in their original format as part of the archived copy of the original tender notices dataset. You can also refer to the supporting documentation for understanding the new CanadaBuys tender and award notices datasets. (6) Tender notices, YYYY-YYYY: These files contain information on all tender notices published in the specified fiscal year that are no longer open to bidding. The current fiscal year's file is refreshed each morning, between 7:00 am and 8:30 am (UTC-0500) to include any updates or amendments, as needed. The files associated with past fiscal years are refreshed monthly. Tender notices in these files can have any publication date between April 1 of a given year and March 31 of the subsequent year (displayed in the field labelled “publicationDate-datePublication”) and can have a status of cancelled or expired (displayed in the field labelled “tenderStatus-tenderStatut-eng”). New records are added to these files once related tenders reach their close date, or are cancelled. Note: New tender notice data files will be added on April 1 for each fiscal year.