This dataset includes data from the Maryland Energy Administration (MEA) about statewide energy consumption, energy savings programs, renewable energy, and electric and hybrid vehicles

This is a dashboard that is created with the available data gathered from multiple sources through desk research for pilot regions in my PhD research. This dashboard is created as a spreadsheet where the list of information about baseline data was stored. The spreadsheet enabled the categorisation of available data based on the source and type of data, data collection method, frequency, etc, which is helpful to identify the boundaries in the model. Conducting desk research on the available datasets, statistics and reports could be useful to evaluate the quality of available information and identify any data gap that may need to be filled. This helped with the initial planning for future data collection to measure the key model outcomes such as visitor number, expenditure, and revenue as a result of the innovation scenarios testing that was conducted for the purpose of model demonstration.

Brand performance data collected from AI search platforms for the query "e-commerce KPI dashboard template".

Analysis of ‘Dashboard Template - Maryland Energy Administration’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/702a5047-8470-4a53-a1e7-d629756d95c1 on 26 January 2022.

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

This dataset includes data from the Maryland Energy Administration (MEA) about statewide energy consumption, energy savings programs, renewable energy, and electric and hybrid vehicles

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

This template ArcGIS Usage Data Dashboard is provided to accompany this blog post and Jupyter Notebook.The template dashboard is configured to display four usage measures:Current Registered Users (indicator)Daily Registered Users (serial chart)Weekly Unique Logins (serial chart)Monthly Unique Logins (serial chart)

Department of Budget and Management data (personnel, debt, fleet, procurement)

This excel workbook contains descriptive information collected from the retired "midashboard.michigan.gov." NOTE: More updated data than what is collected here can be found by searching ARCHIVED JANUARY 2018 and selecting the desired data set from that list. These downloadable spreadsheets primarily contain contextual information about the previously tracked measures, such as why they were important to track and any goals or targets for the data.

This template ArcGIS Usage Data Dashboard is provided to accompany this blog post and Jupyter Notebook.The template dashboard is configured to display four usage measures:Current Registered Users (indicator)Daily Registered Users (serial chart)Weekly Unique Logins (serial chart)Monthly Unique Logins (serial chart)

Spreadsheet for 911 statistics _DEMO DATA

The Programmable Dashboard market has emerged as a critical component in the modern data-driven landscape, providing businesses with the ability to visualize complex data in an accessible format. These dynamic dashboards enable organizations across various industries, including finance, healthcare, retail, and techn

Hong Kong has a lot of real-time data which are made available by the Government of Hong Kong Special Administrative Region at https://DATA.GOV.HK/ (“DATA.GOV.HK”). These data were processed and converted to Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform.These series of Operations Dashboard integrate different available real-time datasets in Hong Kong to provide a dashboard interface for monitoring real-time data feed on your desktop or tablet device. The objectives are to facilitate our Hong Kong ArcGIS Online users to view these data in a spatial ready format and save their data conversion effort.These series of Operations Dashboard come in three themes, environmental, traffic and integrated.The Environmental theme contains real-time temperature, air quality health risk and air pollution concentration of different districts in Hong Kong. To view it, please click here.Traffic theme contains real-time information of estimated journey time, car park vacancy, traffic speed of major roads, traffic snapshot images and speed map panels in Hong Kong.To view it, please click here. The integrated theme combines the above two sets of data, which are environmental and traffic, and makes them into one single dashboard view.

The EFSA dashboard on Salmonella is a graphical user interface for searching and querying the large amount of data collected each year by EFSA from EU Member States and other reporting countries based on Zoonoses Directive 2003/99/EC. The Salmonella dashboard shows summary statistics for the monitoring results of the pathogen with regard to major food categories and animal species, Salmonella-positive official samples in the context of food safety criteria and process hygiene criteria, the occurrence of Salmonella in major food categories and the achievement of Salmonella reduction targets in poultry populations. The Salmonella data and related statistics can be displayed interactively using charts, graphs and maps in the online EFSA dashboard. The main statistics can also be viewed and downloaded in tabular format. Detailed information on the use and features of the Salmonella dashboard can be found in the present user guide that can also be downloaded from the online tool.

Note: April 22, 2025: Updates to "CHN by income and HH size_v3". --------------------------------------------------------------------------------------------------------------------------------- Note: April 16, 2025: Updates to the following files have been made on April 9th and 16th: "CHN by income and HH size_v2", "cd_hh_projections_v2", "csd_hh_projections_v2", and "CMAs_all data_v3". --------------------------------------------------------------------------------------------------------------------------------- Note: March 31, 2025 files "Data_Element_1a" & "...1b" updated to v3 to include additional geographies (CDs and PTs) in the calculation of households close to rail transit. --------------------------------------------------------------------------------------------------------------------------------- Note: This dataset as of March 31st, 2025 now contains data on all 12 data elements, including core housing need among "gender diverse" households (formerly called "2SLGBTQ+" households) in table "Data_Element_ 3". That table (i.e. Data_Element_3) now also includes core housing need data on those priority populations reported in HART's HNA Tool. Two other outputs were migrated from that HNA Tool into this Federal HNA Template dataset: Income Categories and Affordable Shelter Costs, Percentage of Households in Core Housing Need by Income Category and Household Size, and 2021 Affordable Housing Deficit. (HICC Section 3.6), and Projected Households by Household Size and Income Category (HICC Section 6.1.1) This Borealis dataset has been updated accordingly to include that data: "AMHI.csv" (2021 AMHI and dollar ranges of income and shelter cost categories) "cd_hh_projections.csv" (Projected households in 2031 for CDs) "csd_hh_projections.csv" (Projected households in 2031 for CSDs) "CHN by income and HH size.csv" (2021 core housing need by income and household size) The geographical scope of the dataset has also been expanded. Before March 31st, only CSDs were included. As of March 31st, data on CDs, provinces/territories, the country of Canada, and CMA/CAs has been added. Not all data is available for all geographies: Data from CMHC's Rental Market Survey and Starts and Completions Survey are reported at the CSD level within CMAs/CAs. Results for provinces/territories/Canada are reported, but data for CDs is not. Since these surveys may not include all CSDs within a given CD, we have not attempted to aggregate this CSD data into CDs. Data from any custom census order by HART does not include CMA/CAs. We are able to aggregate the data by CSD into CMA/CAs, but all income and shelter cost data had been categorized based on the AMHI of the CSD as part of the original order (i.e. whether a household is "Very Low" income or "Low" income depends on the median household income of the CSD that the household lives in). This will lead to some inaccuracy and ambiguity of interpretation for the income or shelter cost data reported for CMAs. Data on "gender diverse" households is only available from Statistics Canada for geographies with a population count greater than 50,000 as of the 2021 census. This represents a total of 239 geographies (incl. Canada and the provinces/territories). Due to the low number of CSDs with this data, we have not attempted to aggregated this to the CMA/CA level. Data for CMAs/CAs will be added to the tool by mid-April 2025, but the source data has been summarized and included in this dataset: "CMAs_all data.csv" (All available data for CMAs and CAs) --------------------------------------------------------------------------------------------------------------------------------- Update (March 14, 2025): Tables "Data_Element_1a" and "...1b" have been updated to exclude some non-rail rapid transit stops that were erroneous included, notably in Winnipeg. --------------------------------------------------------------------------------------------------------------------------------- For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This database was created to accompany the dashboard on HART's website called the "Federal Housing Needs Assessment Template." URL: https://hart.ubc.ca/federal-hna-template/. This dashboard presents housing-related data to help communities complete the Housing Needs Assessment template requested by the Government of Canada as a requirement for certain funding applications. For more information on that template, please visit the Government of Canada's website (https://housing-infrastructure.canada.ca/housing-logement/hna-ebml/template-modele-eng.html). This dataset represents the underlying data used to populate HART's dashboard. The data contains some public and custom data from Canada's Census of Population (author: Statistics Canada), public data from the Canada Mortgage and Housing Corporation (CMHC) regarding it's Rental Market Survey as well as it's Starts and Completions Survey, private...

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

This dataset contains the response data from the 'National Open Access Monitor: Dashboard Manager Application Form' open from 22nd November 2023. There is no current deadline for responses to the form. The live form is available here: https://app.onlinesurveys.jisc.ac.uk/s/maynoothuniversity/national-open-access-monitor-dashboard-manager-application-form. A PDF reference copy of the form is available here: https://zenodo.org/records/10187037

The purpose of the form is the National Open Access Monitor Ireland RPO and RFO dashboard manager application process. The form allows for one person to apply as primary Dashboard Manager for each RPO/RFO organisation/entity. The supplied data is collated and validated by the National Open Access Monitor Project Manager (Dr Catherine Ferris) and supplied to OpenAIRE. Following application and validation, OpenAIRE contact applicants directly regarding next steps.

As per the requirements of the project, this application process is open and transparent. The list of primary Dashboard Managers will be archived publicly here on Zenodo. For respondents that choose pseudonymisation in their consent forms, identities will not be archived on Zenodo, but all of the information supplied in this form will be shared with OpenAIRE and with others in their organisation/entity on request.

To note:

Responses have been pseudonymised to the level of stakeholder-group e.g. Contributor a, Research Performing Organisation a, where requested by the participant in the participant consent form: https://doi.org/10.5281/zenodo.7589770

This is the original raw data file, in csv format, as downloaded from the Online Surveys platform and subsequently pseudonymised.

This is a dynamic dataset and will be updated regularly with new submissions. This is version v2, uploaded on 29 January 2024. Please refer to the version history for older versions.

The context is detailed in the Irish Research Performing Organisations (RPO) training - National Open Access Monitor, Ireland: https://zenodo.org/doi/10.5281/zenodo.10143612 and the National Open Access Monitor, Ireland Inception Report: https://zenodo.org/doi/10.5281/zenodo.10005955

This project is managed by IReL and has received funding from Ireland's National Open Research Forum under the NORF Open Research Fund. https://norf.ie/funding/ https://norf.ie/orf-projects-announcement/

The dashboard set comprises individual web pages for each sensor/location. Each dashboard includes a map showing the location of the station, basic statistics and time series plots. If enough data is available (more than 10 years), a climatology of the temperature record is calculated. The SSTAARS climatology is also plotted along the sensor data. Hourly time series plots are also available at each instrument’s depth.

See "Interactive map of this dataset" resource link below for a navigation map to the dashboard web pages.

This comprehensive quality-controlled data set is to assist the delivery of the data to better characterise thermal stress events on the GBR to users.

The primary data set is temperature from over 100 permanent temperature logger locations within the reef from the AIMS temperature logger program and other platforms, which include both mobile gliders and drifting buoys to permanent weather stations and moorings. Summary plots of the data can be interrogated and daily climatologies are provided so users can quickly determine the thermal history at each site. Other relevant data sets are provided from multiple observing platforms with a summary plot. Some data sets have well developed websites and so a link to those sites and data sources are also provided for these.

Methods:

For each sensor, in reef temperature loggers, mooring instruments, AIMS weather stations and QLD wave buoys, the data is extracted for 2015, 2016 and 2017. The records are aggregated into hourly intervals and the climatology extracted from the full record (when more than 10 years of data exist), as the mean of daily average for each day of the year. Basic statistics for 2016 and 2017 are calculated and the heat accumulation indicators (NOAA’s degree-heating week, and the maximum monthly mean) extracted for the site and the year. In a second tab, the time series of each location is plotted. Many of the elements of the dashboard are dynamic, so the user can zoom in/out or print sections of the plots. The dashboard is generated using a dedicated R code.

The main code used to generate the dashboards is available on GitHub: https://github.com/eatlas/GBR_NESP-TWQ-4.2_AIMS_Water-temperature-dashboards

Limitations of the data:

Many of the sensors contain data outside the project time frame (2015-2017). However, this data was only used to calculate the climatology, when more than 10 years of data exist. Data outside the project time frame are not plotted in the dashboards. Data is available at AIMS DATA centre.

Format:

The dashboards are individual HTML files. The original data can be downloaded from AIMS data centre (temperature loggers, moorings and weather stations, https://www.aims.gov.au/docs/data/data.html ) or from Queensland Environment (wave buoys, https://www.qld.gov.au/environment/coasts-waterways/beach/monitoring/waves-sites )

References:

Drivers of Bleaching on the Great Barrier Reef - Compilation of temperature data from 2015, 2016 and 2017, https://eatlas.org.au/gbr/nesp-twq-4-2-temperature-data-2015-17

Files Location:

The code for this project is available on GitHub: https://github.com/eatlas/GBR_NESP-TWQ-4.2_AIMS_Water-temperature-dashboards

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2018-2021-NESP-TWQ-4\4.2_Oceanographic-drivers-of-bleaching\data\2020-08-05_GBR_AIMS_NESP-TWQ-4-2_Temp-dashboard_2015-17

Clinical Trials Dashboard Market Outlook

The global market size for Clinical Trials Dashboard in 2023 was estimated to be approximately USD 1.2 billion, and it is projected to reach around USD 3.6 billion by 2032, growing at a robust CAGR of 12.8%. This remarkable growth is primarily driven by the increasing complexity of clinical trials, the surge in pharmaceutical and biotechnology R&D, and the rising need for real-time data analytics to enhance clinical trial efficiency and decision-making processes.

One of the key growth factors for the Clinical Trials Dashboard market is the burgeoning demand for more efficient clinical trial processes. The pharmaceutical and biotechnology sectors are constantly striving to reduce the time and cost associated with bringing new therapies to market. Clinical trials dashboards enable streamlined monitoring and management of trials by providing real-time data, thus significantly reducing delays and facilitating faster decision-making. Additionally, the ability to aggregate large volumes of data from various sources and present it in an easily interpretable format is crucial in helping researchers and clinicians make informed decisions promptly.

Another major driver is the rapid advancement in technology, particularly in the fields of big data analytics and artificial intelligence (AI). These technologies have revolutionized the way data is processed and analyzed, allowing for more accurate and predictive analytics. AI-powered dashboards can identify patterns and trends that may not be immediately apparent to human analysts, enhancing the ability to predict trial outcomes and optimize trial design. Furthermore, the integration of machine learning algorithms can automate routine tasks, thereby increasing efficiency and reducing the likelihood of human error.

The increasing regulatory requirements and the need for compliance with stringent clinical trial standards are also contributing to the market growth. Regulatory bodies such as the FDA and EMA mandate rigorous oversight and documentation of clinical trials to ensure patient safety and data integrity. Clinical trials dashboards facilitate compliance by providing comprehensive audit trails, real-time monitoring, and automated reporting capabilities. This not only helps organizations meet regulatory requirements but also improves transparency and accountability throughout the trial process.

The emergence of a comprehensive Clinical Trial Platform has become increasingly vital in the landscape of modern clinical research. Such platforms offer a unified solution that integrates various functionalities essential for managing clinical trials, including patient recruitment, data collection, and regulatory compliance. By centralizing these processes, a Clinical Trial Platform not only enhances operational efficiency but also facilitates seamless collaboration among stakeholders. This integration is particularly beneficial in multicenter trials where coordination across different sites is crucial. Furthermore, the platform's ability to provide real-time insights and analytics empowers researchers to make data-driven decisions, thereby improving trial outcomes and accelerating the development of new therapies.

From a regional perspective, North America holds the largest share of the Clinical Trials Dashboard market, followed by Europe and the Asia Pacific. The presence of a large number of pharmaceutical and biotechnology companies, coupled with advanced healthcare infrastructure and significant R&D investments, are key factors driving the market in these regions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the increasing number of clinical trials being conducted in emerging economies such as China and India, and the growing adoption of advanced technologies in healthcare.

Component Analysis

The Clinical Trials Dashboard market can be segmented by component into software and services. Software forms the core of the dashboard, providing the necessary tools and features for data collection, analysis, and visualization. The increasing demand for advanced analytics and the need for real-time data access are driving the adoption of sophisticated software solutions. These software tools are designed to handle large datasets, integrate data from multiple sources, and provide interactive and intuitive dashboards for users. The software segment is expected to dominate the market, acco

This Rio Grande and Pecos River Water Operations Dashboard was created using the Microsoft Power BI application and is currently available to the public. This dashboard was created to provide real time data of the Rio Grande and Pecos rivers and reservoirs for water operation managers to assist in monitoring and making decisions. Data includes 15-minute water flow data and reservoir elevation and storage data from the U.S. Geological Survey, Colorado Department of Water Resources, and U.S. Bureau of Reclamation. The water operations dashboard is in an easy to navigate format that allows the user to clearly view current river and reservoir data at a single website to help make operations, management, and planning decisions.

COVID-19 cases, hospitalizations and deaths in Ohio by selected demographics and county of residence, as reported to the Ohio Department of Health (ODH). The data can be viewed on a dashboard and are also available in CSV format containing the daily new number of cases and date of onset, new death and date of death, and new hospitalization and date of admission by county, sex and age-range.

This publication provides the most timely statistics available relating to NHS funded secondary mental health, learning disabilities and autism services in England. This information will be of use to people needing access to information quickly for operational decision making and other purposes. These statistics are derived from submissions made using version 2.0 of the Mental Health Services Dataset (MHSDS). NHS Digital review the quality and completeness of the submissions used to create these statistics on an ongoing basis. More information about this work can be found in the Accuracy and reliability section of this report. Fully detailed information on the quality and completeness of particular statistics in this release is not available due to the timescales involved in reviewing submissions and engaging with data providers. The information that has been obtained at the time of publication is made available in the Provider Feedback sections of the Data Quality Reports which accompany this release. Information gathered after publication is released in future editions of this publication series. More detailed information on the quality and completeness of these statistics and a summary of how these statistics may be interpreted is made available later in our Mental Health Bulletin: Annual Report publication series. All elements of this publication, other editions of this publication series, and related annual publication series' can be found in the Related Links below. This edition includes statistics produced for the Five Year Forward View (FYFV) for Mental Health Dashboard. These can be found in the Children and Young People's Mental Health Indicators Reference Tables and Adult Mental Health Services Selected NHS England Measures Reference Tables. This information has been previously made available on the NHS Digital Supplementary Information web page. The format of reporting of these statistics will be updated in future releases to improve clarity and accessibility. More information about the FYFV Dashboard can be found in the Related Links below. Correction: The statistics relating to Children and Young People Mental Health Indicators were corrected on 9 February 2018. These statistics are meant to count people as accessing services in each financial year where they have received two care contacts. The statistics released originally incorrectly excluded people who accessed services in in a previous financial year. NHS Digital apologises for any inconvenience caused. Please note: MHSDS Monthly: Final July to September 2017 Children and Young People Mental Health Indicators Reference Tables has been updated with an additional note, and now includes the statistics for the number of people under the age of 18 being treated on adult wards. A revised version of Bed days on adult wards for people aged 0-17 and Number of people aged 0-17 on adult wards is available on our supplementary information pages; this file adjusts the measures for known data quality issues to produce the most accurate information possible. A correction has been made to this publication on 10 September 2018. This amendment relates to statistics in the monthly CSV data file; the specific measures effected are listed in the “Corrected Measures” CSV. All listed measures have now been corrected. NHS Digital apologises for any inconvenience caused.