Bk Bom

## Overview

Bk Bom is a dataset for object detection tasks - it contains People annotations for 426 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

BOM Software Market Outlook

The global BOM (Bill of Materials) Software market size is poised for substantial growth, with market figures expected to rise from USD 1.23 billion in 2023 to USD 2.85 billion by 2032, reflecting a compound annual growth rate (CAGR) of 9.8% during the forecast period. This impressive growth trajectory can be attributed to several key factors, including the increasing complexity of manufacturing processes, the heightened focus on operational efficiency, and the rapid adoption of digital transformation initiatives across industries. The demand for effective BOM solutions is expanding as industries recognize the need for efficient materials management, cost control, and streamlined production processes.

A significant growth factor in the BOM software market is the escalating complexity of manufacturing operations. As industries from automotive to electronics embrace advanced manufacturing techniques, the need for precise and comprehensive materials management becomes paramount. BOM software solutions enable companies to maintain accurate records, manage changes efficiently, and ensure that all components are available when needed. This is particularly crucial in industries like aerospace and defense, where stringent quality and compliance standards necessitate meticulous tracking of materials and components. The ability of BOM software to integrate with other enterprise systems further enhances its value, making it an integral part of modern manufacturing ecosystems.

Another key driver is the growing emphasis on operational efficiency. In an era where time-to-market and cost optimization are critical competitive differentiators, organizations are turning to BOM software to streamline their production processes. By providing a centralized and accurate repository of product data, these solutions help eliminate errors, reduce waste, and enable seamless collaboration across departments. This is especially valuable in sectors like automotive and electronics, where the ability to quickly adapt to changes in design and production volumes can significantly impact profitability. The integration of BOM software with supply chain management and enterprise resource planning systems further enhances its effectiveness, allowing for real-time updates and improved decision-making.

Digital transformation initiatives are also playing a pivotal role in the growth of the BOM software market. As businesses increasingly adopt Industry 4.0 technologies, the need for digital solutions that can support these initiatives becomes critical. BOM software, with its capabilities for data integration, analytics, and automation, is well-suited to meet these needs. By enabling manufacturers to harness the power of data, these solutions facilitate predictive maintenance, demand forecasting, and overall process optimization. The ability to leverage cloud-based BOM solutions further extends their reach, allowing organizations to connect and collaborate globally, which is crucial in today's interconnected world.

In terms of regional outlook, North America and Europe are expected to remain dominant players in the BOM software market, driven by the presence of major manufacturing hubs and technological advancements. The Asia Pacific region, however, is anticipated to exhibit the highest growth rate, fueled by the rapid industrialization in countries like China and India. The increasing adoption of automation and digital technologies in these regions is creating a fertile ground for the expansion of BOM software solutions. The Middle East & Africa and Latin America also present significant opportunities, as industries in these regions begin to invest in digital transformation and seek efficient tools to enhance productivity and competitiveness.

Component Analysis

The BOM Software market is strategically segmented by components, primarily into software and services. The software component is a critical aspect of this market, providing the backbone for effective BOM management. The software segment encompasses a variety of tools and platforms designed to facilitate the creation, management, and modification of BOMs. These solutions are increasingly sophisticated, offering features such as advanced analytics, integration capabilities, and user-friendly interfaces. As manufacturing processes become more complex, the demand for robust BOM software that can handle intricate product structures and multiple variations continues to grow.

Within the software segment, there is a significant trend towards customization and scalability. Companies are seeking solutions that can be ta

BOM

## Overview

BOM is a dataset for object detection tasks - it contains Tables annotations for 251 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The river height data is real-time operational data from automated telemetry systems and has not been quality controlled.The data is provided for flood warning purposes and most data will not be available during non flood periods.Most river height data is provided to the Bureau of Meteorology by other agencies. Separate approval may be required to use the data for other purposes.Additional river height data is available from the NSW Office of Water and from the NSW Department of Service Technology and Administration (Manly Hydraulics Laboratory).

The Australian Bureau of Meteorology (BoM) routinely produced 500 mb pressure level forecasts from its Global/Regional Assimilation Prognosis (GASP) model runs. Two model runs were produced daily (00 and 12 UTC). GASP Model output were available from the BoM McIDAS system and the 500 mb pressure level 48-hour forecast charts were archived as GIF imagery. Height contours were plotted every 50 m.

Extraction of the operational global wave forecast system (AUSWAVE-G) of the Australian Bureau of Meteorology (BOM) with a resolution of 0.25° for the Pacific region.

This product is an extraction of the full model output provided by BOM. Only the first forecast of the day for a portion of the Pacific and for a few variables is provided here.

Below is the product description on the BOM website (http://www.bom.gov.au/nwp/doc/auswave/data.shtml):

"The AUSWAVE wave model source code was upgraded from the third-generation wind-wave modelling framework WAVEWATCH III® (WW3) version 3.14 to version 4.18 in early 2016. This included the use of a new physical spectral source term package. Operational runs were performed using surface wind data from the Australian Community Climate and Earth-System Simulator (ACCESS). This model was developed and tested by staff in Bureau National Operations Centre and Research and Development. For more details about this upgrade see the Bureau National Operations Centre Operations Bulletin 106.

The global AUSWAVE-G wave system was upgraded to the new version on 26 May 2020 to incorporate the surface winds from the "Australian Parallel Suite 3" (APS3) ACCESS-G, rather than the previous APS2 version. In addition, the regional AUSWAVE-R wave model was upgraded to use the surface winds from ACCESS-G3 rather than ACCESS-R2."

Rain Forecast for Maitland from BOM

Access API

These spatial datasets represent the Bureau of Meteorology's National rainfall gauge and river level gauge flood network, which is utilised for delivering the flood warning service.

Flood gauges are classified by forecast, information, data and tide gauge locations. Also includes Bureau flood watch catchments and flood warning catchments.

Flood watch and warning catchments include AAC attribution, which refers to the Australian Meteorological and Oceanographic Code (AMOC) Area Code. These codes can be used to link areas and sites to the Bureau’s XML forecast products such as flood warnings and flood watches.

In flood warning catchments the AAC_PARENT is the AAC of the parent catchment for sub-catchment areas. If the catchment has no sub-catchments, the AAC and AAC_PARENT will be identical. If the catchment has sub-catchments, AAC_PARENT is null.

In the flood watch catchments layer, the AAC_PARENT is only used for grouping.

Supporting datasets include Geofabric V3x , showing major rivers and all rivers, streams and creeks, as well as Local Government Areas.

Metadata

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details at the time of acquisition.

Data has been aquired under legislation by the Bureau. Aggregated from different State bodies the data summarises all the groundwater management arrangements in different states and, where possible, has a entitlment volume associated.

Dataset History

No history of the dataset was provided.

Dataset Citation

Bureau of Meteorology (2015) National Groundwater Management Zones BOM 20150730. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/1004e325-f94b-44f5-89e2-9402e8f4ad67.

The Bureau of Meteorology (Bureau) receives water data, measured at stations around Australia, from organisations named in the Water Regulations (2008). Time series data collected from approximately 6500 measurement stations across Australia are available. This includes the following nine data subcategories:\r \t\r * Watercourse Level (Water Regulations subcategory 1a)\r * Watercourse Discharge (Water Regulations subcategory 1b)\r * Storage Level (Water Regulations subcategory 3a)\r * Storage Volume (Water Regulations subcategory 3b)\r * Rainfall (Water Regulations subcategory 4a)\r * Electrical Conductivity of surface water @ 25C (Water Regulations subcategory 9a)\r * Turbidity of surface water (Water Regulations subcategory 9d)\r * pH of surface water (Water Regulations subcategory 9g)\r * Temperature of surface water (Water Regulations subcategory 9h)\r \t\r The above data subcategories are available in the Water Data Online product - http://www.bom.gov.au/waterdata/.\r \r Please see the Water Data Online FAQ tab for further information regarding this dataset.\r \r Please see the Water Data Online Copyright tab for information regarding licensing and use of data.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details.

This data has been extracted by the Water Data Support team in the Bureau of Meteorology for selected gauges in the Sydney Basin from Bureau internal systems. Data has been originally supplied to the Bureau by NSW office of Water through the Water Act Regulations, 2008.

The dataset consists of stream gauging data for the Sydney subregion and was provided by the Bureau of Meteorology in January 2015. It includes discharge totals measured in megalitres for 116 gauging stations and instantaneous levels for 126 gauging stations in .csv format, recorded at 9:00am daily, stored in separate folders. Files represent the complete daily record for the station up until 17/12/2014. Filenames correspond to the gauging station numbers (e.g. 210001.csv). It was used to generate the unified streamflow dataset that is used for surface water and groundwater hydrological modelling.

The metadata folder includes the quality codes used for the gauging data as follows:

\#QUALITY\tTEXT

10\tQuality-A Best Available

11\tBOM derived value

20\tQuality-B Compromised

30\tQuality-C Estimate

31\tEstimate : AWRIS derived data

40\tThe record set has intermittent timing and should not be analysed at a timing resolution\160 less than that supplied.

150\tQuality-E Unknown

151\tBOM Edited - The record set's ability to truly represent the monitored parameter is not known.

200\tQuality-F Missing or not of release quality

201\tBOM edited - The record set is not of release quality or contains missing data.

Dataset History

The dataset was initially created by the BoM in 15 January 2015. This data has been extracted by the Water Data Support team in the Bureau of Meteorology for selected gauges in the Sydney Basin from Bureau internal systems. Data has been originally supplied to the Bureau by NSW office of Water through the Water Act Regulations, 2008.

It was used to generate the unified streamflow data in 28 January 2015.

Data is supplied in CSV format

The following table describes the quality codes for the data

\#QUALITY\tTEXT

10\tQuality-A Best Available

11\tBOM derived value

20\tQuality-B Compromised

30\tQuality-C Estimate

31\tEstimate : AWRIS derived data

40\tThe record set has intermittent timing and should not be analysed at a timing resolution\160 less than that supplied.

150\tQuality-E Unknown

151\tBOM Edited - The record set's ability to truly represent the monitored parameter is not known.

200\tQuality-F Missing or not of release quality

201\tBOM edited - The record set is not of release quality or contains

Dataset Citation

Bureau of Meteorology (2015) SYD ALL Raw Stream Gauge Data BoM v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/b2cfd949-3ef0-4d2e-98c2-120f11b32be4.

The Australian Bureau of Meteorology (BoM) routinely produced 500 mb pressure level analyses from its Global/Regional Assimilation Prognosis (GASP) model runs. Two model runs were produced daily (00 and 12 UTC). GASP model output were available from the BoM McIDAS system and the 500 mb analyses were archived as GIF imagery. Height contours were plotted every 50 m.

Extraction of the operational regional wave forecast system (AUSWAVE-R) of the Australian Bureau of Meteorology (BOM) with a resolution of 0.1° for the Pacific region.

This product is an extraction of the full model output provided by BOM. Only the first forecast of the day for a portion of the Pacific and for a few variables is provided here.

Below is the product description on the BOM website (http://www.bom.gov.au/nwp/doc/auswave/data.shtml):

"The AUSWAVE wave model source code was upgraded from the third-generation wind-wave modelling framework WAVEWATCH III® (WW3) version 3.14 to version 4.18 in early 2016. This included the use of a new physical spectral source term package. Operational runs were performed using surface wind data from the Australian Community Climate and Earth-System Simulator (ACCESS). This model was developed and tested by staff in Bureau National Operations Centre and Research and Development. For more details about this upgrade see the Bureau National Operations Centre Operations Bulletin 106.

The global AUSWAVE-G wave system was upgraded to the new version on 26 May 2020 to incorporate the surface winds from the "Australian Parallel Suite 3" (APS3) ACCESS-G, rather than the previous APS2 version. In addition, the regional AUSWAVE-R wave model was upgraded to use the surface winds from ACCESS-G3 rather than ACCESS-R2."

Significant progress has been made globally in reducing GHG emissions from the operation of buildings, however, huge challenges still remain in mitigating the embodied emissions from the manufacturing, transportation, and disposing of building materials. This is particularly relevant in BC, where electricity is largely generated from renewable sources, indicating limited potential for further reducing GHG emissions from building operations. Therefore, investigating the options to reduce embodied GHG emissions in building materials presents another crucial opportunity to further mitigate the overall GHG emissions from buildings. For example, the City of Vancouver’s newly released Climate Emergency Action Plan has set an ambitious goal of reducing 40% embodied GHG emissions in new buildings compared to the 2018 benchmark. To support decision-making that could ultimately fulfill such an ambitious goal, it is imperative that a standard approach is used to derive benchmark buildings and the corresponding bill-of-materials (BoM). Accordingly, we compiled a BoM dataset of 35 typical buildings in Canada. The data was classified into “whole-building level” and “assembly-level”, and building materials were sorted by an aggregation system (see below) in both classifications. Whole-building-level BoM contains data for 33 buildings, including institutional buildings and residential houses at the University of British Columbia, container-based single-family housing, single-family residential building, precast concrete commercial buildings, etc. On the other hand, assembly-level BoM contains material data for different structural components of one multi-unit apartment and one typical newly-built single-family home in Vancouver. The aggregation system organizes the material data by three tiers - M1, M2, and M3, which offers 3 hierarchical levels of specificity. The first hierarchical level (M1) provides the least specified information while the final level (M3) provides the most detailed information. For example, Aluminum cold-rolled sheet (M3) is categorized within Aluminum (M2) under Metal (M1). This aggregation system offers the flexibility for LCA practitioners to obtain BoM information at the resolution that fits their scope of work.

Bill of Materials (BOM) Software Market size was valued at USD 8.4 Billion in 2023 and is projected to reach USD 10.9 Billion by 2031, growing at a CAGR of 12.3% during the forecast period 2024 to 2031.

Global Bill of Materials (BOM) Software Market Drivers

The market drivers for the Bill of Materials (BOM) Software Market can be influenced by various factors. These may include:

Increasing Complexity of Products:Modern products are becoming more sophisticated, often requiring hundreds or even thousands of components. Managing such complex assemblies manually is not feasible, driving the need for advanced Bill of Materials (BOM) software to ensure accuracy and efficiency in production planning.

Global Supply Chain Integration: Companies are increasingly relying on globally distributed supply chains. Bill of Materials (BOM) software facilitates seamless integration and communication between various stakeholders, including suppliers, manufacturers, and logistics providers, enhancing coordination and reducing lead times.

Regulatory Compliance: Explanation: Many industries are subject to stringent regulations regarding product safety, environmental impact, and quality standards. Bill of Materials (BOM) software helps companies maintain compliance by tracking the origin and specifications of each component, thereby simplifying audits and reporting.

Innovation and Product Development Speed: The competitive market requires rapid innovation and faster time-to-market for new products. Bill of Materials (BOM) software enables more efficient design and prototyping processes by providing accurate, up-to-date component information and facilitating collaboration among R&D teams.

Cost Management: Companies are under constant pressure to reduce costs. Bill of Materials (BOM) software helps in cost optimization by providing detailed insights into the cost structure of each product, enabling better budgeting, cost forecasting, and identification of cost-saving opportunities through alternative sourcing or material substitution.

Data Integration and Real-Time Updates: With the rise of the Internet of Things (IoT) and Industry 4.0, there is an increasing need for real-time data integration. Bill of Materials (BOM) software offers connectivity with enterprise systems like ERP and PLM, providing live updates and ensuring that the latest information is always available for decision-making.

Quality Assurance and Risk Management: Bill of Materials (BOM) software aids in quality assurance by allowing for thorough tracking and documentation of all parts and their relationships. This capability is crucial for risk management, enabling prompt responses to defects, recalls, or any disruptions in the supply chain.

Customization and Personalization Trends: The growing trend of product customization requires flexible and adaptable manufacturing processes. Bill of Materials (BOM) software supports these trends by allowing manufacturers to easily modify and manage various configurations and custom orders without compromising on efficiency or accuracy.

Technological Advancements: Advances in cloud computing, AI, and machine learning are enhancing the capabilities of Bill of Materials (BOM) software. These technological improvements are driving market growth by offering more scalable, user-friendly, and intelligent solutions that can handle complex data and provide actionable insights.

Sustainability Initiatives: Sustainability and eco-friendly production practices are becoming key market differentiators. Bill of Materials (BOM) software supports sustainability efforts by enabling better tracking of materials, promoting the use of sustainable resources, and reducing waste through more efficient production planning. Conclusion

Climate Data Online offers temperature, rainfall and solar exposure values, per day, and per month for each station, for all years that a Station has been operating and measuring that variable. …Show full descriptionClimate Data Online offers temperature, rainfall and solar exposure values, per day, and per month for each station, for all years that a Station has been operating and measuring that variable. Data is from both closed and open stations. Data timespans vary across stations. The earliest data is for Parramatta, commencing 1832. Data values are typically incorporated into CDO within 24 hours of the value being recorded, but QC of the data can take some time. The data's QC-status is indicated by the font (colour and italics). Rainfall: Daily rainfall Observations are nominally made at 9 am local clock time, and record the total for the previous 24 hours. Rainfall includes all forms of precipitation that reach the ground, such as rain, drizzle, hail and snow. For more information, see: [ About daily rainfall data: http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0009.shtml ] and ["About rain data: http://www.bom.gov.au/climate/cdo/about/about-rain-data.shtml ] and [ About measuring rain: http://www.bom.gov.au/climate/cdo/about/rain-measure.shtml ]. The Monthly rainfall data is the total of all available Daily rainfall for the month. Rainfall includes all forms of precipitation that reach the ground, such as rain, drizzle, hail and snow. [ More information about monthly rainfall data: http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0001.shtml ]. More information about Temperature measurements : [http://www.bom.gov.au/climate/cdo/about/about-airtemp-data.shtml ]. Temperature (Daily maximum or minimum): The Daily minimum or maximum air temperature is nominally recorded at 9 am local clock time. The daily maximum air temperature is the highest temperature for the 24 hours leading up to the observation, and is recorded as the maximum temperature for the previous day. The daily minimum air temperature is the lowest temperature for the 24 hours leading up to the observation, and is recorded as the minimum temperature for the day on which the observation was made. Temperature data prior to 1910 should be used with extreme caution as many stations prior to that date used non-standard shelters. [See: http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0011.shtml (minT) and http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0010.shtml (maxT) ] Temperature (Mean minimum or maximum, per month) : The Monthly mean minimum (or maximum) temperature is the average of all available daily minima (or maxima) for the month. For detail of daily Temperature, see above. [ For more detail on Mean minimum or maximum, per month, see : http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0002.shtml ] Temperature (Lowest or Highest per month, for each month) : The Monthly highest (or Lowest) temperature is the highest (or lowest) of all available daily maxima (or minima) for the month. For detail of daily Temperature, see above. [ For more details, see: http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0006.shtml ] Temperature (Lowest Maximum or Highest Minimum temperature per month, for each month) : The Monthly lowest maximum (or highest minimum) temperature is the lowest (or highest) of all available daily maxima (or minima) for the month. For detail of daily Temperature, see above. Solar Exposure (Daily): The Daily global solar exposure (per station) is the total solar energy for a day falling on a horizontal surface. It is measured from midnight to midnight. The values are usually highest in clear sun conditions during the summer and lowest during winter or very cloudy days. Units of Measurements are MJ/m2. The Monthly mean daily global solar exposure is the average of all available daily Solar Exposure for the month. For more details about the Daily Solar Exposure product, see: [ http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0016.shtml ]. Info about solar exposure: [ http://www.bom.gov.au/climate/austmaps/solar-radiation-glossary.shtml#globalexposure ; For more details about the Monthly Sol.Exp product, see: [ http://www.bom.gov.au/climate/cdo/about/about-IDCJAC0003.shtml ].

The Bureau of Meteorology (Bureau) receives water data, measured at stations around Australia, from organisations named in the Water Regulations (2008). Time series data collected from approximately 6500 measurement stations across Australia are available. This includes the following nine data subcategories:

• Watercourse Level (Water Regulations subcategory 1a)
• Watercourse Discharge (Water Regulations subcategory 1b)
• Storage Level (Water Regulations subcategory 3a)
• Storage Volume (Water Regulations subcategory 3b)
• Rainfall (Water Regulations subcategory 4a)
• Electrical Conductivity of surface water @ 25C (Water Regulations subcategory 9a)
• Turbidity of surface water (Water Regulations subcategory 9d)
• pH of surface water (Water Regulations subcategory 9g)
• Temperature of surface water (Water Regulations subcategory 9h)

The above data subcategories are available in the Water Data Online product - http://www.bom.gov.au/waterdata/.

Please see the Water Data Online FAQ tab for further information regarding this dataset.

Please see the Water Data Online Copyright tab for information regarding licensing and use of data.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Mean monthly and mean annual rainfall grids. The grids show the rainfall values across Australia in the form of two-dimensional array data. The mean data are based on the standard 30-year period 1961-1990.

To view the full metadata statement, you will need to download the dataset and view in the 'Description' tab in ArcCatalog.

This dataset has been provided to the BA Programme for use within the programme only. For copyright information go to http://www.bom.gov.au/other/copyright.shtml. Information on how to request a copy of data can be found at www.bom.gov.au/climate/data.

Dataset History

Gridded data were generated using the ANU (Australian National University) 3-D Spline (surface fitting algorithm).

The resolution of the data is 0.025 degrees ( approximately 2.5km) - as part of the 3-D analysis process a 0.025 degree resolution digital elevation model (DEM) was used.

Approximately 6000 stations were used in the analysis over Australia. All input station data underwent a high degree of quality control before analysis, and conform to WMO (World Meteorological Organisation) standards for data quality.

Dataset Citation

Bureau of Meteorology (2008) BOM, Australian Average Rainfall Data from 1961 to 1990. Bioregional Assessment Source Dataset. Viewed 12 March 2019, http://data.bioregionalassessments.gov.au/dataset/fd91f2d4-2cc8-4d5d-9f67-8fe8af1e2676.

The Australian Bureau of Meteorology (BoM) routinely produced Mean Sea Level (MSL) pressure analyses from its Global/Regional Assimilation Prognosis (GASP) model runs. Two model runs were produced daily (00 and 12 UTC). GASP model output were available from the BoM McIDAS system and the MSL analyses were archived as GIF imagery. Pressure contours (isobars) were plotted every 4 mb.

The Australian Bureau of Meteorology (BoM) routinely produced forward trajectory forecasts from its Global/Regional Assimilation Prognosis (GASP) model runs. Two trajectory runs (out to 36 hours) were produced daily (00 and 12 UTC). Selected points for ACE-1 were plotted by NCAR/Research Data Program on the ZEBRA workstation and archived as GIF imagery. Heights of the parcel trajectories are color coded in pressure (mb) levels.