Global Data Mesh Market Share size & share value expected to touch USD 3,375.87 Million by 2032, to grow at a CAGR of 16.3% during the forecast period.

Medical Subject Headings (MeSH) is a hierarchically-organized terminology for indexing and cataloging of biomedical information. It is used for the indexing of PubMed and other NLM databases. Please see the Terms and Conditions for more information regarding the use and re-use of MeSH. NLM produces Medical Subject Headings XML, ASCII, MARC 21 and RDF formats. Updates to the data files are made according to the following schedule: MeSH XML MeSH Descriptor files updated annually MeSH Qualifier files updated annually MeSH Supplemental Concept Records (SCR) updated daily (Monday - Friday) MeSH ASCII MeSH Descriptor files updated annually MeSH Qualifier files updated annually MeSH Supplemental Concept Records (SCR) updated daily (Monday - Friday) MeSH MARC21 All files posted monthly MeSH RDF All files posted daily (Monday - Friday)

Based on Landgate’s high resolution aerial imagery, CSIRO generate the Urban Monitor mosaic from which vegetation height strata of endemic and exotic species has been calculated and reported as an area for each height strata of 0 – 3 m, 3 – 8 m, 8 – 15 m and 15+ m. The area of grass covered areas falling into the 0 – 50 cm range has also been calculated and recorded in square metres. Vegetation coverage greater than 3 metres in height has been deemed tree canopy. The canopies have been aggregated and reported as total canopy coverage in square metres. Urban Forest Mesh Blocks have been published for the following years: 2009, 2014, 2016, 2018, 2024. Parcels to be analysed were sourced from the 2024 Integrated Land Information Database (ILID) and supplied to CSIRO by the Department of Planning, Lands and Heritage. The results were assembled into Urban Forest features where the Urban Monitor coverage was complete. Land parcels were assigned locational data (2021 ABS meshblocks, suburbs, local government authority (LGA) and planning sub-region) based on the parcel centroid. They were then attributed with the following land use categories: • Street Block: residential, commercial, industrial, hospital/medical, educational, and some agricultural and transport land uses • Parks: public parks, open space, private recreation grounds and State Forest • Roads: roads including road reserves • Other Infrastructure: rail, airports and utilities infrastructure • Other: land uses in transition that have not progressed sufficiently to be Street Blocks or do not conform to urban form • Rural: primary production land that does not fall in categories above • Water: ocean and other waterways, including reservoirs The vegetation height strata areas and total canopy coverage values were calculated for each land parcel. The statistics were then aggregated by the field MB_MonitorCategory, a concatenation of the fields MB_CODE2021 and MonitorCategory. Canopy coverage percentages and ranges were calculated based on the sum of the area of parcels within each MB_MonitoryCategory. As parcels with sufficient Urban Monitor coverage for Urban Forest analysis may vary between years making comparison difficult, a field called MBPercentage was added which shows the percentage of the total MB_MonitorCategory area (MBArea) covered by parcels with Urban Forest values for that year. Corresponding MBPercentage values for all published years were also added to inform users. NOTE: As mesh block attributes were assigned based on parcel centroid, aggregated mesh block boundaries based on the parcels may not match ABS mesh block boundaries, and MBAreas will not match ABS mesh block areas.

Data Mesh Market size was valued at USD 4.1 Billion in 2024 and is projected to reach USD 12.5 Billion by 2032, growing at a CAGR of 8.5% from 2025 to 2032.

Global Data Mesh Market Drivers

1. Growing Use of Data Architecture Decentralisation To improve agility and scalability, organisations are moving away from centralised data lakes and towards data mesh architectures. Data management constraints are lessened by domain-driven ownership made possible by data mesh.
2. Increasing Need for Processing Data in Real Time Real-time analytics are necessary for businesses to make better decisions. Distributed data ownership is supported by data mesh, enabling quicker insights and increased operational effectiveness.
3. A rise in the volume and complexity of data Businesses produce enormous volumes of data via cloud apps, IoT, and AI. Scalability is a problem for traditional data architectures, which is why data mesh is a desirable substitute.

In 2022, ** percent of respondents currently using microservices state that implementing service mesh for microservices is complex and therefore a challenge. Importantly, those who already use microservices name complexity a challenge, while less of those that are planning to use microservices in the future expect it to be a challenge.

Mesh Wi-Fi systems are all the hype these days, but there is no surprise either. People hate Wi-Fi dead zones as much as they hate standing in a queue. In the last decade alone, the mesh Wi-Fi system market has seen incredible growth as well. How big is this industry,...

The ten most frequent terms in the input data set, including the depth levels at which each term appears in the MeSH hierarchy.

This submission contains a number of data files with vertices of meshed/interpolated surfaces used in the Phase 2B earth model. Examples include land surface (based on 10-meter DEM), the granitoid-basin fill contact, several faults, and also interpolated temperature isosurfaces for 175 and 225 degrees C. All data are georeferenced to UTM, zone 12N, NAD 83, NAVD 88.

This dataset is the Mesh Block (MB) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2011 for the Australian Capital Territory (ACT). For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.

This is the RSW fully tetrahedral unstructured mesh dataset for a cell-centered code, including the viscous wind tunnel wall. UG3 : Grid File Name = rsw_coarse_tetcc.b8.ugrid UG3 : Quad Surface Faces= 0 UG3 : Tria Surface Faces= 61750 UG3 : Nodes = 1357828 UG3 : Total Elements = 8050193 UG3 : Hex Elements = 0 UG3 : Pent_5 Elements = 0 UG3 : Pent_6 Elements = 0 UG3 : Tet Elements = 8050193 UG3 : BL Tet Elements = 7507013

This dataset provides four kind of freefom surfaces: a unit sphere, a fan disk, a f-theta lens and a block surface with their noisy version.

Working with partners across NIH, led by NIMHD and the NLM OBSSR-Behavioral Ontology Working Group, MeSH on November 29, 2022 added Federally recognized American Indian and Alaskan Native (AI/AN) tribal names and ethnic/ethnolinguistic minority terms as newly created type 5 SCR designated as “Population Groups”. These minority names (1,700+ terms) were mapped and reviewed by subject matter experts and scientists within NIH and from outside including Network of the National Library of Medicine members. Structure: All type 5 SCRs have common fields 1. CC=5 Population Group 2. ST=T098 Population Groups 3. HM= At least one HM is to an MH under Population Groups [M01.686] 4. TH= NIMHD(2023)

This dataset is the Mesh Block (MB) boundaries as defined by the Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July 2011 for Victoria (VIC). For the original data and more information, refer to the Australian Bureau of Statistics' Issue. The ABS encourages the use of the ASGS by other organisations to improve the comparability and usefulness of statistics generally, and in analysis and visualisation of statistical and other data. The Australian Statistical Geography Standard (ASGS) brings together in one framework all of the regions which the ABS and many others organisations use to collect, release and analyse geographically classified statistics. The ASGS ensures that these statistics are comparable and geospatially integrated and provides users with an coherent set of standard regions so that they can access, visualise, analyse and understand statistics.

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.

These datasets contain counts of the total usual resident population and total dwelling count from the 2011 Census of Population and Housing for Mesh Blocks. Mesh Blocks are the smallest geographic region in the Australian Statistical Geography Standard (ASGS), and the smallest geographical unit for which Census data are available. In 2011, there were approximately 347,000 Mesh Blocks covering the whole of Australia without gaps or overlaps. They broadly identify land use such as residential, commercial, agricultural and parks etc. Mesh Blocks are identified with a unique 11 digit code. Most residential Mesh Blocks contain approximately 30 to 60 dwellings. Mesh Blocks have been designed to be small enough to aggregate accurately to a wide range of spatial units and thus enable a ready comparison of statistics between geographical areas. These are large enough to protect against accidental disclosure.

For 2011, Mesh Block counts are available by usual residence for basic person counts and dwelling counts.

\*Persons Usually Resident: This is the count of people where they usually live, which may or may not be where they were on Census Night. This data is coded from the address supplied to the question "Where does the person usually live?". For more information about usual residence, see Place of Usual Residence in the Census Dictionary, 2011 (cat. no. 2901.0).

\*Dwellings: A dwelling is a structure which is intended to have people live in it, and which is habitable on Census Night. Some examples of dwellings are houses, motels, flats, caravans, prisons, tents, humpies and houseboats. All occupied dwellings are counted in the Census. Unoccupied private dwellings are also counted with the exception of those in caravan parks, marinas and manufactured home estates. For more information about dwellings, see Dwelling Type in the Census Dictionary, 2011 (cat. no. 2901.0).

For the 2006 Census, experimental Mesh Blocks were developed and counts for usual residence population and total dwellings were provided for each Mesh Block. The boundaries were reviewed and revised in preparation for the 2011 Census.

Dataset History

No lineage information was provided with the data from the Australian Bureau of Statistics.

Dataset Citation

Australian Bureau of Statistics (2014) ABS Mesh Block Population Counts Aus 2011. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/ee39fa76-db4e-412a-af0a-115d965b5813.

CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24).

Live mesh network monitoring link: http://dsg.ac.upc.edu/qmpsu

The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00". The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture.

A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid.

Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:
feature.rate are columns computed from feature as
feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])
feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted
where n is the number of samples

features
- processes number of processes
- loadavg.m1 1 minute load average
- softirq.rate servicing softirqs
- iowait.rate waiting for I/O to complete
- intr.rate
- system.rate processes executing in kernel mode
- idle.rate twiddling thumbs
- user.rate normal processes executing in user mode
- irq.rate servicing interrupts
- ctxt.rate total number of context switches across all CPUs
- nice.rate niced processes executing in user mode
- nr_slab_unreclaimable The part of the Slab that can't be reclaimed under memory pressure
- nr_anon_pages anonymous memory pages
- swap_cache Memory that once was swapped out, is swapped back in but still also is in the swapfile
- page_tables Memory used to map between virtual and physical memory addresses
- swap
- eth.txe.rate tx errors over all ethernet interfaces
- eth.rxe.rate rx errors over all ethernet interfaces
- eth.txb.rate tx bytes over all ethernet interfaces
- eth.rxb.rate rx bytes over all ethernet interfaces
- eth.txp.rate tx packets over all ethernet interfaces
- eth.rxp.rate rx packets over all ethernet interfaces
- wifi.txe.rate tx errors over all wireless interfaces
- wifi.rxe.rate rx errors over all wireless interfaces
- wifi.txb.rate tx bytes over all wireless interfaces
- wifi.rxb.rate rx bytes over all wireless interfaces
- wifi.txp.rate tx packets over all wireless interfaces
- wifi.rxp.rate rx packets over all wireless interfaces
- txb.rate tx bytes over all ethernet and wifi interfaces
- txp.rate tx packets over all ethernet and wifi interfaces
- rxb.rate rx bytes over all ethernet and wifi interfaces
- rxp.rate rx packets over all ethernet and wifi interfaces
- sum.xb.rate tx+rx bytes over all ethernet and wifi interfaces
- sum.xp.rate tx+rx packets over all ethernet and wifi interfaces
- diff.xb.rate tx-rx bytes over all ethernet and wifi interfaces
- diff.xp.rate tx-rx packets over all ethernet and wifi interfaces

Service Mesh Market is predicted to reach $3450.6 million by 2030 with a CAGR of 39.7% from 2023 to 2030

This dataset with the derived statistical mesh of 500 meters side and according to the 1 square kilometer mesh defined in the framework of GISCO (Geographic Information System of the COmmission) by Eurostat, is non-exact basic mapping used for the symbolic representation of geospatial statistics. The information is provided in the geodetic system of geographic coordinates WGS84 (World Geodetic System 1984, SRID:4326), used by the Canary Islands Institute of Statistics (ISTAC), in various formats and with the cells cut by a generalized coastline. The alphanumeric data is encoded in UTF-8.

BodyParts3D organ model data with the polygon reduction rate of 99%. The zip-compressed download files contain multiple files of ELEMENT file ID-specific polygon data in Wavefront OBJ format.

This is the RSW dataset for a fine fully tetrahedral grid designed for a cell-centered unstructured solver. UG3 : Grid File Name = rsw_fine_tetcc.b8.ugrid UG3 : Quad Surface Faces= 0 UG3 : Tria Surface Faces= 297278 UG3 : Nodes = 12009356 UG3 : Total Elements = 71561254 UG3 : Hex Elements = 0 UG3 : Pent_5 Elements = 0 UG3 : Pent_6 Elements = 0 UG3 : Tet Elements = 71561254 UG3 : BL Tet Elements = 66001229

Abstract

The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset shows the Australian Bureau of Statistics (ABS) Mesh Blocks across Victoria, with the 2011 census population and housing counts attached as attributes. It was derived by the Bioregional Assessment Programme from the ABS Mesh Block Population Counts Aus 2011 dataset, and the ABS Boundaries 2011 dataset. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Dataset History

The ABS 2011 Mesh Block Population Count spreadsheet (https://data.bioregionalassessments.gov.au/datastore/dataset/ee39fa76-db4e-412a-af0a-115d965b5813) was joined to the Victorian ABS Mesh Block boundaries (https://data.bioregionalassessments.gov.au/datastore/dataset/8b65c3a4-7010-4a79-8eaa-5621b750347f) using the unique MB_CODE11 field within ESRI ArcMap 10.2.

Two additional fields were added to show Mesh Block Area (km2) and Population Density (people/km2). These field values were calculated within ESRI ArcMap 10.2 using the Field Calculator tool.

Dataset Citation

Bioregional Assessment Programme (2014) Victorian ABS Mesh Block Population 2011. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/b27fdf82-dd1e-4841-a228-21f671a95368.

Dataset Ancestors

• Derived From ABS Mesh Block Population Counts Aus 2011

• Derived From ABS Boundaries 2011