Graph Database for Grid Topology Market Outlook

According to our latest research, the Global Graph Database for Grid Topology market size was valued at $1.4 billion in 2024 and is projected to reach $6.7 billion by 2033, expanding at a robust CAGR of 18.9% during the forecast period of 2025–2033. The primary factor driving this impressive growth is the increasing complexity of energy grids, which requires advanced data management and real-time analytics to optimize grid operations, improve resilience, and integrate renewable energy sources. As utilities and smart grid operators face mounting pressure to modernize infrastructure and enhance network reliability, the adoption of graph database solutions for grid topology is accelerating globally, enabling more dynamic, efficient, and intelligent grid management.

Regional Outlook

North America currently commands the largest share of the global graph database for grid topology market, accounting for nearly 38% of total market value in 2024. This dominance is attributed to the region’s mature utility sector, widespread adoption of smart grid technologies, and robust regulatory frameworks supporting grid modernization initiatives. The United States, in particular, has witnessed significant investments in upgrading grid infrastructure, integrating distributed energy resources, and enhancing cybersecurity, all of which necessitate sophisticated data management solutions. Major utility companies and technology vendors in North America are leveraging graph databases to enable real-time visualization, fault detection, and predictive maintenance, further consolidating the region’s leadership in this market.

In contrast, the Asia Pacific region is emerging as the fastest-growing market, projected to register an impressive CAGR of 22.5% from 2025 to 2033. Rapid urbanization, surging energy demand, and ambitious government initiatives to deploy smart grids are driving the adoption of graph database technologies in countries such as China, Japan, South Korea, and India. These nations are investing heavily in digital grid infrastructure, renewable energy integration, and advanced metering systems. The growing presence of global and regional technology vendors, coupled with supportive policy frameworks, is fostering innovation and accelerating market expansion in Asia Pacific, positioning it as a key growth engine for the global graph database for grid topology market.

Meanwhile, emerging economies in Latin America and Middle East & Africa are experiencing a gradual yet steady uptake of graph database solutions for grid topology. While market penetration remains relatively low compared to developed regions, localized demand is being driven by efforts to reduce energy losses, combat grid theft, and support electrification in underserved areas. However, these regions face challenges such as limited digital infrastructure, budget constraints, and regulatory uncertainties, which can impede large-scale adoption. Nonetheless, international development programs and cross-border collaborations are beginning to address these barriers, paving the way for future growth and technology transfer in these emerging markets.

Report Scope

The Arctic and Antarctic Research Institute (AARI) in St. Petersburg, Russia, produces sea ice charts for safety of navigation in the polar regions and for other operational and scientific purposes. Arctic charts from 1933 through 2006 are collected in this data set. Chart frequency and spatial coverage varies, but charts were compiled every 10 days during the navigation season, and monthly for the rest of the year, over most of the series. There is a gap with no charts from 1993 through 1996. Chart coverage focuses on the Northern Sea Route, although later charts extend into the central Arctic. The charts were compiled from a variety of data sources, with heavy reliance on regular reconnaissance flights for most of the series until 1992. Early paper charts were digitized, and the entire series, including later charts that were produced entirely digitally, were converted to Sea Ice Grid (SIGRID) format at AARI. AARI provided code to read the SIGRID data and convert it to a format close to Equal Area Scalable (EASE)-Grid. NSIDC completed the conversion to EASE-Grid. The EASE-Grid is in a Lambert equal-area projection with 12.5 km cell size. Total ice concentration, as well as partial concentrations for multiyear, first-year, new/young ice (ice younger than first-year ice), and fast ice, are given in EASE-Grid (binary), SIGRID (ASCII), and browse (PNG) files. Data are available via FTP. This data set replaces and updates the previous data set, AARI 10-Day Arctic Ocean EASE-Grid Sea Ice Observations (nsidc-0050, discontinued December 2007), and the Russian Arctic charts on the Environmental Working Group Joint U.S.-Russian Arctic Sea Ice Atlas (http://nsidc.org/data/g01962.html). Access to the data is unrestricted, but users are encouraged to register for the data. Registered users will receive e-mail notification about any product changes.

The fast and robust identification of fault elements is essential for the security and continuous operation of the power grid. The existing methods might be maloperation for bad data disturbance and require strict and exact synchronization. To address the challenge, this paper uses the conflict graph to propose a new sensitivity graph signal model for the power grid fault diagnosis. Next, a novel graph Fourier transform (GFT)-based method is proposed to diagnose the fault branch. Firstly, the measurement sensitivity graph signals are constructed by the conflict graph model, where the data is from activated recorders and protection devices. Next, the eigenvalue and GFT coefficient are used to extract the frequency characteristics of the signals. The fault branches provide the maximum contribution rate to the high-frequency coefficient of GFT. Then, for each node, the importance degree of the measurement sensitivity conflict graph signal is defined. The high-frequency importance degree-based method is proposed to discriminate the fault branch. Finally, simulations and practical cases verify the correctness and effectiveness of the proposed method. The proposed method owns fast faults diagnosis and good practicability. Additionally, the identification accuracy is high and the method is robust to bad data interference, due to considering measured data from whole activated fault recorders and protection devices.

Neural Bayes estimators are neural networks that approximate Bayes estimators in a fast and likelihood-free manner. Although they are appealing to use with spatial models, where estimation is often a computational bottleneck, neural Bayes estimators in spatial applications have, to date, been restricted to data collected over a regular grid. These estimators are also currently dependent on a prescribed set of spatial locations, which means that the neural network needs to be retrained for new datasets; this renders them impractical in many applications and impedes their widespread adoption. In this work, we employ graph neural networks (GNNs) to tackle the important problem of parameter point estimation from data collected over arbitrary spatial locations. In addition to extending neural Bayes estimation to irregular spatial data, the use of GNNs leads to substantial computational benefits, since the estimator can be used with any configuration or number of locations and independent replicates, thus, amortizing the cost of training for a given spatial model. We also facilitate fast uncertainty quantification by training an accompanying neural Bayes estimator that approximates a set of marginal posterior quantiles. We illustrate our methodology on Gaussian and max-stable processes. Finally, we showcase our methodology on a dataset of global sea-surface temperature, where we estimate the parameters of a Gaussian process model in 2161 spatial regions, each containing thousands of irregularly-spaced data points, in just a few minutes with a single graphics processing unit. Supplementary materials for this article are available online.

IntroductionUK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is the stepping down of voltage as it is moved towards the household; this is achieved using transformers. Transformers have a maximum rating for the utilisation of these assets based upon protection, overcurrent, switch gear, etc. This dataset contains the Grid Substation Transformers, also known as Bulk Supply Points, that typically step-down voltage from 132kV to 33kV (occasionally down to 66 or more rarely 20-25). These transformers can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables. This dataset provides half-hourly current and power flow data across these named transformers from 2021 through to the previous month across our license areas. The data are aligned with the same naming convention as the LTDS for improved interoperability.Care is taken to protect the private affairs of companies connected to the 33kV network, resulting in the redaction of certain transformers. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted. To find which transformer you are looking for, use the ‘tx_id’ that can be cross referenced in the Grid Transformers Monthly Dataset, which describes by month what transformers were triaged, if they could be made public, and what the monthly statistics are of that site. If you want to download all this data, it is perhaps more convenient from our public sharepoint: Open Data Portal Library - Grid Transformers - All Documents (sharepoint.com)This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian.The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps.We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer.The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation.Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these transformers are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS transformer from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same transformer in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing transformers, incorrectly labelled transformers, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.OtherDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.Download dataset information: Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power NetworksTo view this data please register and login.

This is an Australian extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).AWS data licence is "CC BY-NC-SA 4.0", so use of this data must be:- non-commercial (NC)- reuse must be share-alike (SA)(add same licence).This restricts the standard CC-BY Figshare licence.A world speedtest open data was dowloaded (>400Mb, 7M lines of data). An extract of Australia's location (lat, long) revealed 88,000 lines of data (attached as csv).A Jupyter notebook of extract process is attached.A link to Twitter thread of outputs provided.A link to Data tutorial provided (GitHub), including Jupyter Notebook to analyse World Speedtest data, selecting one US State.Data Shows: (Q2)- 3.1M speedtests- 762,000 devices- 88,000 grid locations (600m * 600m), summarised as a point- average speed 33.7Mbps (down), 12.4M (up)- Max speed 724Mbps- data is for 600m * 600m grids, showing average speed up/down, number of tests, and number of users (IP). Added centroid, and now lat/long.See tweet of image of centroids also attached.Versions:v15/16. Add Hist comparing Q1-21 vs Q2-20. Inc ipynb (incHistQ121, v.1.3-Q121) to calc.v14 Add AUS Speedtest Q1 2021 geojson.(79k lines avg d/l 45.4Mbps)v13 - Added three colour MELB map (less than 20Mbps, over 90Mbps, 20-90Mbps)v12 - Added AUS - Syd - Mel Line Chart Q320.v11 - Add line chart compare Q2, Q3, Q4 plus Melb - result virtually indistinguishable. Add line chart to compare Syd - Melb Q3. Also virtually indistinguishable. Add HIST compare Syd - Melb Q3. Add new Jupyter with graph calcs (nbn-AUS-v1.3). Some ERRATA document in Notebook. Issue with resorting table, and graphing only part of table. Not an issue if all lines of table graphed.v10 - Load AURIN sample pics. Speedtest data loaded to AURIN geo-analytic platform; requires edu.au login.v9 - Add comparative Q2, Q3, Q4 Hist pic.v8 - Added Q4 data geojson. Add Q3, Q4 Hist pic.v7 - Rename to include Q2, Q3 in Title.v6 - Add Q3 20 data. Rename geojson AUS data as Q2. Add comparative Histogram. Calc in International.ipynb.v5 - add Jupyter Notebook inc Histograms. Hist is count of geo-locations avg download speed (unweighted by tests).v4 - added Melb choropleth (png 50Mpix) inc legend. (To do - add Melb.geojson). Posted Link to AURIN description of Speedtest data.v3 - Add super fast data (>100Mbps) less than 1% of data - 697 lines. Includes png of superfast.plot(). Link below to Google Maps version of superfast data points. Also Google map of first 100 data points - sample data. Geojson format for loading into GeoPandas, per Jupyter Notebook. New version of Jupyter Notebook, v.1.1.v2 - add centroids image.v1 - initial data load.** Future Work- combine Speedtest data with NBN Technology by location data (national map.gov.au); https://www.data.gov.au/dataset/national-broadband-network-connections-by-technology-type- combine Speedtest data with SEIFA data - socioeconomic categories - to discuss with AURIN.- Further international comparisons- discussed collaboration with Assoc Prof Tooran Alizadeh, USyd.

Introduction UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted. This dataset provides monthly statistics across these named circuits from 2021 through to the previous month across our license areas. The data is aligned with the same naming convention as the LTDS for improved interoperability.To find half-hourly current and power flow for the circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Half Hourly Data.If you want to download all this data, it is perhaps more convenient from our public sharepoint: SharepointThis dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological ApproachThe dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control StatementThe data is provided "as is". In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that.

Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible.Additional InformationDefinitions of key terms related to this dataset can be found in the Open Data Portal Glossary.

Download dataset information:Metadata (JSON)

We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information: Open Data Portal Reuses — UK Power NetworksTo view this data please register and login.

A program for managing collections of full spectrum recordings of bats.v6.2.6660 incorporates the import and export of collections of pictures in the image compare window.v6.2.6661 fixes some bugs and speed issues in 6660.v6.2.6680 tries to fix some database updating problems and adds additional debugging in this area.v7.0.6760 - Major improvements and changes.First define the additional shortvut key in Audacity - CTRL-SHIFT-M=Open menu in focussed track. New item in 'View' menu- Analyse and Import, will open a folder of .wav files and sequentially open them in Audacity. When annotated and the label file saved and Audacity closed the next file will be opened. If the label file is not saved then the process stops and will resume on the next invocation of Analyse and Import on that folder. As each file is opened the label track wil be automatically created and named.and the view ill zoom to the first 5 seconds of the .wav track.7.0.6764 also includes a new report format which (for one or more sessions) gives number of minutes in each ten minute window throughout the day in which a species of bat was detected. Rows are given for each species in the recordings. In Excel looks good as a bar chart or a radar chart.7.06789 hopefully fixes the problems when trying to update a database that caused the program to crash on startup if the database did not contain the more recent Version table.7.0.6799 cosmetic changes to use the normal file selection dialog instead of the folder browser dialog, and also when using Analyse and Import, you no longer need to pick a file when selecting the .wav file folder.7.0.6820 Adds session data to all report formats, including pass statistics for all species found in that session.7.0.6844 Adds the ability to add, save, adjust and include in exported images, Fiducial lines. Lines can be added, deleted or adjusted in the image comparison window and are saved to the database when the window is closed. For exported images the lines are permanently overlaid on the image and are no longer adjustable.7.0.6847 Makes slight improvements to the aspect ratio of images in the comparison window and when images are exported the fiducial lines are only included if the FIDS button is deptessed.7.0.6850 Fixes an occasional bug when saving images through Analyse and Import - using filenames in the caption has priority over bat's names. Also improvements in file handling when changing databases - now attempts to recognise if a db is the right type.7.0.6858 Makes some improvements to image handling, including a modification to the database structure to allow long descriptions for images (previously description+caption had to be less than 250 chars) and the ability to copy images within the application (but not to external applications). A single image may now be used simultaneously as a bat image, a call image or a segment image. Changes to it in one location will be reflected in all the other locations. On deletion the link is removed and if there are no remaining links for the image then the image itself will be removed from the database.7.0.6859 has some improvements to the image handling system. In the batReference view the COMP button now adds all bat and call images for all selected bats to the comparison window. Double clicking on a bat adds all bat, call and segment images for all the bats selected to the comparison window.7.0.6860 removed the COMP button from the bat reference view. Double-clicking in this view transfers all images of bat, calls and recordings to the comparison window. Double-clicking in the ListByBats view transfers all recording images but not the bat and call images to the comparison window. Exported images for recordings use the recording filename plus the start offset of the segment as a filename, or alternatively the image caption. 7.0.6866 Improvements to the grids and to grid scaling and movement especially for the sonagram grids.7.0.6876 Added the ability to right-click on a labelled segment in the recordings detail list control, to open that recording in Audacity and scroll to the location of that labelled segment. Only one instance of Audacity may be opened at a time or the scrolling does not work. Also made some improvements to the scrolling behaviour of the recording detail window.Version 7.1 makes significant changes to the way in which the recordingSessions list is displayed. Because this list can get quite large and therefore takes a long time to load, it now loads the data in discrete pages.At the top of the RecordingSessions List is a new navigation bar with a set of buttons and two combo-boxes. The rightmost combobox is used to set the number of items that will be loaded and displayed on a page. The selections are currently 10, 25, 50 and 100. Slower machines may find it advantageous to use smaller page sizes in order to speed up load times and reduce the demand for memory and cpu-time.The other combobox allows the selection of a sort field for the session list. Sessions are displayed in columns in a DataGrid which allows columns to be re-sized, moved and sorted. These functions all now only apply to the subset of data that has been loaded as a page. The Combo-box allows you to sort the full set of data in the database before loading the page. Thus if the combobox is set to sort on DATE with a Page size of 10, then only the 10 earliest (or the 10 latest depending on the direction of sorting) sessions in the database will be loaded. The displayed set of sessions can be sorted on the screen by clicking the column headers but this only changes the order on the screen, it does not load any other sessions from the database.The four buttons can be used to load the next or previous pages or to move to the start or end of the complete database collection. The Next or Previous buttons move the selection by 2/3 of the Page Size so that there will always be some visual overlap between pages.The sort combo-box has two entries for each field, one with a suffix of ^ and one with a suffix of v . These sort the database in Ascending or Descending order. Selecting a sort field will update the display and sort the display entries on the same field, but the sort direction of the displayed items will be whatever was last used. Clicking the column header will change the direction of sort for the displayed items.v7.1.6885 Updates the database to DB version 6.2 by the addition of two link tables between bats and recordings and between bats and sessions. These tables enable much faster access to bat specific data. Also various improvements to improve the speed of loading data when switching to List By Bats view, especially with very large databases.v7.1.6891 Further performance improvements in loading ListByBats and in loading imagesv7.1.6901 Has the ability to perform screen grabs of images without needing an external screen grabber program. Shift-Click on the 'PASTE' button and drag and resize the semi-transparent window to select a screen area, right click in the window to capture that portion of the screen. For details refer to Import/Import Picturesv7.1.6913 Fixed some scaling issues on fiducial lines in the comparison windowv7.1.6915 Bugfix for adjusting fiducial lines - 7.1.6913 removedv7.1.6941 Improvements and adjustments to grid and fiducial line handlingv7.1.6951 Fixes some problems with the Search dialogv7.2.6970 Introduces the ability to replay segments at reduced speed or in heterodyne 'bat detector' mode.v7.2.6971 When opening a recording or segment in Audacity the corresponding .txt file will be opened as a label track. NB this only works if there is only a single copy of Audacity open - subsequent calls with Audacity still open do not open the label track.v7.2.6978 Improvements to Heterodyne playback to use pure sinewave.7.2.6984 Bug fixes and mods to image handling - image captions can now have a region appended in seconds after the file name.---BRM-Aud-Setup_v7_2_7000.exeThis version includes its only private copy of Audacity 2.3.0 portable, which will be placed in the same folder as BRM and has its own pre-configured configuration file appropriate for use with BRM. This will not interfere with any existing installation of Audacity but provides all the Audacity features required by BRM with no further action by the user. BRM will use this version to display .wav files.v7.2.7000 also includes a new report format which is tailored to provide data for the Hertfordshire Mammals, Amphibians and Reptiles survey. It also displays the GPS co-ordinates for the Recording Session as an OS Grid Reference as well as latitude and longitude.v7.2.7010 Speed improvements and bug-fixes to opening and running Audacity through BRM. Audacity portable is now located in C:\audacity-win-portable instead of under the BRM program folder.v7.2.7012 Fixed some bugs in Report generation when producing he Frequency Table. Enabled the AddTag button in the BatReference pane.v7.2.7021 Upgrades the Audacity component to version 2.3.1 and a few minor bug fixes.

A high resolution bathymetric grid of the nearshore area at Casey station, Antarctica was produced by Geoscience Australia by combining data from two multibeam hydrographic surveys:

1) A survey conducted by the Royal Australian Navy in 2013/14. Refer to the metadata record 'Hydrographic survey HI545 by the RAN Australian Hydrographic Service at Casey, December 2013 to January 2014' with ID HI545_hydrographic_survey.

2) A survey conducted by Geoscience Australia and the Royal Australian Navy in 2014/15.

Refer to the metadata record 'Hydrographic survey HI560 by the RAN Australian Hydrographic Service at Casey, December 2014 to February 2015' with ID HI560_hydrographic_survey and the metadata record 'Seafloor Mapping Survey, Windmill Islands and Casey region, Antarctica, December 2014 - February 2015' with ID AAS_3326_seafloor_mapping_casey_2014_15.

The grid has a cell size of one metre and is stored in a UTM Zone 49S projection, based on WGS84.

Further information is available from the Geoscience Australia website (see a Related URL).