This dataset contains hand-drawn electrical circuit diagram images (also referred to as schematics) as well as accompanying annotation and segmentation ground-truth files. It is intended to train (e.g. ANN) models for the purpose of the extraction of electrical graphs from raster graphics.

Content

• 3.269 Raw Images
  • 31 Main Drafters
    • 12 Circuits per Drafter
    • 2 Drawings per Circuit
    • 4 Photos per Drawing
  • Additional Images from Examinations (provided by TU Dresden; drafter_0)
  • Additonal Images from RPTU Kaiserslautern-Landau (drafter_-1)
  • 248.020 Bounding Box Annotations
  • 40.711 Rotation Annotations
  • 1.437 Mirror Annotations
  • 85.417 Text String Annotations (equals 93.74% completeness)
  • 289.850 Individual Characters
  • 98 Character Types (Upper/Lower Case Latin, Numbers, Special Characters)
• 344 Binary Segmentation Maps
  • Strokes vs. Background
  • Accompanying Polygon Annotation Files
  • 25.129 Polygon Annotations
• 59 Object Classes
• Scripts for Data Loading, Statistics, Consistency Check and Training Preparation

CGHD

This dataset contains images of hand-drawn electrical circuit diagrams as well as accompanying annotation and segmentation ground-truth files. It is intended to train (e.g. ANN) models for extracting electrical graphs from raster graphics.

Content

• 3.269 Annotated Raw Images
  
  • 31 Main Drafters
    • 12 Circuits per Drafter
    • 2 Drawings per Circuit
    • 4 Photos per Drawing
  • Additional Circuit Images provided by TU Dresden (from Real-World Examinations, Drafter 0)
  • Additional Circuit Images provided by RPTU Kaiserslautern-Landau (Drafter -1)
  • 248.020 Bounding Box Annotations
  • 40.711 Rotation Annotations
  • 1.437 Mirror Annotations
  • 85.417 Text String Annotations (equals 93.74% completeness)
    
    • 289.850 Text Characters
    • 98 Character Types (Upper/Lower Case Latin, Numbers, Special Characters)
• 320 Binary Segmentation Maps
  
  • Strokes vs. Background
  • Accompanying Polygon Annotation Files
  • 22.929 Polygon Annotations
• 59 Object Classes
• Scripts for Data Loading, Statistics, Consistency Check and Training Preparation

Today, volcanic sulfur emissions into the atmosphere are measured spectroscopically from the ground, air and space. For eruptions prior to the satellite era, two main sulfur proxies are used, the rock and ice core records, as illustrated by Peccia et al. The first approach is based on calculations of the sulfur content of the magma, while the second uses traces of sulfur deposited in ice. Both approaches have their limitations. For glaciochemistry, the volcano responsible for a sulfur anomaly is often unknown and the atmospheric pathway by which the sulfur reached the ice uncertain. The petrologic method relies, too, on uncertain estimates of eruption size and a number of geochemical assumptions that are hard to verify. A deeper knowledge of processes occurring both within magma bodies prior to eruption, and within volcanic plumes in the atmosphere is needed to further our understanding of the impacts of volcanism on climate

The information of all the charts and supplementary charts in the article

The dataset is a collection of hand-drawn basic circuit schematic components. Each class has about 200 hand-drawn symbols. The following schematic components are available: - AC Source. - Ammeter. - Battery. - Capacitor. - Current source. - DC Voltage Source. - Dependent Voltage Source. - Dependent Current Source. - Earth Ground. - Resistor. - Inductor. - Voltmeter. - Diode

This dataset contains images of hand-drawn electrical circuit diagrams as well as accompanying annotation and segmentation ground-truth files. It is intended to train (e.g. ANN) models for extracting electrical graphs from raster graphics. Content: 2.112 Raw Image Files 175.300 Bounding Box Annotations 229 Binary Segmentation Maps with accompanying Polygon Annotations Statistics, Consistency and Segmentation Workflow Scripts

Training datasets for generalized block diagram models of grid-forming (GFM) inverter-based resources (IBR), particularly with solar photovoltaic (PV) sources. Datasets consist of transient data collected from electromagnetic transient (EMT) simulations or laboratory tests of GFM inverters. See https://pecblocks.readthedocs.io/en/latest/ and https://github.com/pnnl/pecblocks/tree/master/data for details.

The "SPM LTDS Appendix 9 System Schematics" data table provides 33kV connectivity, as is in Appendix 9: System Schematics (Table 9).Click here to access our full Long Term Development Statements for both SPD & SPM. The table gives the following information:Details of the schematic sheet for each primary substationFor additional information on column definitions, please click on the Dataset schema link below. DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Long Term Development Statement dataset.>To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page.Download dataset metadata (JSON)

The "SPD LTDS Appendix 9 System Schematics" data table contains the schematic diagrams of the distribution system. The 33kV network is represented, down to the 11kV primary busbars.Click here to access our full Long Term Development Statements for both SP Distribution (SPD) & SP Manweb (SPM).The table gives the following information:Details of the schematic sheet for each primary substationFor additional information on column definitions, please click on the Dataset schema link below. DisclaimerWhilst all reasonable care has been taken in the preparation of this data, SP Energy Networks does not accept any responsibility or liability for the accuracy or completeness of this data, and is not liable for any loss that may be attributed to the use of this data. For the avoidance of doubt, this data should not be used for safety critical purposes without the use of appropriate safety checks and services e.g. LineSearchBeforeUDig etc. Please raise any potential issues with the data which you have received via the feedback form available at the Feedback tab above (must be logged in to see this). Data TriageAs part of our commitment to enhancing the transparency, and accessibility of the data we share, we publish the results of our Data Triage process.>Our Data Triage documentation includes our Risk Assessments; detailing any controls we have implemented to prevent exposure of sensitive information. Click here to access the Data Triage documentation for the Long Term Development Statement dataset.To access our full suite of Data Triage documentation, visit the SP Energy Networks Data & Information page.Download dataset metadata (JSON)

Integrated III/V-on-CMOS: Electronic Circuits and Systems Designs --- This dataset contains the schematic design of a custom controller circuit for output power stages. An InGaAs HEMT process technology is used to design the circuit. The schematic data should be accessed with the appropriate software, such as Cadence Schematic.

Dataset Description for FigshareThis dataset supports the findings of the research titled:"Attention-Based Framework for Automated Symbol Recognition and Wiring Design in Electrical Diagrams"Authors: Ikenna Ekeke, Carlos Francisco Moreno-García, Eyad ElyanAffiliation: Robert Gordon University, Aberdeen, Scotland, UKDate: April 2025OverviewThe research presents an end-to-end deep learning framework combining YOLOv8 object detection with attention mechanisms to improve symbol recognition in electrical diagrams, followed by a graph-based wiring algorithm that automates wire routing between detected symbols. The system is tested across proprietary and public datasets, including:CGHD (Circuit Graph Hand-drawn Diagrams)DCD (Digital Circuit Diagrams)Datasets IncludedThis data bundle includes the primary data used to generate figures and tables within the manuscript:Model performance metrics across different attention modules (Table 1 - Table 3).Class distribution tables (used in Figures 11a, 11b, 12a, 12b).Class-wise accuracy table (Table 4).Results of statistical analysis (Table 5, Duncan’s test).Validation Results on CGHD and DCD_Datasets (Table 6).Wiring algorithm evaluation metrics (Table 7).Each table is provided in CSV format that maps the data file to the corresponding figure or table in the paper.Use CasesThis dataset is useful for:Benchmarking attention-based models for electrical symbol recognition.Studying the impact of class imbalance on detection accuracy.Reproducing automated wiring design evaluations using pathfinding algorithms.

This dataset is updated irregularly. When there are changes to the routes, relevant content and diagrams will be corrected in a timely manner. (Leapfrog bus)

Carbon fiber reinforced aluminum composites with ordered architectures of shear-induced aligned carbon fibers were fabricated by 3D printing. The microstructures of the printed and sintered samples and mechanical prop erties of the composites were investigated. Carbon fibers and aluminum powder were bonded together with resin. The spatial arrangement of the carbon fibers was fixed in the aluminum matrix by shear-induced alignment in the 3Dprinting process. As a result, the elongation of the composites with a parallel arrangement of aligned fibers and the impact toughness of the composites with an orthogonal arrangement were 0.82% and 0.41 J/cm2, respec tively, about 0.4 and 0.8 times higher than that of the random arrangement

OverviewThis dataset supports the findings of the research titled “Reimagining Electrical Diagrams in Construction: Automated Symbol Detection and Wiring Design and Generation with Deep Learning.”The study presents a fully automated framework that digitises complex electrical diagrams through two main components:Symbol Recognition: A YOLOv8-based deep learning model trained to recognise 30 electrical symbol classes in industrial diagrams.Automated Wiring Design: A modified A* pathfinding algorithm that generates orthogonal wiring between recognised symbols, reducing total wire length by 44% compared to traditional methods.The dataset includes preprocessing experiments such as data augmentation (AUG) and low-intensity sampling (LINS) to improve detection performance and mitigate class imbalance.Datasets IncludedThis data bundle contains the primary files used to produce figures and tables within the manuscript, including:Symbol distribution and augmentation statistics (Table 1).Model performance metrics across preprocessing experiments (YOLOv7 and YOLOv8) (Tables 3–5).Wiring algorithm evaluation results comparing the multiline plotter and modified A* methods (Table 6).Hardware and software configuration details (Table 2).Examples of detected symbols, wiring paths, and recognition visualisations (Figures 1–7).Each table is provided in CSV format, mapping data files directly to their corresponding figure or table in the paper.Use CasesThis dataset can be used for:Benchmarking YOLO-based models for electrical symbol recognition in high-resolution engineering diagrams.Studying the impact of class imbalance, augmentation, and preprocessing techniques on detection accuracy.Evaluating automated wiring algorithms using modified A* search for layout optimisation.Reproducing experimental setups for symbol recognition and routing in industrial diagram analysis.

The data submite to the GCA. Fig. 1 is an assembly schematic diagram not involving data Fig. 2 is an SEM image not involving data Fig. 3 data refer to the "data in Figure 3" Fig. 4 data refer to the "data in Figure 4" Fig. S1 data refer to the "data in Figure S1" Fig. S2 data refer to the "data in Figure S2" Fig. S3 data refer to the "data in Figure S3" Fig. S4 data refer to the "data in Figure S4" Fig. S5 data refer to the "data in Figure S5" Fig. S6 data refer to the "data in Figure S6" Fig. S7 data refer to the "data in Figure S7" Fig. S8 data refer to the "data in Figure S8" Fig. S9 data refer to the "data in Figure S9" Fig. S10 data refer to the "data in Figure S10" Fig. S11 refer to is a schematic model of adjacent mineral diffusion, which does not involve quantitative representation and therefore does not involve data Fig. S12 data refer to the "data in Figure S12"

Number of participants (out of 8) who consistently reported (on schematic diagrams of the target display) the correct direction (Dir) of the target rotation for all 5 targets, for Task A and B, along with the mean measured size of the rotation from the diagrams for all participants who completed the task (°) and between-target SDs (°) across the 5 targets (between-subject SDs).

This dataset contains key characteristics about the data described in the Data Descriptor Building schematic of Vienna in the late 1920s. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

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.

RDS data file containing data of associations tested for interaction effects between exposure and timepoint of COVID-19 positivity to generate scatter plot Figure 4 (also using first time point data seet Figure 2 data).This figure is also part of the Figure 1 schematic diagram. Exposure labels are UK Biobank Field IDs (e.g., https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=102).

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.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. 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 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.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits 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: Sharepoint

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 Approach The 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 Statement The 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 Information Definitions 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 Networks To view this data please register and login.