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

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

• 3.173 Annotated Raw Images
  
  • 30 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)
  • 245.962 Bounding Box Annotations
  • 39.955 Rotation Annotations
  • 1.339 Mirror Annotations
  • 84.431 Text String Annotations (equals 93.41% completeness)
    
    • 286.467 Text Characters
    • 98 Character Types (Upper/Lower Case Latin, Numbers, Special Characters)
• 284 Binary Segmentation Maps
  
  • Strokes vs. Background
  • Accompanying Polygon Annotation Files
  • 21.186 Polygon Annotations
• 59 Object Classes
• Scripts for Data Loading, Statistics, Consistency Check and Training Preparation

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 "SPD LTDS Appendix 9 System Schematics" dataset 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).For additional information on column definitions, please click on the Dataset schema link below.Note: A fully formatted copy of this appendix can be downloaded from the Export tab under Alternative exports.Download dataset metadata (JSON)If you wish to provide feedback at a dataset or row level, please click on the “Feedback” tab above. Data Triage:As 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.

"SPM LTDS Appendix 9 System Schematics": Figures providing 33kV connectivity are provided in Appendix 9: System Schematics (Table 9).Click here to access our full Long Term Development Statements for both SPD & SPM. For additional information on column definitions, please click on the Dataset schema link below.Note: A fully formatted copy of this appendix can be downloaded from the Export tab under Alternative exports.Download dataset metadata (JSON)If you wish to provide feedback at a dataset or row level, please click on the “Feedback” tab above. Data Triage:As 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.

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

The Science and Technology Resource Identification Service Platform (https://www.cstr.cn) is abbreviated as the CSTR Identification Platform. This image is based on the CSTR Identification Platform's logo source file and uses a large model to generate an AI-generated content (AIGC) version of the CSTR Identification diagram. It is intended for disseminating the CSTR Identification Platform. For this image, the large model used is Tongyi Wanxiang, version 2.1 Professional. The prompt used for generation is attached. This image employs the CSTR AIGC Identifier Tool. According to the mandatory national standard "GB 45438-2025 Cybersecurity technology—Labeling method for content generated by artificial intelligence," and corresponds to the relevant administrative regulations, the CSTR AIGC Identifier Tool generates and embeds implicit label into the image. It also has functions for numbering, encoding, embedding, extracting, and verifying implicit label, providing support for the compliant, open sharing, and dissemination of AIGC science and technology resource.

Figure2. Schematic diagram-The implementation code of Figure 2 corresponds to each sub step

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

• 2.208 Raw Images
  • 23 Drafters
  • 12 Circuits per Drafter
  • 2 Drawings per Circuit
  • 4 Photos per Drawing
  • 185.815 Bounding Box Annotations
  • 17.233 Rotation Annotations
  • 38.910 Text String Annotations
• 246 Binary Segmentation Maps
  • Strokes vs. Background
  • Accompanying Polygon Annotation Files
  • 18.344 Polygon Annotations
• 58 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

• 2.304 Raw Images
  • 24 Drafters
  • 12 Circuits per Drafter
  • 2 Drawings per Circuit
  • 4 Photos per Drawing
  • 195.321 Bounding Box Annotations
  • 26.020 Rotation Annotations
  • 59.118 Text String Annotations
• 257 Binary Segmentation Maps
  • Strokes vs. Background
  • Accompanying Polygon Annotation Files
  • 19.425 Polygon Annotations
• 59 Object Classes
• Scripts for Data Loading, Statistics, Consistency Check and Training Preparation

This dataset is in support of my research paper - Short Circuit Analysis of 666 Wh Li-Ion NMC Faults and datasets can be copied to submit in fire cause investigation reports or thesis. The simulation is run for 20 hours (72000 seconds) of simulation time for each fault of 100 faults. PrePrint : (Make sure you have read Caution.)

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

This data set contains Supplemental Official OCS Block Diagram (SOBD) images in Adobe pdf format for areas within the BOEM Atlantic Region. Each SOBD describes a single block within an Official Protraction Diagram (OPD) and shows the lines (Submerged Lands Act, Limit of '8(g) Zone', maritime boundaries and/or marine sanctuaries) which occur within that block and divide it into different areas. The SOBD contains additional coordinates and area calculations for an individual block. These data are scanned images of the official paper SOBD's produced by the BOEM. Note that not all OPDs have boundaries cutting through them, so not all OPDs will have SOBDs generated for them. All current leasing activities will be done using the most current SOBDs. Historical (outdated) SOBDs can be obtained by contacting the Mapping and Boundary Branch. Further information on the historic development of OPD's can be found in OCS Report BOEM 99-0006: Boundary Development on the Outer Continental Shelf: https://www.boem.gov/uploadedFiles/BOEM/Oil_and_Gas_Energy_Program/Mapping_and_Data/99-0006.pdf Also see the metadata for each of the individual GIS files used to create these SOBDs. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

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 data set contains Composite Block Diagram (CBD) images in Adobe pdf format for areas within the BOEM Alaska Region. Each CBD describes a single block within an Official Protraction Diagram (OPD) and shows the lines (Submerged Lands Act, Limit of "8(g) Zone", maritime boundaries and/or marine sanctuaries), NAD 27 leases, and NAD 27 boundaries which occur within that block and divide it into different areas. The CBD contains additional coordinates and area calculations for an individual block. These data are scanned images of the official paper CBDs produced by the BOEM.

China Import: Machine for Producing Flat Panel Monitor: Other Device for Project the Circuit Diagram on Photo data was reported at 61,215.063 USD th in Mar 2025. This records an increase from the previous number of 55,195.081 USD th for Feb 2025. China Import: Machine for Producing Flat Panel Monitor: Other Device for Project the Circuit Diagram on Photo data is updated monthly, averaging 31,776.106 USD th from Jan 2007 (Median) to Mar 2025, with 219 observations. The data reached an all-time high of 182,870.452 USD th in May 2018 and a record low of 0.000 USD th in Feb 2012. China Import: Machine for Producing Flat Panel Monitor: Other Device for Project the Circuit Diagram on Photo data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s Electronic Sector – Table CN.RFB: Electronic Import.

description: This data set contains Supplemental Official OCS Block Diagram (SOBD) images in Adobe pdf format for areas within the BOEM Pacific Region. Each SOBD describes a single block within an Official Protraction Diagram (OPD) and shows the lines (Submerged Lands Act, Limit of '8(g) Zone', maritime boundaries and/or marine sanctuaries) which occur within that block and divide it into different areas. The SOBD contains additional coordinates and area calculations for an individual block. These data are scanned images of the official paper SOBD's produced by the BOEM. Note that not all OPDs have boundaries cutting through them, so not all OPDs will have SOBDs generated for them. All current leasing activities will be done using the most current SOBDs. Historical (outdated) SOBDs can be obtained by contacting the Mapping and Boundary Branch. Further information on the historic development of OPD's can be found in OCS Report BOEM 99-0006: Boundary Development on the Outer Continental Shelf: https://www.boem.gov/uploadedFiles/BOEM/Oil_and_Gas_Energy_Program/Mapping_and_Data/99-0006.pdf Also see the metadata for each of the individual GIS files used to create these SOBDs. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.; abstract: This data set contains Supplemental Official OCS Block Diagram (SOBD) images in Adobe pdf format for areas within the BOEM Pacific Region. Each SOBD describes a single block within an Official Protraction Diagram (OPD) and shows the lines (Submerged Lands Act, Limit of '8(g) Zone', maritime boundaries and/or marine sanctuaries) which occur within that block and divide it into different areas. The SOBD contains additional coordinates and area calculations for an individual block. These data are scanned images of the official paper SOBD's produced by the BOEM. Note that not all OPDs have boundaries cutting through them, so not all OPDs will have SOBDs generated for them. All current leasing activities will be done using the most current SOBDs. Historical (outdated) SOBDs can be obtained by contacting the Mapping and Boundary Branch. Further information on the historic development of OPD's can be found in OCS Report BOEM 99-0006: Boundary Development on the Outer Continental Shelf: https://www.boem.gov/uploadedFiles/BOEM/Oil_and_Gas_Energy_Program/Mapping_and_Data/99-0006.pdf Also see the metadata for each of the individual GIS files used to create these SOBDs. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

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 across our Eastern Power Networks (EPN) license area. The data is 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 Statement Creating 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 Networks

SPIQA Dataset Card Dataset Details Dataset Name: SPIQA (Scientific Paper Image Question Answering)

Paper: SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers

Github: SPIQA eval and metrics code repo

Dataset Summary: SPIQA is a large-scale and challenging QA dataset focused on figures, tables, and text paragraphs from scientific research papers in various computer science domains. The figures cover a wide variety of plots, charts, schematic diagrams, result visualization etc. The dataset is the result of a meticulous curation process, leveraging the breadth of expertise and ability of multimodal large language models (MLLMs) to understand figures. We employ both automatic and manual curation to ensure the highest level of quality and reliability. SPIQA consists of more than 270K questions divided into training, validation, and three different evaluation splits. The purpose of the dataset is to evaluate the ability of Large Multimodal Models to comprehend complex figures and tables with the textual paragraphs of scientific papers.

This Data Card describes the structure of the SPIQA dataset, divided into training, validation, and three different evaluation splits. The test-B and test-C splits are filtered from the QASA and QASPER datasets and contain human-written QAs. We collect all scientific papers published at top computer science conferences between 2018 and 2023 from arXiv.

If you have any comments or questions, reach out to Shraman Pramanick or Subhashini Venugopalan.

Supported Tasks: - Direct QA with figures and tables - Direct QA with full paper - CoT QA (retrieval of helpful figures, tables; then answering)

Language: English

Release Date: SPIQA is released in June 2024.

Data Splits The statistics of different splits of SPIQA is shown below.

Dataset Structure The contents of this dataset card are structured as follows:

bash SPIQA ├── SPIQA_train_val_test-A_extracted_paragraphs.zip ├── Extracted textual paragraphs from the papers in SPIQA train, val and test-A splits ├── SPIQA_train_val_test-A_raw_tex.zip └── The raw tex files from the papers in SPIQA train, val and test-A splits. These files are not required to reproduce our results; we open-source them for future research. ├── train_val ├── SPIQA_train_val_Images.zip └── Full resolution figures and tables from the papers in SPIQA train, val splits ├── SPIQA_train.json └── SPIQA train metadata ├── SPIQA_val.json └── SPIQA val metadata ├── test-A ├── SPIQA_testA_Images.zip └── Full resolution figures and tables from the papers in SPIQA test-A split ├── SPIQA_testA_Images_224px.zip └── 224px figures and tables from the papers in SPIQA test-A split ├── SPIQA_testA.json └── SPIQA test-A metadata ├── test-B ├── SPIQA_testB_Images.zip └── Full resolution figures and tables from the papers in SPIQA test-B split ├── SPIQA_testB_Images_224px.zip └── 224px figures and tables from the papers in SPIQA test-B split ├── SPIQA_testB.json └── SPIQA test-B metadata ├── test-C ├── SPIQA_testC_Images.zip └── Full resolution figures and tables from the papers in SPIQA test-C split ├── SPIQA_testC_Images_224px.zip └── 224px figures and tables from the papers in SPIQA test-C split ├── SPIQA_testC.json └── SPIQA test-C metadata

The testA_data_viewer.json file is only for viewing a portion of the data on HuggingFace viewer to get a quick sense of the metadata.

Metadata Structure The metadata for every split is provided as dictionary where the keys are arXiv IDs of the papers. The primary contents of each dictionary item are:

arXiv ID Semantic scholar ID (for test-B) Figures and tables Name of the png file Caption Content type (figure or table) Figure type (schematic, plot, photo (visualization), others)

QAs Question, answer and rationale Reference figures and tables Textual evidence (for test-B and test-C)

Abstract and full paper text (for test-B and test-C; full paper for other splits are provided as a zip)

Dataset Use and Starter Snippets Downloading the Dataset to Local We recommend the users to download the metadata and images to their local machine.

Download the whole dataset (all splits). bash from huggingface_hub import snapshot_download snapshot_download(repo_id="google/spiqa", repo_type="dataset", local_dir='.') ### Mention the local directory path

Download specific file. bash from huggingface_hub import hf_hub_download hf_hub_download(repo_id="google/spiqa", filename="test-A/SPIQA_testA.json", repo_type="dataset", local_dir='.') ### Mention the local directory path

Questions and Answers from a Specific Paper in test-A bash import json testA_metadata = json.load(open('test-A/SPIQA_testA.json', 'r')) paper_id = '1702.03584v3' print(testA_metadata[paper_id]['qa'])

Questions and Answers from a Specific Paper in test-B bash import json testB_metadata = json.load(open('test-B/SPIQA_testB.json', 'r')) paper_id = '1707.07012' print(testB_metadata[paper_id]['question']) ## Questions print(testB_metadata[paper_id]['composition']) ## Answers

Questions and Answers from a Specific Paper in test-C bash import json testC_metadata = json.load(open('test-C/SPIQA_testC.json', 'r')) paper_id = '1808.08780' print(testC_metadata[paper_id]['question']) ## Questions print(testC_metadata[paper_id]['answer']) ## Answers

Annotation Overview Questions and answers for the SPIQA train, validation, and test-A sets were machine-generated. Additionally, the SPIQA test-A set was manually filtered and curated. Questions in the SPIQA test-B set are collected from the QASA dataset, while those in the SPIQA test-C set are from the QASPER dataset. Answering the questions in all splits requires holistic understanding of figures and tables with related text from the scientific papers.

Personal and Sensitive Information We are not aware of any personal or sensitive information in the dataset.

Licensing Information CC BY 4.0

Citation Information bibtex @article{pramanick2024spiqa, title={SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers}, author={Pramanick, Shraman and Chellappa, Rama and Venugopalan, Subhashini}, journal={NeurIPS}, year={2024} }