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722 results for “use case”
Supplementary Data - "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio"
<p>Supplementary data for the manuscript entitled "Analysis of Venusian Wrinkle Ridge Morphometry Using Stereo-Derived Topography: A Case Study from Southern Eistla Regio".</p> <p>Includes data for topographic profiles of wrinkle ridges ("wrinkleridge_profiledata.xlsx"), COULOMB model inputs ("COULOMB_modelinputs.xlsx") and outputs ("COULOMB_modeloutputs.xlsx"), and GIS shapefile data for mapped wrinkle ridges (files labelled "allwrinkleridges" and "studiedwrinkleridges"), topographic profile lines (files labelled "topographicprofilelines"), and the regional profile (files labelled "regionalprofile"). </p>
Profile data of the ADMIRE project use case applications
<p>HPC application traces from the use cases in the ADMIRE project. They were collected with TAU monitoring tools.</p> <p>Applications traced are described at: https://www.admire-eurohpc.eu/UseCases/</p> <p>- <em>Application 1: <a href="http://meteo.uniparthenope.it/">Monitoring and Modelling Marine, weather and Air quality</a> </em></p> <p>- <em>Application 2: Car-Parrinello molecular dynamic simulation of large molecules and small proteins </em></p> <p><em>- Application 3: Simulation of large scale turbulent flow.</em></p> <p><em>- Application 4: Continental-scale land cover mapping with scalable and automatic deep learning frameworks.</em></p> <p><em>- Application 5: Super-resolution imaging using Opera microscopy and SRRF/ImageJ software. </em></p> <p><em>- Application 6: Software Heritage Management & Indexing.</em></p> <p> </p> <p> </p> <p> </p>
Data Set of Industrial Metaverse Use Cases
<p>Data Set of Industrial Metaverse Use Cases</p> <p>Potential of the Industrial Metaverse – A Taxonomic Approach<br>IFIP 21st International Conference on Product Lifecycle Management (2024)</p> <p>This data set comprises the following components:</p> <ul> <li>Use Cases & Classification</li> <li>Dimensions & Characteristics</li> <li>Review Documentation</li> </ul>
Dataset for KIOS CoE Sandboxing use-case SUC4 corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme (IEC 61850 GOOSE)
<p><span>The datasets reflect on two main scenarios (S1-S2) related to SUC4 - corresponding to cyber attacks affecting the Coordinated Overcurrent Protection Scheme. </span><span>The first scenario explores the response of the coordinated overcurrent protection when circuit breakers (CBs) are healthy, under normal operation, i.e., SUC4/S1(without attack), and the under a FDI cyberattack on IEC 61850 - GOOSE communication protocol, i.e., SUC4/S1(with FDI attack). </span>Similarly, the second scenario investigates the response of the coordinated overcurrent protection when there a mechanical failure in the CB of the downstream feeder, under normal operation, i.e., SUC4/S2(without attack), and the under a message suppresion (MS) cyber-attack on GOOSE protocol, i.e., SUC4/S2(with MS attack). Details regarding the datasets captured during the execution of each scenario (with and without attacks), including electrical measurements and network traffic, are briefly rsummarized below, while the full details are provided in the supporting documents.</p> <ul> <li><span><strong>SUC4/S1(without attack) datasets/Normal operation (without cyber-attack on GOOSE) when CBs are healthy </strong>: This dataset is related to the operation of the sandboxing use case SUC4 described in this document, which examines operation of the protection scheme in a substation using overcurrent protective relays (IEDs) in the sandboxing environment, that communicate with each other via IEC6180/GOOSE protocol. Specifically, this dataset corresponds to the first scenario (S1) of SUC4, without any attack. More details about the scenario related to this dataset can be found in Section 1.3.1 of the SUC4 supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.</span></li> <li><span><strong>SUC4/S1(with FDI attack) datasets/FDI cyber-attack on GOOSE signals when CBs are healthy</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(without attack) datasets/ Normal operation (without attack on GOOSE) when CB presents a failure</strong>: This dataset corresponds to the first scenario (S1) of SUC4, where an FDI cyber-attack is conducted in the local network by an attacker model, in order to inject fake messages to deceive an IED to unnecessarily trip its CB during normal grid conditions (without a shortcircuit event) and cause a regional blackout. More details about the scenario related to this dataset can be found in Section 1.3.1 of the supporting document. The dataset includes electrical measurements of the upstream and downstream feeders of the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of<br>the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> <li><span><strong>SUC4/S2(with MS attack) datasets/MS cyber-attack on GOOSE signals when CB presents a failure</strong>: This dataset corresponds to the second scenario (S2) of SUC4, where an MS cyber-attack is conducted in the local network in order prevent critical benign messages, such inter-trip messages requesting backup protection, to reach their destination (back-up IED) when a CB failure occurs during a short-circuit event. As a result, the duration of a short-circuit is prolonged or the protection scheme is not able to clear the short-circuit event, which can cause catastrophic failures to power system. More details about the scenario related to<br>this dataset can be found in Section 1.3.2 of the support document. The dataset includes electrical measurements of the upstream and downstream feeders of<br>the substation, the status (stNum) and sequence (sqNum) numbers, along with the binary values in “alldata” field of the GOOSE messages of IED1 and IED2, as well as the status of the CB1 and CB2. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded with a 0.5-millisecond time resolution by specific blocks in RT-Lab environment of the real time simulator. In addition, network traffic data as Packer CAPture files (.pcapng) are included in this database.<br></span></li> </ul>
Dataset for KIOS CoE Sandboxing use-case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids
<p>These datasets <span> illustrate two primary scenarios (S1-S2) concerning the operation of the sandboxing use case SUC5 corresponding to cyber attacks affecting the control of active distribution grids and microgrids. These scenarios examine the functioning of an active distribution grid and microgrid system, along with the effects of certain cyber-attacks in this context. The demonstration of each scenario is detailed in selected time-series plots which were described in detail in Section </span><span>1.3 of the supporting document of SUC5 (</span><span>accompanied by an in-depth analysis of the processes and an impact assessment). A</span><span>ll data captured during the execution of each scenario was collected, including electrical measurements, reference and set-point signals. </span></p> <ul> <li><span><span><strong>SUC5/S1 datasets/<span>MITM with FDI</span> cyber-attack</strong><span><strong> in an active distribution grid (grid-connected)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) of the KIOS CoE Sandboxing environment for cyber-physical analysis of EPES, which examines the operation of an active distribution grid, when the distribution grid is interconnected with the main grid. Specifically, this dataset corresponds to the first scenario (S1) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the active power set-point allocated to BSS inverter controller from the secondary controller. More details about the scenario related to this dataset can be found in Section 1.3 of the supporting document. The dataset includes electrical measurements of the active power generated by the BSS inverter (connected at bus 2), and the active power set-point before and after the attack. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> <li><span><span><span><strong>SUC5/S2 datasets/MITM with FDI cyber-attack in a microgrid (islanding mode)</strong>: This dataset is related to the operation of the fifth sandboxing use case (SUC5) which investigates the operation of a microgrid during islanding mode. This dataset corresponds to the second scenario (S2) of SUC5, where a MITM with FDI cyber-attack is virtually conducted within the sandboxing environment to introduce an offset deviation to the frequency reference signal, exchanged between the higher-level controller (tertiary controller) and the microgrid local controller (secondary V-f controller). More details about the scenario related to this dataset can be found in Section 1.3 of this supporting document. The dataset includes electrical measurements of the microgrid frequency, the reference frequency value generated by the tertiary controller, as well as the attacked frequency reference value. The dataset is provided in the form of time-series measurements available as MATLAB (.mat) and CSV (.csv) files. The measurements were recorded from the real time simulator using the “OpWrite” block of the RT-LAB, with 1-millisecond time resolution. <br></span></span></span></li> </ul>
Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study
<p><strong>This repository contains raw data relating to: </strong>Continuous monitoring of patient mobility for 18 months using inertial sensors following traumatic knee injury: a case study Mueller A., Hoefling H., Nuritdinow T., et al. DOI: 10.1159/000490919</p> <p><strong>Metadata and processed data derived from the raw data deposited here is available here:</strong> https://github.com/Novartis/mueller_et_al_2018</p> <p><strong>Article Abstract</strong></p> <p>Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with > 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.</p>
Use Case 1 Data set
<p>Effect of nanofiller on the stiffness of a Carbon Fiber Reinforced Thermoplastic (CFRP).<br> The effect of different volume fractions (0%, 2%, 5% and 12%) of Multiwall Carbon Nanotubes (MWCNT) are computed for PEEK reinforced Carbon Fiber UD. Two different Carbon Fiber(CF) volume fractions are considered (40% and 60%). </p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions (dataset)
<p>This repository contains data (features) necessary to run STXGB model and accompanies the paper titled "Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions".</p> <p> </p> <p>STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
H2020 Platone German Demonstrator - Use Case Setting Data
<p>This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). The dataset contain information that have been set for Use Cases (UCs) parameterization during the project phase. UC have been parameterized along a grafical user interface (GUI) named "Use Case Selector". Each parameterized UC triggered has been logged in the dataset.</p> <p><strong>Background - Field Test Setup</strong></p> <p>The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single point of common coupling (PCC). i.e., a secondary substation that includes a transformer with senors on the LV busbar, to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity. </p> <p><strong>Definition of data:</strong></p> <p><strong>RequID</strong> - Request ID - Identifier for each triggered UC</p> <p><strong>Alert</strong> - Indicates, whether UC has been executed successfull ( " "and " true" indacates successfull implementation by Energy Management System (EMS); "false" indicates that UC has not been implemented by EMS)</p> <p><strong>Submission -</strong> timestamp of UC submission<strong> </strong></p> <p><strong>Note -</strong> Annotations entered by UseCase operator</p> <p><strong>Priority -</strong> Defines UC priority set by operator (priority: 1 - high , 2 - medium, 3 - low, 4 - very low) (only relevant for UC 2)</p> <p><strong>Status - </strong>Indicates the status of the UC (closed - UC has been executed, cancelled, UC has been has been canceled before or during application)</p> <p><strong>Start</strong> - Point of time set for the beginning of UC</p> <p><strong>End</strong> - Point of time set for the end of UC</p> <p><strong>Type</strong>- Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4)</p> <p><strong>Subtype</strong> - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End;</p> <p><strong>bulkStart</strong> - Point of time of start of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulkEnd</strong> - Point of time of end of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulk Energy</strong> - Amount of energy triggered to be imported or exported as bulk (only relevant for UC 3 and 4)</p> <p><strong>Final_SOF </strong>- State of Charge (SOC) of CBES that should be achieved at end of UC (End)</p> <p><strong>FlexDemand </strong>- Requested power exchange that should be achieved at MV/LV PCC (Only relevant for UC 2)</p> <p><strong>Ptcb - </strong>Measured CBES charging power at poin of time of UC submission </p> <p><strong>Ptei </strong>- Measured power exchange at PCC at point of time of UC submission </p> <p><strong>SoC </strong>- State of Charge of CBES at point of time of UC submission </p> <p><strong>SoE</strong> - State of Energyof CBES at point of time of UC submission </p> <p><strong>ActiveSet </strong>- State of Energyof CBES at point of time of UC submission </p> <p><strong>maxSoC </strong>- Maximum SoC set for CBES at point of time of UC submission </p> <p><strong>minSoc - </strong>MinimumSoC set for CBES at point of time of UC submission </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Market 3rd quarter 2022
<p>areti_market_flexibility_TSO_requestes</p> <p>areti_market_flexibility_DSO_requestes</p> <p>areti_market_flexibility_Aggregator_bids</p> <p>areti_market_flexibility_settlement</p> <p>areti_market_flexibility_outcomes</p> <p>- TSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>Grid Area</li> </ul> <p>- DSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes, Grid Area</li> </ul> <p>- Aggregator bids:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>PoDs List</li> </ul> <p>- Settlement data:</p> <ul> <li>Pod</li> <li>Requested Active Power</li> <li>Measured Active Power</li> <li>Requested Reactive Power</li> <li>Measured Reactive Power</li> </ul> <p>- Market Outcomes:</p> <ul> <li>Market Outcome Id</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> </ul> <p>Other than TSO flexibility requests, to test the demo, other data could be simulated. In this case, it will be indicated in the metadata documentation.</p> <p>(Useful link to consult Italian UC: <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a>; <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a>, <a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Market 2nd quarter 2022
<p>areti_market_flexibility_TSO_requestes</p> <p>areti_market_flexibility_DSO_requestes</p> <p>areti_market_flexibility_Aggregator_bids</p> <p>areti_market_flexibility_settlement</p> <p>areti_market_flexibility_outcomes</p> <p>- TSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>Grid Area</li> </ul> <p>- DSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes, Grid Area</li> </ul> <p>- Aggregator bids:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>PoDs List</li> </ul> <p>- Settlement data:</p> <ul> <li>Pod</li> <li>Requested Active Power</li> <li>Measured Active Power</li> <li>Requested Reactive Power</li> <li>Measured Reactive Power</li> </ul> <p>- Market Outcomes:</p> <ul> <li>Market Outcome Id</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> </ul> <p>Other than TSO flexibility requests, to test the demo, other data could be simulated. In this case, it will be indicated in the metadata documentation.</p> <p>(Useful link to consult Italian UC: <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a>; <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a>, <a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Market 1st quarter 2022
<p>areti_market_flexibility_TSO_requestes</p> <p>areti_market_flexibility_DSO_requestes</p> <p>areti_market_flexibility_Aggregator_bids</p> <p>areti_market_flexibility_settlement</p> <p>areti_market_flexibility_outcomes</p> <p>- TSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>Grid Area</li> </ul> <p>- DSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes, Grid Area</li> </ul> <p>- Aggregator bids:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>PoDs List</li> </ul> <p>- Settlement data:</p> <ul> <li>Pod</li> <li>Requested Active Power</li> <li>Measured Active Power</li> <li>Requested Reactive Power</li> <li>Measured Reactive Power</li> </ul> <p>- Market Outcomes:</p> <ul> <li>Market Outcome Id</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> </ul> <p>Other than TSO flexibility requests, to test the demo, other data could be simulated. In this case, it will be indicated in the metadata documentation.</p> <p>(Useful link to consult Italian UC: <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a>; <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a>, <a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled: Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study Iliana Chollett1, D. Ross Robertson2 1 Sea Cottage, Louisburgh, Co. Mayo, Ireland 2 Smithsonian Tropical Research Institute, Balboa, Panamá
<p><strong>A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled:</strong></p> <p><strong><em> </em></strong></p> <p><strong><em>Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study</em></strong></p> <p> </p> <p> Iliana Chollett, D. Ross Robertson</p> <p><strong> </strong></p> <p><strong> </strong></p> <p><strong>Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá</strong></p> <p><strong> </strong></p> <p>This set of six databases contains georeferenced location records from six sources as described below.These six sources provided georeferenced records of occurrence of fishes found in the Greater Caribbean study area (6-33<sup>0</sup> N, 57-100<sup>0</sup> W). Each occurrence record consists of a species name and associated latitude and longitude. Databases included in the comparisons made here are from five major online aggregators. Since their content overlaps to some extent, and OBIS, iDigBio and FishNet collaborate with GBIF, their data might be expected to produce similar biogeographic patterns. STRI includes a curated compendium of data from those five aggregators, enriched with data from many additional sources.</p> <p> </p> <p>Only reef-associated fish species were included in the present analysis. These mostly represent demersal species known to occur on hard bottoms (coral, rock and oyster substrata), but also include species living on rubble, sand and vegetated bottoms within and around the immediate fringes of reefs, and pelagic species regularly found on reefs. All exotic and non-resident species and species other than reef-associated fishes were excluded from all databases prior to comparisons. Non-residents were defined as otherwise widespread species only rarely seen in the study area. Shore-fishes, including what are generally regarded as reef fishes, include those found in the waters of continental and insular shelves, i.e. between 0-200m. Reef-fish assemblages dominated by shallow-water taxa extend down to that depth in the study area (Baldwin <em>et al.</em> 2018). We used the shelf edge as a breakpoint and excluded records in areas deeper than 200m, identifying those areas using the General Bathymetric Chart of the Oceans (Kapoor, 1981; GEBCO Compilation Group, 2019).</p> <p> </p> <p>Before the analyses, for all databases, duplicate records were deleted. Subsequently, records in the Pacific or on land were deleted. We used the Global Self-consistent, Hierarchical, High-resolution Geography Database (Wessel & Smith, 1996) to identify these areas. The spatial distribution of species-records in each database is shown in Figure 1 of the publication.</p> <p><strong> </strong></p> <p><strong>Global Biodiversity Information Facility </strong>(GBIF, https://www.gbif.org/): GBIF is an international network and research infrastructure aimed at providing open access to data about all types of life on earth. GBIF works through participant nodes using common standards and open-source tools that enable them to share information. Data from among the 49,000+ datasets hosted by GBIF that were used here range from those on museum specimens collected since the 18th century, to published scientific checklists, to curated local checklists produced by trained science sources such as the Atlantic and Gulf Rapid Assessment Program (https://www.agrra.org/),to geotagged smartphone photos (that act as vouchers allowing verification) shared by amateur and scientific naturalists through iNaturalist (https://www.inaturalist.org/), to unvouchered, unverified and unverifiable observation records from untrained divers such as those contributing to DiveBoard (http://www.diveboard.com). GBIF data are standardized in Darwin Core format. GBIF data were obtained from a polygon of the region of study and subject to taxonomic review and selection after downloading. GBIF data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the GBIF portal, https://www.gbif.org/, on or about 2019-05-19).</p> <p> </p> <p><strong>Ocean Biogeographic Information System</strong> (OBIS, <a href="https://obis.org/">https://obis.org/</a>): OBIS is a global open-access data and information clearing-house on marine biodiversity (OBIS, 2019) that was adopted as a project of the Intergovernmental Oceanographic Data and Information Exchange of the Intergovernmental Commission of UNESCO . Its range of sources is similar to that of GBIF. OBIS hosts data from organizations or programs that join it as one of 13 “nodes”, and harvest the data from the IPT (Integrated Publishing Toolkit), where providers publish their data. The IPT is developed and maintained by the GBIF, and OBIS is a major contributor of marine data to GBIF. Data are standardized in Darwin Core format. OBIS data were obtained for the region of study by downloading data on each family, then retaining only data inside the study area, which were then subject to taxonomic review and selection (accessed through the OBIS portal, https://obis.org/, on or about 2019-05-19).</p> <p> </p> <p><strong>Integrated Digitized Biocollections</strong> (iDigBio, https://portal.idigbio.org/portal/search): iDigBio is sponsored by the a US National Science Foundation and run by the University of Florida that provides digital data from public, non-federal, US collections. Data are standardized in a Darwin Core format, and provided “as is”. IDigBio joined the GBIF network in 2017. IDigBio records were downloaded from a polygon of the region of study and subject to taxonomic review and selection (accessed through the iDigBio portal, https://portal.idigbio.org/portal/search, on or about 2019-05-19).</p> <p> </p> <p><strong>FishNet2 </strong>(http://www.fishnet2.net/): FishNet2 is a collaborative effort that aggregates data on fish collections around the world to share and distribute data on specimen holdings from ~75 museums, universities and other institutions. FishNet2 distributes data in Darwin Core, and data are provided “as is”. FishNet2 is part of the network VerNet, which has contributed to GBIF since 2013 and became part of IDigBio in 2016. While FishNet2 has made substantial efforts to georeference location-record data it hosts, many hosted records still lack georeferencing. FishNet2 data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the Fishnet2 Portal, www.fishnet2.org, 2019-05-19).</p> <p> </p> <p><strong>FishBase</strong> (<a href="http://www.fishbase.org/">http://www.fishbase.org</a>): FishBase is a global biodiversity information system supervise by a consortium of nine non-USA international institutions, and hosts data on fin fishes and elasmobranchs (Froese & Pauly, 2009). Information presented in FishBase is extracted from the scientific literature, reports and museum or aggregator (GBIF) databases, and standardized by a team of specialists. Data from Fishbase were downloaded for the following ecosystems: Caribbean Sea, Gulf of Mexico, Southeast U.S. Continental Shelf, Atlantic Ocean, Sargasso Sea and Bermuda, and subject to taxonomic review and selection after downloading (2019-05-19).</p> <p> </p> <p><strong>Smithsonian Tropical Research Institute</strong> (STRI; <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>): The STRI database was compiled by DRR and Ernesto Peña at STRI’s Naos Marine Laboratory, and represents about 15 years accumulation of curated data (see below) from the following sources: data downloaded at roughly two year intervals from the five aggregators; data from online databases of various museums that supply aggregators (data directly downloaded from a museum sometimes differs from that available in an aggregator from the same museum), including the Swedish Museum of Natural History, the American Museum of Natural History, the Natural History Museum of Denmark, the Gulf Coast Research Laboratory, the Colombian Museum of Natural Marine History, the United States National Museum, and the United States Geological Survey; data from national aggregators of Colombia (Sistema de Información Sobre Biodiversidad de Colombia (https://sibcolombia.net/), and Sistema de Información Ambiental Marina de Colombia, https://siam.invemar.org.co/), Mexico (La Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO; http://www.conabio.gob.mx/informacion/gis/), and Costa Rica (Museo de Zoologia de la Universidad de Costa Rica, http://museo.biologia.ucr.ac.cr/); verified (by DRR) underwater photographs of fishes taken at known locations; peer reviewed publications containing location information (species descriptions; taxonomic revisions of species, genera and families; regional and local checklists); fisheries reports; digital tagging data for species such as elasmobranchs; diving surveys and collections of local faunas by DRR (e.g. Robertson et al. 2019). In addition selected data from two sources that collect species lists at sites scattered throughout the Greater Caribbean are incorporated: from the Atlantic and Gulf Rapid Reef Assessment program (AGRRA, https://www.agrra.org/: Kramer & Lang, 2003) and from trained citizen scientists who contribute data on fishes to the Reef Environmental Education Foundation’s database (REEF: Pattengill-Semmens & Semmens, 2003). The bibliographic module (https://biogeodb.stri.si.edu/caribbean/en/library) of Robertson & VanTassel (2019) contains ~1700 publications linked to species names, among them the publications from which location data were extracted.</p> <p> </p> <p>Data from the aggregators is presented “as is” and the aggregators themselves do not do data curation. Duplicates (and occasionally triplicates and quaduplicates) of the same museum record often are included from multiple sources (e.g. the original museum source, derivative checklists, an aggregator), sometimes with slightly different georeferenced coordinates. Data available in one year may subsequently disappear from an aggregator, and different data may be available for the same species under different names (e.g. the old and new names when a species is reassigned to another genus). Errors, sometimes large errors (Robertson, 2008), are common in aggregator data, from museums as well as other sources, and longstanding errors can seem to take on a perpetual existence. For example the damselfish <em>Abudefduf saxatilis </em>is a common and widespread inhabitant of tropical reefs on both sides of the Atlantic, but does not naturally occur outside that ocean. Despite the fact that its taxonomic status and range were resolved ~30 y ago (e.g. see Allen, 1991) museum data presented by the all five aggregators that contributed to the multi-source database used in this study currently (December 10, 2019) show large numbers of records of this species throughout the entire tropical Indo-Pacific, as well as across its native range in the Atlantic. Since many of the databases accumulating on aggregators are derivative (lists derived from records and from other derivative lists) it will become increasingly difficult to eliminate such errors as corrections to data in primary sources do not automatically propagate through the chain of usage by different databases. Due to increasing limitations on resources for taxonomic work, museums themselves have difficulty dealing with errors in specimen identity and location, and old specimens become unidentifiable, specimens never get returned when loaned out, or simply vanish, and entire collections can get destroyed by hurricanes or fires, or get dumped when museums close or experience a major change in mission. Georeferenced location data on fish distributions in the neotropics (and presumably most other areas) hosted by aggregators, particularly GBIF and OBIS, which take data from a broad range of source types, might best be described as messy, and the significant potential for errors in location records and an inability to verify records always needs to be taken into account when incorporating data from aggregators, primary museum sources, and analog sources.</p> <p> </p> <p>Data considered for inclusion in the STRI database were screened as follows to exclude questionable records. Data from two databases hosted by OBIS and GBIF were excluded entirely due to lack of reliability: BioGoMx (https://www.gulfbase.org/project/biodiversity-gulf-mexico-biogomx-database) and Diveboard (http://www.diveboard.com). The only REEF data used were from “expert” REEF recorders on readily identifiable species that are unlikely to be confused with similar species (e.g. data for some genera of sparids, gerreids, labrisomids and gobies that include various sympatric species with very similar appearances, were not used). After data from aggregators and museum sources were combined into a single database duplicate records were filtered out by rounding all records to three decimal places and eliminating duplicates, a process that inevitably deleted some valid records as well as duplicates. The sizes of the databases and abundance of such duplicates precluded individual manual exclusion. Finally, all location data for each species were revised by DRR by examining the distribution of its georeferenced coordinates overlayed on a digital map of the current known distribution range of that species (for such range information see Carpenter & De Angelis, 2002; Ebert <em>et al.</em>, 2013; Last <em>et al.</em>, 2016; Robertson & Van Tassell, 2019; IUCN Redlist species accounts for most species considered here: https://www.iucnredlist.org/search). Such revision took into account any recent modifications to taxonomy and distributions due to new data and new publications, or as a result of discussions between DRR and experts in the taxonomy of particular species or genera. Source information of many individual questionable records provided by aggregators with the hosted data was inspected to try and assess their validity. Records thought likely to be erroneous were deleted. Those included inexplicable records lacking adequate documentation located well outside the known distribution range, and records in unlikely habitats (e.g. on land for marine species; in deep water for shallow-water species). This revision process reduced the number of records by about 30%.</p> <p> </p> <p>Data from the five individual aggregator databases that are used in the comparisons described here were all downloaded from their online portals during May, 2019. However, data from those five aggregators that were incorporated in the STRI database were downloaded in March 2017, with data from other sources described above added to the STRI database intermittently between then and May 2019, when the entire dataset was curated as described above. Hence the five individual aggregator databases analyzed in this study undoubtedly contain data not included in the version of the STRI database used in the present analyses.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p> </p> <p>Data acquisition and construction of the STRI database was supported by funds from STRI, the Smithsonian Marine Science Network, the Smithsonian Publications Fund, the Smithsonian’s Deep Reef Observation Project, the National Geographic Society, the IUCN Red List program, the Harte Research Institute, and CONABIO. We thank REEF and AGRRA for supplying species-location records, various people for taxonomic and location-record information used to construct that database (principal among them C Baldwin, S Brandl, K Conway B Frable, T Menut, T Munroe, R Robins, L Tornabene, J Van Tassell and B Victor), and hundreds of citizen-scientist submarine photographers whose images (see <a href="https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists">https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists</a>) acted as vouchers for location records.</p> <p> </p> <p><strong>References</strong></p> <p><strong> </strong></p> <p>Allen, G.R. (1991) <em>Damselfishes of the World</em>. Mergus, Melle, 271 p.</p> <p>Baldwin, C.C., Tornabene, L. & Robertson, D.R. (2018) Below the mesophotic. <em>Scientific Reports</em>, 8, 4920.</p> <p>Carpenter, K.E. (Ed) (2002) <em>The living marine resources of the Western Central Atlantic.</em> Vols 1-3, FAO, Rome, 2127 p.</p> <p>Ebert, D.A., Fowler, S., Compagno, L. (2013) <em>Sharks of the World: a fully illustrated guide</em>. Wild Nature Press, Plymouth. 528 p.</p> <p>GEBCO Compilation Group (2019) GEBCO 2019 Grid (doi:10.5285/836f016a-33be-6ddc-e053-6c86abc0788e).</p> <p>Kapoor, D.C. (1981) General bathymetric chart of the oceans (GEBCO). <em>Marine Geodesy</em>, 5, 73–80.</p> <p>Kramer, P.R. & Lang, J.C. (2003) Appendix one: The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Protocols: Former Version 2. 2. <em>Atoll Research Bulletin</em>, 496, 611–624.</p> <p>Last, P. R., White, W.A., de Carvalho, M.R., Séret, B., Stehmann, F.W., & Naylor, J.P. (2016). <em>Rays of the World</em>. CSIRO, Clayton. 790 p.</p> <p>Pattengill-Semmens, C.V. & Semmens, B.X. (2003) <em>Conservation and management applications of the reef volunteer fish monitoring program</em>. <em>Coastal Monitoring through Partnerships: Proceedings of the Fifth Symposium on the Environmental Monitoring and Assessment Program (EMAP) Pensacola Beach, FL, U.S.A., April 24–27, 2001</em> (ed. by B.D. Melzian), V. Engle), M. McAlister), S. Sandhu), and L.K. Eads), pp. 43–50. Springer Netherlands, Dordrecht.</p> <p>Robertson, D. R. (2008) Global biogeographic databases on marine fishes: caveat emptor. <em>Diversity and Distributions, 14<strong>,</strong> 891-892</em></p> <p>Robertson, D.R,, Dominguez-Dominguez, O., Lopez Arollo, Y.M., Moreno Mendoza. R., Simoes, N. (2019) Reef-associated fishes from the offshore reefs of western Campeche Bank, Mexico, with a discussion of mangroves and seagrass beds as nursery habitats. <em>Zookeys </em>843: 71-115. <a href="https://doi.org/10.3897/zookeys.843.33873">https://doi.org/10.3897/zookeys.843.33873</a></p> <p>Robertson, D.R & Van Tassell, J. (2019) Shorefishes of the Greater Caribbean: online information system. Version 2.0. <em>Smithsonian Tropical Research Institute, Balboa, Panamá</em>. <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>.</p> <p>Wessel, P. & Smith, W.H.F. (1996) A global, self-consistent, hierarchical, high-resolution shoreline database. <em>Journal of Geophysical Research: Solid Earth</em>, 101, 8741–8743.</p>
Data from: Linking land use and the nutritional ecology of herbivores: a case study with the Senegalese locust
1) Access to high-quality food is a main driver of population dynamics. For herbivores protein and carbohydrates are key nutrients that are notoriously variable in plants and are affected by land use. However, few studies have linked foraging decisions and performance in the laboratory to the nutritional landscape available in the field. 2) <i>Oedaleus senegalensis</i> is a nonmodel locust, a grass-feeder, and the main pest of millet, a subsistence crop in the Sahel. In this study, we examined dietary preference and locust performance across a range of protein:carbohydrate ratios using the Geometric Framework methodology. We then applied a fitness landscape approach to visualize these results with the plant nutrient contents available across four land-use types: millet, groundnut, fallow, and grazed fields. Finally, we contrasted our results with locust distribution in the field. Several locust species (<i>O. senegalensis</i> included) exhibit density dependent color polymorphism thus we also reported individual coloration (brown or green). 3) We found that <i>O. senegalensis</i> preferred moderately carbohydrate-biased food 1:1.6 protein: carbohydrate ratio. All traits recorded (mass gain, development time, growth rate, molt success, and performance index) were best near that ratio and declined on either side presenting a "hump-shape". Fallow fields contained more plants, particularly grasses, that were both abundant and closer to the optimal protein:carbohydrate ratio recorded from the lab experiments. 4) When we surveyed <i>O. senegalensis</i> abundance and proportion, we found that they were more numerous in the fallow fields. Brown morph individuals, the ones associated with high density, were proportionally more abundant in fallow fields than green individuals. 5) Our study provides evidence that variation in nutritional landscapes—relative to an herbivore's optimal nutrient balance—is a key driver of herbivore population distribution and abundance, and can be used to predict bottom-up effects on herbivore species. protein:carbohydrate ratio recorded from the lab experiments.
Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon
<p>This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present. </p>
OW2 Decoder java use-cases data (WP6)
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Decoder OpenCV use case data
<p>OpenCV datasets for DECODER project deliverable D6.2 "Use-case data from the PKM". See the deliverable for further details (deliverable will be available on <a href="https://www.decoder-project.eu/view/Main/Deliverables">project website</a> after EC review).</p>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Use Case Points Benchmark Dataset
<p>This dataset was gathered by us from three software houses. This is real-life dataset. Use Case points method as originated by Karner was used for counting a steps or number of actors. Data are based on different languages, various problem domains. ISBSG style for language, domain and application type were adopted.</p> <p><br> Attributes are used as follows:<br> Project_No - only project ID for identification purposes<br> Simple Actors - Number of actor classify according UCP - simple actors.<br> Average Actors - Number of actor classify according UCP - average actors.<br> Complex Actors - Number of actor classify according UCP - complex actors.<br> UAW - Unadjusted Actor weight, computed by using UCP equation. <br> Simple UC - Number of use cases classified as simple - UCP number of steps is used.<br> Average UC - Number of use cases classified as average - UCP number of steps is used.<br> Complex UC - Number of use cases classified as complex - UCP number of steps is used.<br> UUCW - Unadjusted UseCase Weight - computed by using UCP equation.<br> TCF - Technical Complexity FactorECF - Enviromental Complexity Factors<br> Real_P20 - Real_P20 - Real Effort in Person hours, decided by productivity factor (PF = 20).<br> Real_Effort_Person_Hours - Real Effort (development time) in person-hours.<br> Sector - Problem domain of projectLanguage - Programming language used for project.<br> Methodology - Development methodology used for project development.<br> ApplicationType - Classification of project type - provided by donator. <br> DataDonator - anonymized acronym for data donator.<br> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.