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2,222 results for “coordination”
Dataset and R code: Above and belowground functional trait coordination in the Neotropical understory genus Costus
<p>Dataset and R code accompanying the paper "Above and belowground functional trait coordination in the Neotropical understory genus <em>Costus</em>" published by AoB Plants. </p>
MESSENGER magnetometer and coordinates prepared dataset
<p>This dataset is based on the original MESSENGER mission magnetometer and coordinate data as made available at PDS PPI. It introduces a number of improvements upon the original [1 sec temporal resolution]:</p> <ol> <li>Magnetometer calibration signals have been removed.</li> <li>Coordinates and magnetic field measurements have been merged together.</li> <li>Additional fields, such as model dipole magnetic field and planetary position in a heliocentric coordinate system have been added.</li> <li>The dataset has been split up into files by orbit numbers, with each file centered on the periapsis.</li> <li>Data for a number of partially recorded orbits has been removed.</li> </ol>
Dataset to Study TSO-DSO Coordination Market Models for Flexibility Procurement to Balancing and Congestion Management
<p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 45 to 50. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. A minimum value for the quantity is imposed as 0.01. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Dataset for CoordiNet D6.2 - Chapter 4 - Evaluation of Combinations of Coordination Schemes and Products for Grid Services
<p>This is a supporting material for chapter 4 of CoordiNet D6.2. The deliverable is available at:</p> <p><a href="https://coordinet-project.eu/publications/deliverables">CoordiNet deliverables (coordinet-project.eu)</a></p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>In addition, table check_system contains the forecasted flows over lines before flexibility activation. For the transmission system, the SFTN is used and only active power is calculated. For the distribution systems, a linearized model of the power flow is used, thus active and reactive power are calculated:</p> <ul> <li>System: the system (TN, DN_18, DN_69, DN_141) where the line is located;</li> <li>From/to: bus numbers of the connection;</li> <li>Flow: forecasted active flow over transmission system lines;</li> <li>Flow P/Flow Q: forecasted active/reactive flow over distribution system lines (also for interface flows TN-DN);</li> <li>Congestion: if congestion is forecasted over the line.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
SoildiverAgro Nemoral Region Coordinator Merrit Shanskiy
<p>SoildiverAgro Nemoral Region Coordinator Merrit Shanskiy</p> <p>In this interview, Merrit Shanskiy from EULS (Nemoral Region) introduces herself and explains his role in the SoildiverAgro project and what farmers can expect from the case studies developed in his region.</p> <p>This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].</p>
SoildiverAgro Mediterranean South Regional Coordinator Raul Zornoza
<p>SoildiverAgro Mediterranean South Regional Coordinator Raul Zornoza</p> <p>In this interview, Raul Zornoza from UPCT (Mediterranean South) introduces himself and explains his role in the SoildiverAgro project and what farmers can expect from the case studies developed in his region.</p> <p>This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].</p>
SoildiverAgro Lusitanean Regional Coordinator David Fernández
<p>SoildiverAgro Lusitanean Regional Coordinator David Fernández</p> <p>In this interview, David Fernández from @UVigo (Project coordinator and head of the Lusitanean Region) introduces himself and explains his role in the SoildiverAgro project and what farmers can expect from the case studies developed in his region.</p> <p>This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].</p>
SoildiverAgro Atlantic Central Region Coordinator Lieven Waeyenberge
<p>SoildiverAgro Atlantic Central Region Coordinator Lieven Waeyenberge</p> <p>In this interview, Lieven Waeyenberge from ILVO (Atlantic Central Region) introduces himself and explains his role in the SoildiverAgro project and what farmers can expect from the case studies developed in his region</p> <p>This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].</p>
SoildiverAgro Boreal Region Coordinator Krista Peltoniemi
<p>SoildiverAgro Boreal Region Coordinator Krista Peltoniemi</p> <p>In this interview, Krista Peltoniemi from LUKE (Boreal Region) introduces herself and explains his role in the SoildiverAgro project and what farmers can expect from the case studies developed in his region.</p> <p>This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].</p>
SoildiverAgro Continental Region Coordinator Stefan Schrader
<p>SoildiverAgro Continental Region Coordinator Stefan Schrader</p> <p>In this interview, Stefan Schrader from Thünen-Institute of Biodiversity (Continetal Region) introduces himself and explains his role in the SoildiverAgro project and what farmers can expect from the case studies developed in his region.</p> <p>This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].</p>
A Practical Tool-Chain for the Development of Coordination Scenarios - Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator
<p>The Peer Model is a modeling tool for coordination based on blackboard-based collaboration. </p> <p>The tool-chain consists of a modeler, translator and simulator.</p> <p>Its goal is to help developers of distributed and concurrent coordination software better understand algorithms and identify deficiencies from the beginning.</p> <p><br> </p>
Atomic coordinates used in the solution of a TDRD2 crystal structure
<p>These coordinates were referred to as "Coordinates from [...] an unpublished TDRD2 crystal structure" in Supporting Information of our manuscript <em>Structural basis for arginine methylation-independent recognition of PIWIL1 by TDRD2</em>. This model has not been validated for any other use.</p>
Data to Three-Dimensional Binocular Eye-Hand Coordination in Normal Vision and with Simulated Visual Impairment
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G., Kwon, M. & Bex, P.J. (2018) Three-dimensional binocular eye--hand coordination in normal vision and with simulated visual impairment. <em>Experimental Brain Research</em>. https://doi.org/10.1007/s00221-017-5160-8</p>
DEM and associated kinematic GPS coordinates of September 2002 survey of the salar de Uyuni, Bolivia
<p>This dataset consists of two parts: 1) the post-processed kinematic GPS coordinates of a September 2002 survey of a 45 x 54 km region of the salar de Uyuni, Bolivia. 2) a digital elevation model (DEM) of the salar de Uyuni surface derived from those kinematic GPS data.</p> <p>Details of the survey and DEM generation can be found in the manuscript, "Topography of the salar de Uyuni, Bolivia from kinematic GPS" (doi: 10.1111/j.1365-246X.2007.03604.x). The only difference between this dataset and one described is that the DEM was generated from fitting two-dimensional Fourier basis set with parameters: L_x = L_y = 70000 meters, m = n = 10. This results in a basis set with a nominal resolution of 7 km, which is almost identical to that used in the dataset shown in the manuscript.</p>
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>
Jupyter Usage in Institutions with Coordinates
<p>A dataset with the coordinates of several Institutions which are using Jupyter along with some metadata</p>
Back to the edge: relative coordinate system for use-wear analysis [complement to Online Resource 6]
<p>Raw data, and R markdown scripts and HTML outputs of the statistical procedures.</p> <p>Instructions to download all files at once are given here: <a href="https://doi.org/10.5281/zenodo.4011952">https://doi.org/10.5281/zenodo.4011952</a></p>
Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data
<p>This is a release of the Big Data to Knowledge (BD2K) Training Coordinating Center (TCC) Educational Resource Discovery Index (ERuDIte) as Linked Data.<br> <br> ERuDIte contains over 11,000 training resources on data science including courses (MOOCs), video tutorials, conference talks, and other materials. The metadata of these resources is described uniformly using schema.org. In addition, we use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology (DSEO), which we developed to further describe the contents of the training resources. Resource relevance and tags are curated by experts to ensure high quality. Finally, we map the references to people and organizations in the learning resource metadata to entities in DBpedia, DBLP, and ORCID, thus embedding our collection in the web of linked data. Our collection is continually growing. We hope that ERuDIte will provide a framework to foster open linked educational resources on the web.<br> <br> Distributed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/)</p>
Toolkit on Open Access for Research Project Coordinators
<p>The materials in this toolkit were created by Romain Féret as a resource for training on how to help project coordinators to comply with their open access requirements. The slides of the training are available on Zenodo at 10.5281/zenodo.3381783. This training day took place on Wednesday the 5th of June 2019, at the University of Lille. It was organized with the support of Couperin as a part of its activities in the project OpenAIRE-Advanced.</p> <p>The tutorials are divided into two folders. The ‘Coordinator’ folder contains documents that can be sent directly to the researchers, while the ‘Support staff’ folder contains tutorials for support staff (librarians, project managers) who help the coordinators to manage their project. Each tutorial is in .pdf and .docx format for easy reuse and modification. Each document is available in French and in English.</p>
Processed data from SnoHATS and METCRAX II: anisotropic turbulence and geometry of the Reynolds stress tensor in a streamline coordinate system
<p>Datasets used for the paper 'Interpreting turbulence anisotropy in a streamline coordinate system'. Data from SnoHATS and METCRAX II field campaigns. Datasets include turbulent quantities calculated on 30- and 1-min averaging windows for unstable and stable conditions, with prior linear detrending. Planar fit was used in METCRAX II and double rotation in SnoHATS to rotate the flow into the mean wind direction. Datasets include quantities to characterize the anisotropy of the Reynolds stress tensor, such as eigenvalues, eigenvectors, and the angles between the eigenvectors and the streamline coordinate system, defined in the direction of the mean wind vector.</p> <p>1c: one-component Reynolds stress tensor</p> <p>2c: two-component axisymmetric Reynolds stress tensor</p> <p>3c: isotropic Reynolds stress tensor</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.