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136 results for “BIDS”
Dataset of Concurrent EEG, ECG, and Behavior with Multiple Doses of transcranial Electrical Stimulation - BIDS
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Dataset Clinical Epilepsy iEEG to BIDS -RESPect_intraoperative_iEEG
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Dataset Clinical Epilepsy iEEG to BIDS - RESPect_longterm_iEEG
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bids_dataset
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Bids Pilot Project
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Delay Discounting Bidding
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BIDS Data for "A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation"
<p>Base data package for the “"A Whole-Brain Map and Assay Parameter Analysis of Mouse VTA Dopaminergic Activation” article, formatted corresponding to the Brain Imaging Data Structure.</p>
Demo bids for to demonstration areas in OneNet project of the Hungarian demonstration
<p>Bid auction data of a DSO flexibility market simulation data in two demo areas based on past real measurement, power gas exchange data.</p> <p>The two .csv files contain the bid data for all FSP assets in two demonstration areas, both are a given snippet of DSO networks used for congestion simulations, Demo Area 1 and Demo Area 2, respectively. The bids are simulated and based on post hoc day-ahead market data and measurements. All FSP assets are photovoltaic generators. For a given day every asset submits stepwise hourly bids for every hour of the day.</p> <p>The two .CSV files consist of the following columns:</p> <p>Date: the date that the bid is submitted to (YYYY-MM-DD)</p> <p>Time: the hour that the bid is submitted to (HH)</p> <p>AssetId: ID of the bidding asset (photovoltaic generator)</p> <p>quantity: quantity of a bid step for an hour of a day in MW</p> <p>price: the price of the bid step for an hour of a day in EUR</p> <p> </p> <p>More information on the demo areas can be found in the <a href="https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D10.4_V1.0.pdf">D10.4 Report on demonstration</a> deliverable of the OneNet project.</p> <p>public_demo_area_1.csv file represents the bids in the E.On demo area and the public_demo_area_2.csv file in the MVM demo area, respectively. </p> <p> </p>
Sample Multi-Modal BIDS dataset (v2.1)
<p>This a sample BIDS dataset created for continous integration of the Connectome Mapper 3.</p> <p>This dataset was acquired at the Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, using a 3T Siemens Prisma MRI scanner.</p> <p>It adopts the sub-/ses- structure and contains one T1w anatomical MRI (MPRAGE), one diffusion MRI (DSI) , and one resting-state functional MRI as well as additional Freesurfer derivatives.</p> <p>It is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. (See https://creativecommons.org/licenses/by/4.0/ for more details)</p> <p><strong><em>Changes</em></strong></p> <p><em>Version 2.1</em></p> <ul> <li>Fix issues with the resampling of the DWI and rfMRI scans with Slicer. They were regenerated in version 2.1 with `mri_convert` to better handle the 4th dimension.</li> <li>For the sake of the size of the dataset, only 100 frames in the fMRI recording has been kept and the <em>sourcedata/</em> folder has been dropped but can be easily be retrieved in the previous 2.0 version (https://zenodo.org/record/5788803#.Yb2-giYo8bV).</li> </ul> <p>Version 2.0</p> <ul> <li>For testing purposes, scans found in the root <em>sub-01</em> directory have been downsampled to 2x2x2 mm3 (MPRAGE), and to 3x3x3 mm3 (DSI and rfMRI) with the ResampleScalarVolume module of Slicer 4.6.2. A copy of the output produced in the terminal by Slicer has been created in the `code/` directory.</li> <li>Original data have been placed in <em>sourcedata/</em> in concordance to BIDS.</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
Supporting Dataset for the Analysis on TSO-DSOs Cooperation and Stable Cost Allocation for the Joint Procurement of Flexibility (Network and Bid List)
<p>The data provides supporting material for the two case studies in Chapter 5 of CoordiNet D6.2 (the deliverable is available at <a href="https://coordinet-project.eu/publications/deliverables">https://coordinet-project.eu/publications/deliverables</a>) and the two case studies in paper on TSO-DSO cooperation (available at <a href="https://arxiv.org/abs/2111.12830">https://arxiv.org/abs/2111.12830</a>).</p> <p>The dataset is cooresponding to two case studies. In the first case study, the interconnected system consists 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). The interface flow limit is TPmax. In the second case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three Matpower systems 18-bus distribution networks, who are named as DN_1, DN_2, DN_3. </p> <p>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. The interconnected system is fully represented in "Network_XXX.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_XXX);</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_XXX.xlsx" (transmission system) and "OrderbookDN_XXX.xlsx" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_XXX) 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>
BIDS formatted EEG meditation experiment data
<p>This meditation experiment contains 24 subjects. Subjects were meditating and were interrupted about every 2 minutes to indicate their level of concentration and mind wandering. The scientific article (see Reference file) contains all methodological details.</p> <p>- Arnaud Delorme (October 17, 2018)</p>
Hydropower dataset of hourly inflow values for European bidding zones for ACDC-ESM
<p><strong>Energy Climate dataset consistent with ENTSO-E Pan-European Climatic Database (PECD 2021.3) in CSV and netCDF format</strong></p> <p> </p> <p><strong>TL;DR</strong>: this is a nationally aggregated hourly dataset for the capacity factors per unit installed capacity for storage hydropower plants and run-of-river hydropower plants in the European region. All the data is provided for 30 climatic years (1981-2010).</p> <p> </p> <p><strong>Method Description </strong><br> The hydro inflow data is based on historical river runoff reanalysis data simulated by the E-HYPE model. E-HYPE is a pan-European model developed by The Swedish Meteorological and Hydrological Institute (SMHI), which describes hydrological processes including flow paths at the subbasin level. E-hype only provides the time series of daily river runoff entering the inlet of each European subbasin over 1981-2010. To match the operational resolution of the dispatch model, we linearly downscale these time series to hourly. By summing up runoff associated with the inlet subbasins of each country, we also obtain the country-level river runoff.</p> <p>The hydro inflow time series per country is defined as the normalized energy inflows (per unit installed capacity of hydropower) embodied in the country-level river runoff. A dispatch model can be used to decides whether the energy inflows are actually used for electricity generation, stored, or spilled (in case the storage reservoir is already full).</p> <p><strong>Data coverage</strong><br> This dataset considers two types of hydropower plants, namely storage hydropower plant (STO) and run-of-river hydropower plant (ROR). Not all countries have both types of hydropower plants installed (see table). </p> <p>The countries and their acronyms for both technologies included in this dataset are:</p> <table> <thead> <tr> <th scope="col">Country</th> <th scope="col">Run-of-River </th> <th scope="col">Storage</th> </tr> </thead> <tbody> <tr> <td>Austria</td> <td>AT_ROR</td> <td>AT_STO</td> </tr> <tr> <td>Belgium</td> <td>BE_ROR</td> <td>BE_STO</td> </tr> <tr> <td>Bulgaria</td> <td>BG_ROR</td> <td>BG_STO</td> </tr> <tr> <td>Switzerland</td> <td>CH_ROR</td> <td>CH_STO</td> </tr> <tr> <td>Cyprus</td> <td>CZ_ROR</td> <td>CZ_STO</td> </tr> <tr> <td>Germany</td> <td>DE_ROR</td> <td>DE_STO</td> </tr> <tr> <td>Denmark</td> <td>DK_ROR</td> <td> </td> </tr> <tr> <td>Estonia</td> <td>EE_ROR</td> <td> </td> </tr> <tr> <td>Greece</td> <td>EL_ROR</td> <td>EL_STO</td> </tr> <tr> <td>Spain</td> <td>ES_ROR</td> <td>ES_STO</td> </tr> <tr> <td>Finland</td> <td>FI_ROR</td> <td>FI_STO</td> </tr> <tr> <td>France</td> <td>FR_ROR</td> <td>FR_STO</td> </tr> <tr> <td>Great Britain</td> <td>GB_ROR</td> <td>GB_STO</td> </tr> <tr> <td>Croatia</td> <td>HR_ROR</td> <td>HR_STO</td> </tr> <tr> <td>Hungary</td> <td>HU_ROR</td> <td>HU_STO</td> </tr> <tr> <td>Ireland</td> <td>IE_ROR</td> <td>IE_STO</td> </tr> <tr> <td>Italy</td> <td>IT_ROR</td> <td>IT_STO</td> </tr> <tr> <td>Luxembourg</td> <td>LU_ROR</td> <td> </td> </tr> <tr> <td>Latvia</td> <td>LV_ROR</td> <td> </td> </tr> <tr> <td>the Netherlands</td> <td>NL_ROR</td> <td> </td> </tr> <tr> <td>Norway</td> <td>NO_ROR</td> <td>NO_STO</td> </tr> <tr> <td>Poland</td> <td>PL_ROR</td> <td>PL_STO</td> </tr> <tr> <td>Portugal</td> <td>PT_ROR</td> <td>PT_STO</td> </tr> <tr> <td>Romania</td> <td>RO_ROR</td> <td>RO_STO</td> </tr> <tr> <td>Sweden</td> <td>SE_ROR</td> <td>SE_STO</td> </tr> <tr> <td>Slovenia</td> <td>SI_ROR</td> <td>SI_STO</td> </tr> <tr> <td>Slovakia</td> <td>SK_ROR</td> <td>SK_STO</td> </tr> </tbody> </table> <p> </p> <p><strong>Data structure description</strong><br> The files is provided in CSV (.csv) format with a comma (,) as separator and double-quote mark (") as text indicator. The first row stores the column labels. The columns contain the following:</p> <ul> <li>first column (or A) contains the row number <ul> <li>Label: unlabeled</li> <li>Contents: interger range [1,262968]</li> </ul> </li> <li>second column (or B) contains the valid-time <ul> <li>Label: T1h</li> <li>Contents represent time with text as [DD/MM/YYYY HH:MM])</li> </ul> </li> <li>column 3-52 (or C-AY) each contain the capacity factor for each valid combination of a country and hydropower plant type <ul> <li>Label: XX_YYY the two letter country code (XX) and the hydropower plant type (YYY) acronym for storage hydropower plant (STO) and run-of-river hydropower plant (ROR)</li> <li>Contents represent the capacity factor as a floating value in the range [0,1], the decimal separator is a point (.).</li> </ul> </li> </ul> <p><strong>DISCLAIMER</strong>: <em>the content of this dataset has been created with the greatest possible care. However, we invite to use the original data for critical applications and studies. </em></p>
BIDS wildtype data selection from "Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories"
<p>Data package selecting wildtype animals from the “Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories” article, formatted corresponding to the Brain Imaging Data Structure. The relevant publication can be found via DOI <a href="https://doi.org/10.1093/cercor/bhy046">10.1093/cercor/bhy046</a> .</p>
BIDS CHB-MIT Scalp EEG Database
<p>This dataset is a BIDS-compatible version of the CHB-MIT Scalp EEG Database. It reorganizes the file structure to comply with the BIDS specification. To this effect:</p><ul><li>The data from subject chb21 was moved to sub-01/ses-02.</li><li>Metadata was organized according to BIDS.</li><li>Data in the EEG edf files was modified to keep only the 18 channels from a double banana bipolar montage.</li><li>Annotations were formatted as BIDS-score compatible `tsv` files.</li></ul><h3><strong>Details related to access to the data</strong></h3><h4><strong>License</strong></h4><p>The dataset is released under the <a href="https://physionet.org/content/chbmit/view-license/1.0.0/">Open Data Commons Attribution License v1.0</a>.</p><h4><strong>Contact person</strong></h4><p>The original Physionet CHB-MIT Scalp EEG Database was published by Ali Shoeb. This BIDS-compatible version of the dataset was published by Jonathan Dan.</p><h4><strong>Practical information to access the data</strong></h4><p>The original Physionet CHB-MIT Scalp EEG Database is available on the <a href="https://physionet.org/content/chbmit/1.0.0/">Physionet website</a>.</p><h3><strong>Overview</strong></h3><h4><strong>Project name</strong></h4><p>CHB-MIT Scalp EEG Database</p><h4><br><strong>Year that the project ran</strong></h4><p>2010</p><h4><strong>Brief overview of the tasks in the experiment</strong></h4><p>This database, collected at the Children's Hospital Boston, consists of EEG recordings from pediatric subjects with intractable seizures. Subjects were monitored for up to several days following withdrawal of anti-seizure medication in order to characterize their seizures and assess their candidacy for surgical intervention.</p><h4><strong>Description of the contents of the dataset</strong></h4><p>Each folder (sub-01, sub-01, etc.) contains between 9 and 42 continuous .edf files from a single subject. Hardware limitations resulted in gaps between consecutively-numbered .edf files, during which the signals were not recorded; in most cases, the gaps are 10 seconds or less, but occasionally there are much longer gaps. In order to protect the privacy of the subjects, all protected health information (PHI) in the original .edf files has been replaced with surrogate information in the files provided here. Dates in the original .edf files have been replaced by surrogate dates, but the time relationships between the individual files belonging to each case have been preserved. In most cases, the .edf files contain exactly one hour of digitized EEG signals, although those belonging to case sub-10 are two hours long, and those belonging to cases sub-04, sub-06, sub-07, sub-09, and sub-23 are four hours long; occasionally, files in which seizures are recorded are shorter.</p><p>The EEG is recorded at 256 Hz with a 16-bit resolution. The recordings are referenced in a double banana bipolar montage with 18 channels from the 10-20 electrode system.</p><p>The dataset also contains seizure annotations as start and stop times.</p><p>The dataset contains 664 `.edf` recordings. 129 those files that contain one or more seizures. In all, these records include 198 seizures.</p><h3><strong>Methods</strong></h3><h4><strong>Subjects</strong></h4><p>23 pediatric subjects with intractable seizures. (5 males, ages 3–22; and 17 females, ages 1.5–19; 1 n/a)</p><h4><strong>Apparatus</strong></h4><p>Recordings were performed at the Children's Hospital Boston using the International 10-20 system of EEG electrode positions. Signals were sampled at 256 samples per second with 16-bit resolution.</p>
Info about bidding (market data)
<p>The SLO_BIDDING_DATA dataset includes data regarding tendered bids, accepted bids and price of capacity bids (monthly aggregates). It is intended for market players and system operators to follow the prices on the market and amount of purchased flexibility. </p> <p>Two data sets in the CIM XML format are available with information about bidding for DSOs. One document is covering the process type "congestion management", the other for "voltage control". Due to CGMES being TSO oriented, some attributes are missing to fully describe DSO processes. Therefore we had to extend the attribute "processType" to cover DSO needs.</p> <p>Addition information about the Slovenian demo is available in the OneNet 10.4 deliverable (<a href="https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D10.4_V1.0.pdf">OneNet_D10.4_V1.0.pdf (onenet-project.eu)</a>).</p> <p>XSD is compliant with ReserveBid_Document defined by ENTSO-E (<a href="https://eepublicdownloads.entsoe.eu/clean-documents/EDI/Library/cim_based/schema/Reserve_bid_document_UML_model_and_schema_v1.2.pdf">Reserve bid document UML model and schema (entsoe.eu)</a>).</p>
BRAIN Journal-Intelligent Continuous Double Auction method For Service Allocation in Cloud Computing-Figure 2. bid value for time factor
<p>Each consumer is looking for utilizing its requested service with minimum price before its<br> deadline. To utilize a service all required resources should be allocated before deadline and<br> otherwise service failed to utilize and consumer must pay penalty to providers for all other<br> resources which is allocated to it. So Consumer should adjust its bid price rapidly to the acceptable<br> price of the market. Since consumers are generally sensitive to deadline in acquiring requested<br> service, it is intuitive to consider deadline time when formulating the bid price. Consumer agent<br> time dependent bid price formula is determined in.</p>
Linked collectors and determiners for: La liste Rouge des plantes menacées de la Guinée (BID-AF2015-0042-NAC).
Natural history specimen data linked to collectors and determiners held within, "La liste Rouge des plantes menacées de la Guinée (BID-AF2015-0042-NAC)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/60b45fdd-246f-4bfd-999e-9fe187cc9c1f">https://bionomia.net/dataset/60b45fdd-246f-4bfd-999e-9fe187cc9c1f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/60b45fdd-246f-4bfd-999e-9fe187cc9c1f">https://gbif.org/dataset/60b45fdd-246f-4bfd-999e-9fe187cc9c1f</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: La seconde liste Rouge des plantes menacées de la Guinée (BID-AF2015-0042-NAC).
Natural history specimen data linked to collectors and determiners held within, "La seconde liste Rouge des plantes menacées de la Guinée (BID-AF2015-0042-NAC)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4179bbb7-eb1f-4318-9bfa-7280dbb46f79">https://bionomia.net/dataset/4179bbb7-eb1f-4318-9bfa-7280dbb46f79</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4179bbb7-eb1f-4318-9bfa-7280dbb46f79">https://gbif.org/dataset/4179bbb7-eb1f-4318-9bfa-7280dbb46f79</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Occurrence Gabon BID-AF2020-194-USE.
Natural history specimen data linked to collectors and determiners held within, "Occurrence Gabon BID-AF2020-194-USE". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/44d6b38d-af49-470a-b03c-438b636d947b">https://bionomia.net/dataset/44d6b38d-af49-470a-b03c-438b636d947b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/44d6b38d-af49-470a-b03c-438b636d947b">https://gbif.org/dataset/44d6b38d-af49-470a-b03c-438b636d947b</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Data from CRSN herbarium digitization initiative under BID-funded project.
Natural history specimen data linked to collectors and determiners held within, "Data from CRSN herbarium digitization initiative under BID-funded project". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/bcffbc19-b28c-42ae-995a-ff852e158ce5">https://bionomia.net/dataset/bcffbc19-b28c-42ae-995a-ff852e158ce5</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/bcffbc19-b28c-42ae-995a-ff852e158ce5">https://gbif.org/dataset/bcffbc19-b28c-42ae-995a-ff852e158ce5</a>. Formatted as a Frictionless Data package.
ScienceDex guides
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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.