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136 results for “BIDS”
BIDS Data for "Effects of Acute and Chronic Reuptake Inhibitionon on Optogenetically Induced Serotonergic Activity"
<p>Base data package for the “Effects of Acute and Chronic Reuptake Inhibitionon Optogenetically Induced Serotonergic Activity” article, formatted corresponding to the Brain Imaging Data Structure.</p>
BIDS Data for "An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging"
<p>Base data package for the “An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging” article, formatted corresponding to the Brain Imaging Data Structure.</p>
BIDS-formatted example mouse brain data for SAMRI
<p>BIDS-formatted Magnetic Resonance Imaging mouse brain data example used in the SAMRI test suite.</p>
Results Data for "Price Formation Without Fuel Costs: The Interaction of Elastic Demand with Storage Bidding"
<h2>Abstract</h2> <p>Studies on electricity market design for high shares of wind and solar often predict the breakdown of energy-only markets, citing a lack of fuel costs to set prices. Issues include prolonged zero prices, politically unacceptable scarcity prices, price collapses from minor capacity changes, low market value cost recovery, revenue variability across different weather years, and challenges in long-term storage operation. These issues arise from modeling with perfectly inelastic demand. Introducing even a small amount of short-term elasticity (-5%) significantly mitigates these problems. A simplified model with wind, solar, batteries, and hydrogen storage shows that demand elasticity and storage opportunity costs stabilize pricing, smoothing the price duration curve, reducing zero-price hours from 90% to 30%, and ensuring price stability across capacities and weather years. Green hydrogen-derived fuels replace fossil fuels as backup. The long-term model matches the short-term model prices with identical capacities, guiding storage bidding strategies for short-term operations. A model trained on 35 years of weather data and tested on another 35 years demonstrates the energy-only market's potential in future dispatch and investment coordination.</p> <h2>Data Sources</h2> <p>- <strong>Solar and Wind Time Series (1950-2020):</strong> <a href="https://doi.org/10.17864/1947.000321">Bloomfield and Brayshaw (2021)</a><br>- <strong>Techno-Economic Assumptions:</strong><a href="https://github.com/PyPSA/technology-data/tree/v0.8.1"> technology-data (v0.8.1)</a>, <a href="https://ens.dk/en/our-services/technology-catalogues">Danish Energy Agency</a><br>- <strong>Demand Elasticity Assumptions:</strong> <a href="https://doi.org/10.1016/j.eneco.2024.107652">Hirth et al. (2024)</a>, <a href="https://www.ewi.uni-koeln.de/en/publications/on-the-functional-form-of-short-term-electricity-demand-response-insights-from-high-price-years-in-germany-2/">Arnold (2023)</a></p> <h2>Installation</h2> <p>Use <code>conda</code> environment manager:</p> <p><br><code>conda update conda</code><br><code>conda env create -f workflow/envs/environment.fixed.yaml</code><br><code>conda activate price-formation</code></p> <p><strong>Main Dependencies:</strong></p> <ul> <li>pypsa (v0.27.1)</li> <li>linopy (v0.3.8)</li> <li>snakemake (v8.5)</li> <li>gurobi (v11.0.2)</li> </ul> <h2>Run</h2> <p>From the root of the repository:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda</code></p> <p>Or with a specific scenario configuration file:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml</code></p> <h2>Cluster</h2> <p>On an HPC cluster, run:</p> <p><br><code>snakemake -call --profile slurm --use-conda --conda-frontend conda</code></p> <h2>Compress Results</h2> <p>Use <code>tar</code> to compress results (excluding the report directory):</p> <p><br><code>tar -cJf price-formation-results.tar.xz \</code><br><code> config data figures results resources workflow \</code><br><code> .gitignore .pre-commit-config.yaml .syncignore-receive \</code><br><code> .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md</code></p> <h2>Licenses</h2> <p>The code in this repository is MIT licensed.</p> <p>The data in this repository is CC-BY-4.0 licensed.</p> <h2>Amendments</h2> <p>In <code>v0.2.0</code>, we added the file <code>revision-1-amendments.tar.xz</code>, which includes additional results for sensitivity runs with cross-elastic terms.</p>
Modelling input data for the case studies of the paper "Strategic bidding in light-robust day-ahead electricity markets".
<p><span>This data package includes the modelling input data to replicate the results of the case studies included in the paper "Strategic bidding in light-robust day-ahead electricity markets". </span></p> <p><span>This supplementary data package includes the following files:</span></p> <p><span><span>-<span> </span></span></span><span>Meta Data – Input data: Dataset containing the input data for the strategic bidding behavior problem. It includes bids from conventional, demand and stochastic players and the scenarios for system imbalance and real-time production.</span></p> <p><span><span>-<span> </span></span></span><span>Readme.txt: Includes a detailed description of the data packages</span></p> <p><span> </span></p> <p><span>Sources of data:</span></p> <p><span>* Ordoudis, C., Pinson, P., Morales, J. M., & Zugno, M. (2016). An updated version of the IEEE RTS 24-bus system for electricity market and power system operation studies. Technical University of Denmark.</span></p> <p><span>* Silva-Rodriguez, L., Sanjab, A., Fumagalli, E., Virag, A., & Gibescu, M. (2022). A light robust optimization approach for uncertainty-based day-ahead electricity markets. Electric Power Systems Research, 212, 108281. https://doi.org/10.1016/J.EPSR.2022.108281<span> </span></span></p> <p><span>* Derived (scaled down) from Elia. (2024). Open data. Retrieved from https://www.elia.be/en/grid-data/open-data<span> </span></span></p> <p><span>* Own data<span> </span></span></p> <p><span><span> </span></span></p>
The Krakow Paradigm - fMRI datasets in BIDS format
<p><strong>Participants</strong></p> <p>Forty-nine participants (mean age, 24.2 ± 3.7 years; 16 males) met the following experiment requirements: no contraindication for MRI scanning; normal or corrected-to-normal vision; no reported physical or psychiatric disorders; drug-free. To ensure sufficient experience in the environment, subjects had to be Krakow residents for at least one year. Subjects lived in Krakow on average 9.1 years (SD 8.1).</p> <p>Participants were informed about the procedure and goals of the study and they gave written consent. The study was approved by the bioethics commission at the Polish Military Institute of Aviation Medicine and was conducted in accordance with ethical standards described in the Declaration of Helsinki. The study was a part of a larger registered project (ISRCTN 18109340). </p> <p><strong>Experimental Task</strong></p> <p>A novel place recognition task, the Krakow Paradigm, was prepared and generated using E-Prime 2.0 (©Psychology Software Tools). The task comprised of two stages: the training session and the fMRI session. Before the training session, subjects were presented with a map of Krakow city on which a thick red line marked the city “center” area and were asked to familiarize with the borders. </p> <p>The trial comprised of the stimulus (4.5 sec duration) and two response screens (each 1.0 sec duration), all separated by the blank screens (each 0.5 sec duration). The stimulus was a photograph taken in the Krakow city (resolution 640 x 428), presenting either characteristic landmarks (e.g. an Old Square) or uncharacteristic outside places (e.g. a playground near an estate community). Photograph was presented centrally on the light-gray background and covered 60% of the screen. On the first response screen, the question “Krakow Center?” occurred with three possible answers (‘yes’, ‘no’, ‘I don’t know’) given by pressing a button on a key-pad with right-hand index, middle, or ring finger respectively. On the second response screen, the question “Have you seen it in real-life?” occurred with two possible answers (‘yes’, ‘no’) given by pressing a button using index or middle finger respectively. For both questions, responses were recorded for 1.5 sec. Between the stimuli, a fixation point (a hash sign) was presented for a varying interval between 2.4 and 6.6 sec every 0.7 sec (on average total trial length = 12 sec). Total scan time was less than 13 minutes.</p> <p>The training session was conducted to ensure timely responses. It was comprised of 7 trials, different than those used in the fMRI session, and was presented on regular computer screen. The fMRI session included 60 trials and was presented using the VisualSystem HD (NordicNeuroLab, Bergen, Norway) binocular apparatus. 50% of the photos were taken in the “center” and 50% outside of it. Characteristic and uncharacteristic places were counterbalanced across both location possibilities. At the end of the task, a feedback information was given to participants informing them the percentage of correctly classified places. Because participants were instructed to wait until the response screen appeared before making a response, reaction times are not informative and were not reported. The rationale for this procedure was to promote accuracy rather than speed, and to encourage response preparation, i.e. memory retrieval, while looking at a photo.</p> <p><strong>MRI Data Acquisition</strong></p> <p>MRI was performed using a 3T scanner (Magnetom Skyra, Siemens) with a 20-channel head/neck coil. High-resolution, whole-brain anatomical images were acquired using a T1-MPRAGE sequence. A total of 176 sagittal slices were obtained (voxel size 1×1×1.1 mm3; TR = 2300 ms, TE = 2.98 ms, flip angle = 9°) for co-registration with the fMRI data. Next, a B0 inhomogeneity gradient fieldmap (magnitude and phase images) was acquired with a dual-echo gradient-echo sequence, matched spatially with fMRI scans (TE1 = 4.92 ms, TE2 = 7.38 ms, TR = 400 ms).</p> <p>Functional T2*-weighted images were acquired using a whole-brain echo planar (EPI) pulse sequence with the following parameters: 3 mm isotropic voxel; TR = 2070 ms; TE = 30 ms; flip angle = 90°; FOV 224 × 224 mm2; GRAPPA acceleration factor 2; and phase encoding A/P. Due to magnetic saturation effects, the first four volumes (dummy scans) of each session were discarded instantly resulting in 360 volumes acquired for each participant.</p>
Replication package for: Resolving Failed Banks: Uncertainty, Multiple Bidding & Auction Design
<p>"Resolving Failed Banks: Uncertainty, Multiple Bidding, and Auction Design," Jason Allen, Robert Clark, Brent Hickman and Eric Richert, forthcoming, <em>Review of Economic Studies.</em></p> <p> </p> <p>DATA AND CODE NEEDED TO REPLICATE ANALYSIS</p>
Input and output data for the paper "Evaluating the German PV auction program: The secrets of individual bids revealed"
<p>Batz Liñeiro, T., Müsgens, F., (2021). Energy Policy</p> <p><a href="http://doi.org/10.1016/j.enpol.2021.112618">doi.org/10.1016/j.enpol.2021.112618</a></p> <p>ABSTRACT</p> <p>Auctions have become the primary instrument for promoting renewable energy around the world. However, the data published on such auctions are typically limited to aggregated information (e.g., total awarded capacity, average payments). These data constraints hinder the evaluation of realisation rates and other relevant auction dynamics. In this study, we present an algorithm to overcome these data limitations in German renewable energy auction programme by combining publicly available information from four different databases. We apply it to the German solar auction programme and evaluate auctions using quantitative methods. We calculate realisation rates and—using correlation and regression analysis—explore the impact of PV module prices, competition, and project and developer characteristics on project realisation and bid values. Our results confirm that the German auctions were effective. We also found that project realisation took, on average, 1.5 years (with 28% of projects finished late and incurring a financial penalty), nearly half of projects changed location before completion (again, incurring a financial penalty) and small and inexperienced developers could successfully participate in auctions.</p> <p>Description</p> <p>The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "2 Auction Realizations Solar-2" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values. However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process.</p> <p>For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.</p>
Input and output data for the paper "Evaluating the German onshore wind auction programme: An analysis based on individual bids"
<p>Batz Liñeiro, T., Müsgens, F., (2023). Energy Policy</p> <p><a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a></p> <p>ABSTRACT</p> <p>Auctions are a highly demanded policy instrument for the promotion of renewable energy sources. Their flexible structure makes them adaptable to country-specific conditions and needs. However, their success depends greatly on how those needs are operationalised in the design elements. Disaggregating data from the German onshore wind auction programme into individual projects, we evaluated the contribution of auctions to the achievement of their primary (deployment at competitive prices) and secondary (diversity) objectives and have highlighted design elements that affect the policy's success or failure. We have shown that, in the German case, the auction scheme is unable to promote wind deployment at competitive prices, and that the design elements used to promote the secondary objectives not only fall short at achieving their intended goals but create incentives for large actors to game the system.</p> <p>Description</p> <p>The data package offered in this publication comprises input, processing, and output files, accompanied by the corresponding R-codes used for data processing at different stages. Among the various data outputs, the "Auctions" sheet within the file "3 Auction Realizations Onshore Wind" holds particular significance for users. Within this sheet, users can identify the realized projects, their respective IDs, and the reported individual bid values (BV). However, it is recommended to refer to the attached publication to gain a comprehensive understanding of the bid-value identification process.</p> <p>For users seeking to update the results, the input files can be easily updated by referring to partner publications that share the same file names. These partner publications include the <a href="https://zenodo.org/record/7945029">UnitRegister</a>, <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and <a href="https://zenodo.org/record/8013071">TariffRegister </a>datasets.<br> </p>
UK Electricity consumption time-series from Elexon data portal (Actual Total Load Per Bidding Zone)
<p>Data from 2015-01-01 to 2023-08-10. Downloaded using ElexonDataPortal for Python.</p> <p>Dataset B0610 – Actual Total Load per Bidding Zone: <a href="https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=0CDYQw7AJahcKEwjw1P2089yAAxUAAAAAHQAAAAAQAw&url=https%3A%2F%2Fwww.elexon.co.uk%2Fdocuments%2Fbmrs-api-and-data-push-guide-for-p408%2F&psig=AOvVaw3JTwF_pxNDLFZp3HSDJa_s&ust=1692128319855369&opi=89978449">https://www.elexon.co.uk/documents/bmrs-api-and-data-push-guide-for-p408/</a></p>
A 12-Month Study Comparing Fluticasone Propionate/Salmeterol (ADVAIR) DISKUS Combination Product 250/50mcg BID To Fluticasone Propionate (FLOVENT) DISKUS 250 mcg BID In Symptomatic Subjects With Asthm
ClinicalTrials.gov study NCT00452699. IPD Sharing: YES. Countries: 5. Publications: 2.
A Double-blind, Placebo-controlled, Crossover Study to Assess the Effect of Aclidinium Bromide 400 μg Bid on COPD Symptoms and Sleep Quality After 3 Weeks of Treatment in Patients With Stable Moderate
ClinicalTrials.gov study NCT02153489. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study Evaluating the Efficacy and Safety of Intranasal Administration of 100, 200, and 400 μg of Fluticasone Propionate Twice a Day (BID) Using a Novel Bi Directional Device in Subjects With Bilateral
ClinicalTrials.gov study NCT01622569. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Three Dose Levels of CP-690,550 Monotherapy Versus Placebo, Administered Orally Twice Daily (BID) for 6 Weeks
ClinicalTrials.gov study NCT00147498. IPD Sharing: Not stated. Countries: 9. Publications: 13.
A Non-inferiority Study to Evaluate the Efficacy, Safety, and Tolerability of Combination Dry Powder of Fluticasone Propionate and Salmeterol (FSC) 250/50 Microgram (mcg) Twice Daily (BID) in Adults a
ClinicalTrials.gov study NCT01978119. IPD Sharing: YES. Countries: 2. Publications: 1.
Efficacy and Safety of Aclidinium Bromide 400 µg BID (Twice a Day)Compared to Placebo in Patients With Stable Moderate to Severe Chronic Obstructive Pulmonary Disease (COPD)
ClinicalTrials.gov study NCT01471171. IPD Sharing: Not stated. Countries: 3. Publications: 1.
Influence Of Salmeterol Xinafoate/Fluticasone Propionate (50/500 µg BID) On The Course Of The Disease And Exacerbation Frequency In COPD Patients Gold Stage III And IV
ClinicalTrials.gov study NCT00527826. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Efficacy Safety Study of Arformoterol QD Dosing Versus BID Dosing in COPD
ClinicalTrials.gov study NCT00571428. IPD Sharing: Not stated. Countries: 1. Publications: 1.
NVA237 BID Versus Placebo Twelve-week Efficacy Study
ClinicalTrials.gov study NCT01715298. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A 26-week Trial Comparing Efficacy and Safety of Insulin Degludec/Insulin Aspart BID and Insulin Degludec OD Plus Insulin Aspart in Subjects With Type 2 Diabetes Mellitus Treated With Basal Insulin in
ClinicalTrials.gov study NCT01713530. IPD Sharing: Not stated. Countries: 5. Publications: 3.
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.