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50 results for “generator region”
Datasets of sequences, alignments and structural models generated for the structural prediction of complexes mediated by intrinsically disordered regions.
<p>This repository contains input and ouput files used and generated for the scanning of intrinsically disordered region and the prediction of their binding sites to receptor proteins using the <a href="https://github.com/i2bc/SCAN_IDR">SCAN_IDR</a> pipeline with AlphaFold2-Multimer.</p><p>It contains two archives: </p><ol><li><a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> dedicated to the analysis of a dataset of 42 protein complexes non redundant with the dataset used for AlphaFold2 training,</li><li><a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> dedicated to the analysis of 923 complexes from the ELM database.</li></ol><p>These data can be used to rerun specific sections of the pipeline and scripts provided in: <a href="https://github.com/i2bc/SCAN_IDR">https://github.com/i2bc/SCAN_IDR</a></p><h4><strong>Dataset of 42 non redundant complexes</strong></h4><p>The first archive <a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> contains 3 compressed directories and a README file detailing their contents :</p><ul><li>the initial raw sequence and alignment data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the input and output data of every Alphafold run for every complex -> DIRECTORY <strong>af2_runs/</strong></li><li>the native reference structures -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p>The protein-peptide complex cases have been assigned a distinct index number, from 1 to 42, consistent across the several directories of the archive. Their corresponding directories are labelled as <i><index>_<pdbcode></i>.</p><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.2</i></p><h4><strong>Dataset of 923 complexes selected from the ELM database</strong></h4><p>The second archive <a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> contains input and ouput files used and generated for the analysis of 923 Eukaryotic Linear Motifs (ELM) database entries.</p><p>Each ELM entry is indexed with specific integer id and is composed of a receptor and a ligand protein. </p><p>The archive contains a Table associating ELM indexes with the ELM entry information, 5 directories and a README file detailing their contents:</p><ul><li>the table describing ELM entries -> FILE <strong>Table_923ELM_uid_delimitations_info_for_archive.txt</strong></li><li>the initial raw sequence and multiple sequence alignment (MSA) data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the concatenated MSA model for every ELM complex and protocol used -> DIRECTORY <strong>af2_elm_coali_inputs/</strong></li><li>the best model of every AF2 protocol for every complex according to the AF2 -> DIRECTORY <strong>af2_elm_models/</strong></li><li>the best model cut in the ligand part to select only the ELM motifs as used for the evaluation of the models -> DIRECTORY <strong>elm_cut_models/</strong></li><li>the reference structures used for the evaluation of the models -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.3</i></p>
Dataset and neural network weights to the paper: "Generative diffusion for regional surrogate models from sea-ice simulations"
<p>All the needed code and data to reproduce the results from the paper: "Generative diffusion for regional surrogate models from sea-ice simulations".<br>While most of the code is a frozen clone of the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, this capsule also includes the dataset and neural network weights to train and apply the surrogate models.</p> <p>The <strong>dataset</strong> for training and evaluation can be found at <em>data/nextsim</em>, which includes three different Zarr folders for training/validation/testing. The dataset is based on neXtSIM simulation data and ERA5 forcing data and extracted from the <a href="https://ige-meom-opendap.univ-grenoble-alpes.fr/thredds/catalog/meomopendap/extract/catalog.html">SASIP shared data OpenDAP server</a>:</p> <ul> <li>The neXtSIM simulations were performed by Gauillaume Boutin and published in the paper "<a href="https://doi.org/10.5194/tc-17-617-2023">Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework</a>" (Boutin et al., 2023) and available as Zenodo <a href="../records/7277523">dataset</a> (Boutin et al., 2022).</li> <li>The forcing data is based on the ERA5 reanalysis dataset published in the paper: "<a href="https://doi.org/10.1002/qj.3803">The ERA5 global reanalysis</a>" (Hersbach et al., 2020) and available as dataset from the Copernicus Climate Change Service (C3S, Copernicus Climate Change Service, 2023). The here used forcing data is based on the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels">hourly reanalysis data on single levels</a> and interpolated with nearest neighbors to the curvilinear grid as used in the output from the neXtSIM simulations. <strong>Disclaimer:</strong> The results contain modified Copernicus Climate Change Service information, 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</li> </ul> <p>The <strong>neural network weights</strong> are included under <em>data/models </em>and split into weights for the deterministic models and the diffusion models.<br>These neural network weights have been used to generate the results presented in the paper.</p> <p>In this capsule, the <em>notebooks</em> folder includes also the figures used within the paper and additional trajectory data used in the qualitative analysis of the paper.</p> <p>Generally, we recommend to just download the <em>data.tar.gz </em>file and use otherwise the original <a href="https://github.com/cerea-daml/diffusion-nextsim-regional">Repository</a>, since the here included code can be outdated. We further refer to the repository for additional information.</p> <p> </p> <p>Contained in this capsule:</p> <ul> <li>configs.tar.gz: The configuration files for the experiments.</li> <li>data.tar.gz: The dataset and neural network weights.</li> <li>diffusion_nextsim.tar.gz: The main code for the neural network etc.</li> <li>environment.yaml: The anaconda environment file, can be used to install the needed packages.</li> <li>notebooks.tar.gz: The notebooks that were used to create the figures in the paper. The figures from the paper and the data from the qualitative analysis are included as well.</li> <li>readme.md: The readme file from the repository.</li> <li>scripts.tar.gz: The scripts used for the experiments.</li> <li>setup.py: the file to install the <em>diffusion_nextsim</em> package in a python environment.</li> </ul> <p>References:</p> <p>Guillaume Boutin, Heather Regan, Einar Ólason, Laurent Brodeau, Claude Talandier, Camille Lique, & Pierre Rampal. (2022). Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7277523</p> <p>Boutin, G., Ólason, E., Rampal, P., Regan, H., Lique, C., Talandier, C., Brodeau, L., and Ricker, R.: Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework, The Cryosphere, 17, 617–638, https://doi.org/10.5194/tc-17-617-2023, 2023.</p> <p>Copernicus Climate Change Service (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: <a href="https://doi.org/10.24381/cds.adbb2d47">10.24381/cds.adbb2d47</a>.</p> <p>Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. <em>Q J R Meteorol Soc</em>. 2020; 146: 1999–2049. <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></p> <p> </p>
Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"
<div> <div> <div> <div> <p><strong>Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a></strong></p> <p><strong><span>Suppl. File 1: Protein Family Clusters.</span></strong></p> <p><strong><span>Suppl. File 2: Cluster GO enrichments/depletions. </span></strong></p> <p><strong><span>Suppl. File 3: The raw ID-CBR data with annotations. </span></strong></p> <p><strong><span>Suppl. File 4: ­ID-CBR Cluster membership.</span></strong></p> <p><strong><span>Each file has an explanatory header. </span></strong></p> <p> </p> </div> </div> </div> </div>
Raw NGS data for the study 'Spouse-to-spouse Transmission and Evolution of Hypervariable Region 1 and 5’ Untraslated Region of Hepatitis C Virus Analyzed by Next-generation Sequencing'
<p>This file contains the original next-generation sequencing data (raw sequences in fastq format) which were analyzed in the study titled: "Spouse-to-spouse Transmission and Evolution of Hypervariable Region 1 and 5’ Untraslated Region of Hepatitis C Virus Analyzed by Next-generation Sequencing".</p> <p> </p> <p> </p>
Origin and Structure of Electromagnetic Generator Regions at the Edge of the Electron Diffusion Region
<p>The data included here is for an article submission to the Physics of Plasmas: MMS special Issue. There are electric field, magnetic field, and particle data files from MMS (cdf files). There are files from Particle-in-Cell simulations (xdmf files, h5 files, and a log file with run info). Included here is the python notebook (ipynb file) used to analyze the simulation data, and two IDL files (generator_fig.pro and Energy_Flux.pro) that were used to produce the panel plots of MMS data in the article.</p>
Educational and workforce development through creation of programs in transportation to generate future careers for our students in Navajo Nation region
<p>Navajo Technical University (NTU) in New Mexico, has made a partnership with the Tran-SET regional center at Louisiana State University, LSU. We have provided learning and training the Native American students in transportation-related Industries to some of STEM students at NTU through these partnerships opportunities.<br> The program was designed and supported by Native American students from high school to STEM undergraduate students. The program was implemented accordingly with four main components: (1). Selected eligible and interested<br> Native American student to paid summer internships to the field and facilities of transportations industries and participate in research summer at LSU. (2). Directed the students to the transportation-related workshops and conducting at research NTU and they provided with and prepared them with the necessary background in transportation fields. (3). Several students were selected to research during the academic semester related to the field particularly, analyzing the causes of workforce shortages in the transportation industry. (4). We are planning to create and offer transportation certificate programs at NTU to train and expose the Native American students in transportation fields in the future if we get additional funding. We also proposed projects and conducted significant outreach activities in some of the local high schools and colleges. If successful we are planning to develop online courses, which would be provided for free to the Native American universities across the nation in order to make this program more sustainable.<br> Also, we are expected and planning to support 15-20 undergraduate and high school students per year through this partnership program. Other activities undertaken by NTU were the collaboration with different research, education, and outreach projects related to transportations undertaken by the NTU faculty partners. Some of the NTU STEM and pre-engineering students participated in the summer research internship programs, educational, laboratory, site visits as well as exposing students to the most technically-advanced Material research laboratories in Region 6 as well as state-of-the-art facilities at LSU. Some NTU students worked with local industries and agencies to enhance workforce development in the region.</p>
Data from: Explosion-generated infrasound recorded on ground and airborne microbarometers at regional distances
Recent work in deploying infrasound (low frequency sound) sensors on aerostats and free flying balloons has shown them to be viable alternatives to ground stations. However, no study to date has compared the performance of surface and free floating infrasound microbarometers with respect to acoustic events at regional (100s of kilometers) range. The prospect of enhanced detection of aerial explosions at similar ranges, such as those from bolides, has not been investigated either. We examined infrasound signals from three 1 ton TNT equivalent explosions using microbarometers on two separate balloons at ranges of 280 to 400 km and ground stations at ranges of 6.3 to 350 km. Signal celerities were consistent with acoustic waves traveling in the stratospheric duct. However, significant differences were noted between the observed arrival patterns and those predicted by an acoustic propagation model. Very low background noise levels on the balloons were consistent with previous studies that suggest wind interference is minimal on freely drifting sensors. Simulated propagation patterns and observed noise levels also confirm that balloon-borne microbarometers should be very effective at detecting explosions in the middle and upper atmosphere as well as those on the surface.
Massively parallel sequencing data of the HIV-1 pol region generated from the plasma of therapy-naïve chronically infected Brazilian blood donors
<p>The submitted massively parallel sequencing (MPS) data were partial data from the pol region of HIV-1 plasma viruses. Samples were obtained from 18 therapy-naive HIV-1 Brazilian blood donors with longstanding infection. Illumina ultra-deep sequencing technology (MiSeq platform) was used to generate the sequences. </p>
Load and renewable generation time series for selected regions in Africa and Eurasia
<p>The data set consist of three groups of yearly time series for 2003-2012 used in grid modelling. All time series are presented in .csv format. The data covers wind and PV power generation and load of 12 regions. The regions have 2 letter identifier and include the following:</p> <p>EU - EU countries</p> <p>NA - Algeria, Egypt, Libya, Morocco, Tunisia</p> <p>WA - Cameroon, Ghana, Nigeria</p> <p>EA - Ethiopia, Kenya, Tanzania, Uganda</p> <p>SA - South Africa</p> <p>RU - European part of Russia, Belarus, Ukraine</p> <p>MS - Arabic countries, Israel, Turkey</p> <p>IP - Bangladesh, India, Pakistan, Sri Lanka</p> <p>SB - Asian part of Russia, Kazakhstan, Turkmenistan, Uzbekistan</p> <p>IC - Indonesia, Malaysia, Philippines, Thailand, Vietnam</p> <p>CN - China</p> <p>FE - South Korea, Japan</p> <p> </p> <p>Wind power time series are derived from MERRA wind speed data for Enercon E-126 wind turbine.</p> <p>PV power time series are derived from MERRA solar surface irradiance data.</p> <p>Load time series are generated with the periodical function.</p> <p> </p> <p>Data for EU was derived within the project RESTORE 2050 as described in</p> <p>Kies, A., Chattopadhyay, K., von Bremen, L., Lorenz, E., & Heinemann, D. (2016). Simulation of renewable feed-in for power system studies.</p> <p>Data for all other nodes was derived as described in</p> <p>Krutova, M. et al., The smoothing effect for renewable resources in an Afro-Eurasian power grid, Adv. Sci. Res., 2017</p> <p>Load for EU is obtained from ENTSO-E and is not included in this data set.</p>
DNA sequence data generated using non-invasive feather and eggshell samples from the Grenada Dove for two gene regions: Cyt b and ND2
<p>As an island endemic with a decreasing population, the Critically Endangered Grenada Dove <em>Leptotila wellsi</em> is threatened by accelerated loss of genetic diversity resulting from ongoing habitat fragmentation. Small, threatened populations are difficult to sample directly but advances in molecular methods mean that non-invasive samples can be used. We performed the first assessment of genetic diversity of populations of Grenada Dove by a) assessing mtDNA genetic diversity in the only two areas of occupancy on Grenada, b) defining the number of haplotypes present at each site and c) evaluating evidence of isolation between sites. We used non-invasively collected samples from two locations: Mt Hartman (n=18) and Perseverance (n=12). DNA extraction and PCR were used to amplify 1,751 bps of mtDNA from two mitochondrial markers: NADH dehydrogenase 2 (<em>ND2</em>) and Cytochrome b (<em>Cyt b</em>). Haplotype diversity (<em>h</em>) of 0.4, a nucleotide diversity (π) of 0.00023 and two unique haplotypes were identified within the <em>ND2</em> sequences; a single haplotype was identified within the <em>Cyt b </em>sequences. Of the two haplotypes identified; the most common haplotype (haplotype A = 73.9%) was observed at both sites and the other (haplotype B = 26.1%) was unique to Perseverance. Our results show low mitochondrial genetic diversity and clear evidence for genetically isolated populations. The Grenada Dove needs urgent conservation action, including habitat protection and potential augmentation of gene flow by translocation in order to increase genetic resilience and diversity with the ultimate aim of securing the long-term survival of this Critically Endangered species. </p>
Where is Region 1 field-aligned current generated?
<p>This contains the original simulation data used by the paper 'Where is Region 1 field-aligned current generated?', which will appear in Journal of Geophysical Research - Space Physics. The data were obtained by the global MHD simulation (REPPU) with Level 6. </p> <p>The VTK file (Visualization Tookkit format) contains the physical variables in the magnetosphere at t = 244. 8 min as</p> <ul> <li>plasma pressure (P in nPa),</li> <li>velocity vector (V in km/s),</li> <li>current density vector (J in nA/m2),</li> <li>magnetic field vector (B in nT).</li> </ul> <p>The VTK file can be opened by Visualization Toolkit, and 3-D visualization software packages "VisIT", and "ParaView". </p> <p> </p> <p>The ASCII files named packet-position-P.txt and packet-position-Q.txt include lists of the position of the packet traced backward in time from the positions P and Q, respectively. </p> <p> </p> <p> </p>
RAD-seq generated single nucleotide polymorphisms resolve patterns of genetic diversity and structure of the freshwater mussel Ptychobranchus fasciolaris in glaciated and unglaciated regions of North America
<p>Included are the initial unfiltered SNP output from the STACKS pipeline, and the final filtered SNP dataset in VCF format used to do analysis in the manuscript titled "<span>RAD-seq generated single nucleotide polymorphisms resolve patterns of genetic diversity and structure of the freshwater mussel <em>Ptychobranchus fasciolaris </em>in glaciated and unglaciated regions of North America" which was submitted to <em>Hydrobiologia </em>in September 2024.</span></p>
Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis
<p>This dataset provides the MOVES-Matrix emission and energy use rates for the NCST project "Synthetic Fleet Generation and Vehicle Assignment to Synthetic Households for Regional and Sub-regional Sustainability Analysis" by the Georgia Tech research team.</p> <p> </p> <p>The abstract of the project is as follows.</p> <p><span>In this study, a modeling framework was developed to generate high-resolution synthetic fleets, for use with synthetic household modeling in activity-based travel models, by integrating various data sources. The synthetic households were generated by pairing household locations and demographic attributes, and synthetic fleets were assigned to the households so that travel demand model outputs would have vehicles associated with each model-predicted tour for energy and emissions analysis. The CO emissions were modeled for each vehicle and each link traversed by vehicles as predicted by the travel demand model, and the results of the synthetic fleet (by employing Monte Carlo simulations and Bootstrap techniques) were compared with those from standard regional and sub-regional fleet configurations. The results demonstrated that using a traditional sub-regional fleet scenario produced 30% higher predicted emissions than when the synthetic fleet was employed with predicted vehicle trips, and that using a regional average fleet (applied throughout the region) produced emissions that were more than 50% higher than synthetic fleet emissions. Lowest household emissions were associated with low-income and non-working households, and highest emissions were associated with moderate-income households and one-person high-income household groups. The results presented in the research are not necessarily conclusive, because the licensed vehicle data procured for Atlanta appear to be biased toward older vehicles. Model year penetration rates are accounted for in these analyses, but the authors believe that the variability in the registration mix for newer vehicles is likely underestimated in the data procured for these analyses. The authors conclude that access to statewide registration data will be required to remove potential biases that exist in licensed private data sets. Nevertheless, the study does demonstrate that properly pairing vehicle model years with the most active households (and their daily trips) significantly impacts energy and emissions analysis.</span></p> <p> </p>
Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States
<p>This dataset contains information of 432 study catchments used in the WRR paper "Regional Patterns and Physical Controls of Streamflow Generation across the Conterminous United States", including shapefile, hydrological signatures, catchment descriptors, and catchment classification (the primary class of each catchment).</p>
Code and data for publication "pyGRETA, pyCLARA, pyPRIMA: A pre-processing suite to generate flexible model regions for energy system models"
<p>This dataset contains the code of the three pre-processing tools <a href="https://github.com/tum-ens/pyGRETA">pyGRETA</a>, <a href="https://github.com/tum-ens/pyPRIMA">pyPRIMA</a> and <a href="https://github.com/tum-ens/pyCLARA">pyCLARA</a> and an examplary database for the scope of Austria.</p> <p>To run the code with full functionality additional data is needed. Check the documentation of the tools for further information.</p> <p> </p> <p>Sources for data can be found here: </p> <p>pyGRETA: https://pygreta.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyPRIMA: https://pyprima.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p> <p>pyCLARA: https://pyclara.readthedocs.io/en/stable/user_manual.html#recommended-input-sources</p>
Primary Versus Secondary Gravity Wave Responses at F-region Heights Generated by a Convective Source
<p>Simulation outputs for the paper "Primary Versus Secondary Gravity Wave Responses at F-region Heights Generated by a Convective Source" by Heale et al.</p> <p>The .mat file consist of 3D (t,x,z) arrays of the temperature perturbation (K) for the full amplitude (T_fullamp), 1/4 amplitude (T_quarteramp), and 1/100th amplitude (T_hundrethamp) simulations. The time resolution is 60 seconds and the spatial resolution is 1km. The .mat file also included an x array and height array (km)</p>
Basic helix-loop-helix pioneer factors interact with the histone octamer to invade nucleosomes and generate nucleosome depleted regions.
<p>This dataset includes all time traces from single-molecule experiments as well as raw gel images used in the manuscript. All time traces from single-molecule experiments and can be viewed using vbFRET (https://vbfret.sourceforge.net/). </p>
Simulation data for Upstream shift of generation region for whistler-mode rising-tone emission in the magnetosphere
<p>Simulation data for the article "Upstream shift of generation region for whistler-mode rising-tone emission in the magnetosphere" by Nogi and Omura (2023).</p> <p>Nogi, T., & Omura, Y. (2023). Upstream shift of generation region of whistler-mode rising-tone emissions in the magnetosphere. <em>Journal of Geophysical Research: Space Physics</em>, <em>128</em>, e2022JA031024. https://doi. org/10.1029/2022JA031024</p>
Load and generation time series for German federal states: Static vs. dynamic regionalization factors (data)
<p>This dataset contains regionalization factors for electricity generation and demand time series in Germany for the years 2019 - 2022. The factors can be used to distribute national generation and demand time series available from SMARD or ENTSO-E to federal state level. The methods underlying the regionalization factors are described in [1], with a focus on the year 2021. However, an extended version of the dataset covering the years 2019-2022 is also included for comprehensive analysis. Moreover, the dataset comprises the corresponding regionalized generation and demand time series at the federal state level of Germany. This time series has been generated using the provided distribution factors for the years 2019-2022 and corresponding generation and demand time series from SMARD [2]. Addtionally, the regionalization methodology for the distributed generation and demand data for the year 2021 has been supplemented with validation data, as described in [1]. This data has been cross-checked against the available SMARD Transmission System Operator (TSO) data. A description of the preprocessing required to obtain the TSO data comparison is provided in a separate .txt file. A PDF document has been prepared, which includes scatter plots that illustrate a comparison between actual and allocated generation per production type or demand data for TSOs on an hourly basis for the year 2021.</p> <p><strong>"static_regionalization_factors.2021[csv, xlsx]"</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Regionalization factors are based on share of generation capacity in each state (generation) or population and GDP (demand).</p> <p><strong>"dynamic_regionalization_factors_2021.[csv, xlsx]"<br> “dynamic_regionalization_factors_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Each row corresponds to a specific hour of the years 2019 through 2022. Regionalization factors are based on a combination of per unit generation data and share of generation capacity in each state, simulated renewable generation data based on spatio-temporal weather data and distribution of wind and solar generation capacities, and a regionalized load dataset for 2015 [3].</p> <p><strong>“time_series_federal_states_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to the allocated electricity generation or demand per federal state per production type or demand in units of MWh. Each row corresponds to a specific hour of the years 2019 through 2022. The regionalized generation and demand time series has been created by utilizing the dynamic regionalization factors provided in the dataset, in conjunction with the national electricity generation and demand data of Germany as provided by SMARD [2].</p> <p><strong>“TSO_actual.[csv, xlsx]”<br> “TSO_allocated.[csv, xlsx]”</strong></p> <p>Each column corresponds to the spatially aggregated electricity generation per type or demand per TSO in units of GWh. Each row corresponds to one hour of the year 2021. The TSOs in Germany do not hold direct responsibility for individual federal states, but rather for specific regions. In order to assess the validity of the regionalization methodology employed, it was necessary to generate data at the NUTS3 level and subsequently aggregate it to correspond with the relevant TSOs. The data is pre-processed at NUTS3 level and then undergoes the same methodology as outlined in [1]. The preprocessing steps required to map the installed capacity to the TSO level are explained in the accompanying .txt file. The allocated generation and demand data are aggregated to correspond to the TSO level using a shapefile of mapped regions in Germany that correspond to the TSOs [4]. The actual TSO data is generation and demand as published by SMARD [2]. The accompanying PDF presents scatter plots that showcase the actual vs allocated hourly generation types or demand per TSO, expanding on the information provided in the article.</p> <p>[1] M. Sundblad, T. Fürmann, A. Weidlich and M. Schäfer, "<a href="https://arxiv.org/abs/2304.02951">Load and generation time series for German federal states: Static vs. dynamic regionalization factors</a>," <em>2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)</em>, Aachen, Germany, 2023, pp. 1-6, doi: 10.1109/OSMSES58477.2023.10089686.</p> <p>[2] Bundesnetzagentur | <a href="https://www.smard.de/home">SMARD.de</a></p> <p>[3] Matthias Kühnbach, Anke Bekk, and Anke Weidlich (2021). <a href="https://www.forecast-model.eu/forecast-en/content/publications.php">Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany</a>. Energy Strategy Reviews, 34:100609, 20</p> <p>[4] Frysztacki, Martha Maria. (2023). Mapping of districts to control zones of German Transmission System Operators (TSOs) (v0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7530196">https://doi.org/10.5281/zenodo.7530196</a></p>
Data from: Explosion-generated infrasound recorded on ground and airborne microbarometers at regional distances
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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