Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
81
datasets available to search
ShareScore release 0.9.0
Dataset results
81 results for “SUMO”
Data set for the journal article: Site-Specific Protein Ubiquitylation Using an Engineered, Chimeric E1 Activating Enzyme and E2 SUMO Conjugating Enzyme Ubc9
<p>Mutations observed in evolved chimeric E1 variants. Top row (1.X to 4.X) describes rounds of evolutions with respective variants in the round. </p> <p>Residues that appear to be enriched are highlighted with gray fill. Star (★) marks residues subjected to saturation mutagenesis in the round 4.</p>
Generation of synthetic, realistic vehicular traces for three access highways of Quito using SUMO
<p>These files present the maps of three access highways of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in July 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles for each road considerings the statistics from traffic authority of Quito</li> <li>Configuration of the 5 generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular traces</li> </ul>
Synthetic, realistic vehicular traces for financial district of Quito using SUMO
<p>These files present the map of the financial district of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in November 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles considering the statistics from traffic authority of Quito</li> <li>Configuration of the generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular trace</li> </ul> <p>In addition, we include the scripts and program to analyze vehicular traces from the point of view of vehicular communications. The scripts were developed using awk, bash and R. The software to compute connectivity matrix was developed with C++</p>
Traffic forecasting with Virtual Induction Loops - SUMO simulation dataset
<p>This repository is associated with my doctoral dissertation titled, "<strong>Smartphone based applications for Road Traffic Telematics</strong>". In particular this repository serves as the basis of Chapter 7 titled, "<strong>Traffic forecasting with Virtual Induction Loops (VIL)</strong>". The basic idea is to validate a traffic forecasting system which uses machine learning techniques on the simulation of real traffic flows on a real intersection in the City of Turin. This dataset contains simulation output from SUMO software for 56 real days between the months of October-2017 to April-2018. Details about these days are available in my thesis. For each day, 3 output files are available. Here is the description and naming convention:</p> <ol> <li>M1_100seed_100pr_dump.csv (This is the data dump file from SUMO. It contains flows of every single vehicle that was simulated. Naming convention is day_seed_vilPenetrationRate_dump.csv)</li> <li>M1_100seed_ilNorth_100pr.xml (This is the output from a simulated induction loop for Northbound traffic. Naming convention is day_seed_ilNorth_vilPenetrationRate.xml)</li> <li>M1_100seed_ilSouth_100pr.xml (This is the output from a simulated induction loop for Southbound traffic. Naming convention is day_seed_ilSouth_vilPenetrationRate.xml)</li> </ol> <p>For further details, please refer to my thesis.</p>
SUMO intersection model and vehicle trip data
<p>There are two parts of the data: 1) a SUMO model of a typical intersection that consists of 4 approaches, each of which consists of 3 movements (left turn, right turn, and straight); 2) the vehicle trip information data generated by SUMO under different volume files, which is used to train the intersection signal control algorithm.</p> <p>The SUMO model contains five ".xml" files (node, edge, connection, net, and additional files) which are used to construct and configure the model. One can refer to the official SUMO tutorial for the format and functions of these files: (<a href="https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo">https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo</a>) </p> <p>The vehicle trip data is generated by SUMO as an output (which is specified in ".sumocfg" file). One can refer to the official tutorial (<a href="https://sumo.dlr.de/wiki/Simulation/Output/TripInfo">https://sumo.dlr.de/wiki/Simulation/Output/TripInfo</a>) to understand the data format.</p> <p>Note that readers capable to read ".xml" files like Notepad++ are required to read the SUMO model and vehicle trip data.</p>
Synthetic dataset for mobile wireless networks with SUMO -- aggregated traces
<p>Aggregated dataset from a published wireless dataset generator base in SUMO mobility model. </p> <p> </p> <p>This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with reference UIDB/50021/2020 and SFRH/BD/132053/2017.</p>
SUMO models
<p>SUMo models compressed file is composed of two models: source separation and target separation. These models were trained using the OSTrain dataset (available as a Zenodo DOI at 10.5281/zenodo.8362616) using gradient boosting decision trees (xgboost library in Python). The source separation model was trained with the hyperparameters {colsample_bylevel = 0.7; colsample_bytree = 0.6; gamma = 0.1; learning_rate = 0.1; max_depth = 3; n_estimators = 800; reg_alpha = 1e-5; reg_lambda = 0.1}, and the target separation model was trained with the hyperparameters {colsample_bylevel = 0.4; colsample_bytree = 0.6; gamma = 0.1; learning_rate = 0.1; max_depth = 15; n_estimators = 800; reg_alpha = 1e-5; reg_lambda = 1}. These hyperparameters were obtained using Bayesian optimization and validated using the OSValidate dataset (available as a Zenodo DOI at 10.5281/zenodo.8360991).</p> <p> </p> <p>Once decompressed, the source separation model will be at ./models/source_separation/source_separation_model_bayesian_optimization.joblib, and the target separation model will be at ./models/target_separation/target_separation_model_bayesian_optimization.joblib. The files at ./models/source_separation/log_parameters.txt and at ./models/target_separation/log_parameters.txt contain the log for all the hyperparameter combinations considered during the Bayesian optimization process.</p>
sumo_features_for_deepcoffea
<p>SUMo datasets OSTrain, OSValidate, OSTest converted to DeepCoFFEA's format.</p>
Seasonal Foliar Respiration, Assimilation, Growth, Non-structural Carbohydrates and Water Potential data from Survival/Mortality (SUMO) Experiment, Piñon /Juniper Woodland, Los Alamos NM: 2013
These data were collected as part of an ecosystem temperature and precipitation manipulation experiment on mature piñon (Pinus edulis) and juniper (Juniperus monosperma) trees near Los Alamos, New Mexico, USA. The experiment used open-top chambers (OTCs) to manipulate temperature and under-canopy troughs to remove precipitation to test various hypotheses. The data presented herein contain measures of foliar carbon economy (respiration, assimilation, growth, non-structural carbohydrates) and water status (pre-dawn foliar water potential) in the growing season (April – October) of 2013.
Athens Car Traffic data SUMO
Open the record for dataset details and reuse information.
SUMO input route files used for experimental testing in Master's dissertation
<p>SUMO Simulator input route files that were used to generate an edge lane emissions file for each of the two scenarios considered in my Master's dissertation experimental testing.</p>
Dataset related to article Super-resolution study of PIAS SUMO E3-ligases in hippocampal and cortical neurons
<p>Immagini ottenute con la sim e il confocale dei neuroni ippocampali e corticali presenti nelle immagini. Grafici relativi ai coefficenti di localizzazione</p>
F2C precollected dataset in SUMO environment
<p>The dataset is collected by training pure SAC for 1,000,000 steps in SUMO environment.</p>
Distinct Adaptive Mechanisms Drive Recovery from Aneuploidy Caused by Loss of the Ulp2 SUMO Protease [WGS]
GEO Series GSE121899. Saccharomyces cerevisiae. 3 samples. Type: Other.
Liver transcriptome analysis of obese mice expressing a SUMO-deficient Prox1 (K556R) mutant in hepatocytes
GEO Series GSE237593. Mus musculus. 29 samples. Type: Expression profiling by high throughput sequencing.
Most cancer cell lines give very modest transcriptional regulation following treatment with the SUMO activating enzyme inhibitors TAK-981 or ML-792
GEO Series GSE173116. Mus musculus; Homo sapiens. 57 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiles of rescue with wild type or SUMO double mutant TRIM24
GEO Series GSE77140. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
The UBC9 E2 SUMO conjugating enzyme binds the PR-Set7 histone methyltransferase to facilitate target gene repression
GEO Series GSE30361. Homo sapiens. 4 samples. Type: Expression profiling by array.
Transcriptome sequencing of K562 cells stably expressing SUMO negative MYB
GEO Series GSE124542. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Rhes, a striatal enriched protein, regulates SUMOylation of nuclear proteins and gene expression in a SUMO-dependent manner
GEO Series GSE150990. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
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.