Find research datasets worth reusing

Campi Flegrei cGPS network – RINEX data quality control (2000 – 2019)

For each cGPS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information: <ol> <li>cGPS station name (Name),</li> <li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GPS week (Week),</li> <li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GPS week (Week),</li> <li>the start and end times of the window (time format is year month day hour min),</li> <li>window time laps (Hrs),</li> <li>observation interval (OI),</li> <li>the number of possible observations (#expt) above the elevation mask,</li> <li>the number of complete observations (#obs),</li> <li>the ratio of complete to possible observations as a percent (DCP),</li> <li>the RMS “multipath combinations” values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP22) rounded to two decimal points,</li> <li>cycle slips (CS),</li> <li>the ratio of complete observations to cycle slips (obs/CS).</li> </ol> A full description of cGPS network is reported in: - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725. Please cite this when using the dataset

Ischia cGPS network – RINEX data quality control (2001 – 2019)

Bacterial Community Diversity and Bacterial Interaction Network in Eight Mosquito Species

Sequences of the V4 region of 16S rRNA from 111 mosquito samples.  This dataset was used for analysis of bacterial diversity and bacterial interaction network in eight mosquito species. Result of the analysis is in the article "Bacterial Community Diversity and Bacterial Interaction Network in Eight Mosquito Species" (Genes 2022, 13(11), 2052; https://doi.org/10.3390/genes13112052).

Plasticization of a Semicrystalline Metallosupramolecular Polymer Network

Data set divided in folders according to the Figures in the manuscript and supporting information

opencc-by-4.0Dec 2021View details →

Data supporting: Impacts of extreme climatic events on trophic network complexity and multidimensional stability

Data used to produce the results presented in the manuscript entitled "Impacts of extreme climatic events on trophic network complexity and multidimensional stability", published in the journal "Ecology". The data were obtained from an outdoor pond mesocosm experiment where freshwater communities were exposed to two different heatwave scenarios.

opencc-by-4.0Mar 2022View details →

Dynamic rewiring of electrophysiological brain networks during learning

This space is dedicated to the dataset used in the manuscript "Dynamic rewiring of electrophysiological brain networks during learning" under peer-reviewing process.

opencc-zeroNov 2022View details →

Habitat loss shapes the structure and species roles in tropical plant-frugivore networks

Habitat loss is a global threat to biodiversity with pervasive effects on species and populations. These impacts may generate cascading effects on ecological processes propagating across ecological networks. Thus, understanding how habitat loss affects ecological networks is fundamental for conservation. We used a database of 25 plant-frugivore networks distributed across the whole Brazilian Atlantic Forest to understand how landscape-scale habitat loss shapes network structure, robustness, species role and traits related to seed dispersal. We compared whether these network properties have linear or non-linear relationships and used centrality metrics and indirect effects to evaluate if habitat loss change the role of species in plant-frugivore networks. We found linear and non-linear relationships with negative effects of habitat loss on the network structure. As a consequence of shifts in species richness and number of links, the number of interactions and the proportion of possible interactions observed (connectance) were negatively associated with habitat loss. In contrast, nestedness increased with habitat loss. Network robustness, mean bill width and mean seed size were not significantly related to habitat loss. In addition to changes in interaction patterns at network level, habitat loss also favors changes in interaction among species, shifting the species playing central roles in network organization or contributing to indirect effects in the networks. In forested landscapes, obligate frugivores are the main central species in the network, and the ones potentially contributing to indirect effects, while in deforested landscapes these roles are fulfilled by occasional frugivores. Thus, our results emphasize the widespread effect of habitat loss on plant-frugivore systems, adding evidence that its pervasive effects on biodiversity also proliferate on mutualistic interactions with negative consequences for seed dispersal that potentially go beyond the direct pairs of interacting species.

opencc-zeroNov 2022View details →

The social network of target of rapamycin complex 1 in plants

The target of rapamycin complex 1 (TORC1) is a highly conserved serine–threonine protein kinase crucial for coordinating growth according to nutrient availability in eukaryotes. It works as a central integrator of multiple nutrient inputs such as sugar, nitrogen, and phosphate and promotes growth and biomass accumulation in response to nutrient sufficiency. Studies, especially in the past decade, have identified the central role of TORC1 in regulating growth through interaction with hormones, photoreceptors, and stress-signaling machinery in plants. In this review, we comprehensively analyse the interactome and phosphoproteome of the Arabidopsis TORC1 signaling network. Our analysis highlights the role of TORC1 as a central hub kinase communicating with the transcriptional and translational apparatus, ribosomes, chaperones, protein kinases, metabolic enzymes, and autophagy and stress response machinery to orchestrate growth in response to nutrient signals. This analysis also suggests that along with the conserved downstream components shared with other eukaryotic lineages, plant TORC1 signaling underwent several evolutionary innovations and co-opted many lineage-specific components. Based on the protein–protein interaction and phosphoproteome data, we also discuss several uncharacterized and unexplored components of the TORC1 signaling network, highlighting potential links for future studies.

opencc-by-4.0Jun 2021View details →

An Improved Tandem Neural Network Architecture for Inverse Modeling of Multicomponent Reactive Transport in Porous Media

This data includes the training and testing dataset for DNN design and the observation data of synthetic example for validation.  The code of TNNA-AUS inversion method.

opencc-by-nc-4.0Nov 2022View details →

datset of "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset"

This dataset accompanies the manuscript: "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset." DOI: (https://doi.org/10.1101/2022.10.29.22281666) by Flavia Venetucci Gouveia1,2,3*&dagger;,Jürgen Germann4,5&dagger;, Gavin JB Elias4,5, Alexandre Boutet4,6, Aaron Loh4,5, Adriana Lucia Lopez Rios7,8, Cristina V Torres Diaz9, William Omar Contreras Lopez10,11, Raquel CR Martinez3,12, Erich T Fonoff13, Juan C Benedetti-Isaac14,  Peter Giacobbe 2,15,16, Pablo M Arango Pava17, Han Yan5,18, George M Ibrahim5, 18,19,20, Nir Lipsman2,5,15, Andres M Lozano4,5, Clement Hamani2,5,15* 1. Neuroscience and Mental Health, Hospital for Sick Children Research Institute; Toronto, Canada  2. Sunnybrook Research Institute; Toronto, Canada 3. Division of Neuroscience, Sírio-Libanês Hospital; São Paulo, Brazil 4. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada 5. Division of Neurosurgery, Department of Surgery, University of Toronto; Toronto, Canada 6. Joint Department of Medical Imaging, University of Toronto; Toronto, Canada 7. Department of Functional and Stereotactic Neurosurgery, University Hospital San Vicente Fundación,     Medellín, Colombia 8. Department of Functional and Stereotactic Neurosurgery, San Vicente Fundación, Rionegro, Colombia 9. Department of Neurosurgery, University Hospital La Princesa; Madrid, Spain 10. Nemod Research Group, Universidad Autónoma de Bucaramanga; Bucaramanga, Colombia 11. Division of Functional Neurosurgery, Department of Neurosurgery, FOSCAL Clinic; Bucaramanga,       Colombia 12. LIM 23, Institute of Psychiatry, School of Medicine, University of São Paulo; São Paulo, Brazil 13. Department of Neurology, Integrated Clinic of Neuroscience, School of Medicine, University of São Paulo;       São Paulo, Brazil. 14. Stereotactic and Functional Neurosurgery Division of the International Misericordia Clinic; Barranquilla,      Colombia 15. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre; Toronto, Canada 16. Department of Psychiatry, University of Toronto; Toronto, Canada 17. Servicio de Neuocirugia Funcional y Esterotaxia, Clinica Comuneros Bucaramanga, Clinica Desa y Clinica       Dime Neurocardiovascular de Cali; Clinica Nueva del Lago, Bogota, Colombia. 18. Division of Neurosurgery, The Hospital for Sick Children; Toronto, Canada 19. Institute of Biomedical Engineering, University of Toronto; Toronto, Canada 20. Institute of Medical Science, University of Toronto; Toronto, Canada &dagger; Flavia Venetucci Gouveia and Jürgen Germann contributed equally to this work and share first authorship. * Corresponding Author: Dr. Flavia Venetucci Gouveia. Neuroscience and Mental Health, Hospital for Sick Children Research Institute. 686, Bay Street, Toronto, ON, M5G 0A4, Canada. flavia.venetuccigouveia@sickkids.ca * Corresponding Author: Dr. Clement Hamani. Sunnybrook Research Institute. 2075 Bayview Ave, S126. Toronto, ON, M4N3M5, Canada. clement.hamani@sunnybrook.ca It contains a zip folder ("estimated_binary_Volume_of_Tissue_Activated.zip") with one file (in nii.gz format) per patient estimating the Volume of Activated Tissue for that patient (the estimated 'reach' of the active DBS stimulation) and a demographics file. The case numbers are identical to Table 1 in the manuscript.

FI, Remote Driving, Remote driving in a redundant network environment

Use Case Category: Remote Driving User Story: Remote driving in a redundant network environment Location: Finnish (FI) trial site According to 3GPP TS 22.186 R16, Remote Driving “enables a remote driver or a V2X application to operate a remote vehicle for those passengers who cannot drive themselves or a remote vehicle located in dangerous environments. For a case where variation is limited, and routes are predictable, such as public transportation, driving based on cloud computing can be used. In addition, access to cloud-based back-end service platform can be considered for this use case group”. User Story: Remote driving in a redundant network environment The remote driving of an SAE L4 vehicle is enabled by a V2N connection between the vehicular Onboard Unit (OBU) and a remote server hosting V2N applications, in this case the remote driving application is used by the remote human operator. The V2N connection transfers the sensor data feed (high resolution perception data) from the vehicle to the remote human operator (in the uplink direction). The sensor data provides the human operator a “driver’s view” allows the human operator to send appropriate command messages (e.g. command trajectories) back to the L4 vehicle (in the downlink direction). The remote control/driving of vehicle presents stringent requirements on connection between the vehicle and the Remote Operations Centre (ROC). These requirements include the need to ensure that human operator always maintains connectivity to the vehicle they control, the latency minimized to ensure timeliness of the downlink control messages from the human operator; and the uplink capacity is guaranteed for the transmission of the sensor data feeds from the vehicle. The whole control loop needs to keep tight. The accumulated delay from: sensor reading, sensor data processing, uplink, data visualization, manual control, control signal reading, downlink, and control signal processing to control must be kept low for direct control (depending on speed and dynamics of the vehicle). Furthermore, the vehicle should be aware of any latency issues, so that the operational speed could be adjusted accordingly. Remote driving (and other V2X use cases) will occur in multi-operator scenarios in legacy 4G [3GPP TR 36.885]4 and future 5G [3GPP 38.885]5 contexts, whereby, the L4 vehicle trajectory is an area covered by multiple public land mobile networks (PLMNs) or transitions between two PLMN coverage areas. The remote driving user story also underlines safety aspects and need for the L4 vehicle to maintain reliable/uninterrupted V2N connectivity. In practice, a vehicle’s home PLMN (original serving network) may have locations with poor or non-existent coverage, or then experience V2N connection degradation or failure due to overloading, network failure etc. To guarantee availability V2N connectivity for critical L4 vehicle services (such as, remote driving), the possibility of the vehicle to seamlessly switch to (or simultaneously utilise) a visited PLMN ensures safer operation of the vehicle regardless of instantaneous network conditions.

Test dataset for "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks"

Data corresponding to test dataset used in Aberra AS, Lopez A, Grill WM, Peterchev AV. (2022). "Rapid estimation of cortical neuron activation thresholds by transcranial magnetic stimulation using convolutional neural networks". bioRxiv. Dataset includes: <ul> <li>simnibs/ - SimNIBS mesh and E-field solution file used in test dataset (posterior-anterior TMS of M1 in ernie example mesh, meshed with mri2mesh pipeline)</li> <li>layer_data/ - surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li> <li>nrn_sim_data/ - Thresholds from NEURON simulations for all 25 model neurons included in the study, each at 4,999-5,000 positions and 12 azimuthal orientations ("ground truth" for CNN) </li> <li>cell_data/ - Coordinates and morphology information for all model neurons</li> <li>weights/  - Trained 3D convolutional neural networks for estimating neuron model-specific TMS thresholds given input E-field distributions on a 3D grid (see code/manuscript for dimensions)</li> <li>est_data/ - Output of trained CNNs on all E-field data for test dataset  </li> </ul>

Genova - Rain monitoring network

This dataset is published into the Genova Open Data Portal and imported in the UNaLab Open Data to monitor weather data from Genova city

opencc-zeroNov 2022View details →

Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset

Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.

opencc-by-4.0Aug 2022View details →

SAGE Sky Images and model checkpoints for "Let's Unleash the Network Judgement: A Self-supervised Approach for Cloud Image Analysis" paper

This dataset contains: <ul> <li>84912 images collected from our camera (a 6MP Fisheye Sky facing camera with 192 degrees horizontal and vertical angle view). Each image is a JPEG image, JFIF standard 1.01, aspect ratio, density 1x1, segment length 16, baseline, precision 8, 2048x2048, components 3. Images were recorded on October–December 2021. The dataset is clusterized with images belonging to 20 different clusters/classes. We used PCA_MLE plus SOM to produce such clusterization. Files Cluster_*.zip</li> <li>11 model checkpoints from the training process. Files checkpoint*.pth and log.txt</li> <li>Output feature vectors and their corresponding file names resulting from inference . File features.zip</li> <li>The clusterization produced on <a href="https://github.com/CV-Application/WSISEG-Database ">WSISEG-Database</a>. File README.txt </li> </ul>

opencc-by-4.0May 2022View details →

Code and extensive data for training neural networks for radiation, used in "Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0""

Data and code used in a paper submitted to JAMES titled : Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System 1) The files ml_training_*.7z contain extensive datasets (in NetCDF format) for training neural network versions of the RRTMGP gas optics scheme as described in the paper. The datasets are read by <a href="https://github.com/peterukk/rte-rrtmgp-nn/blob/main/examples/rrtmgp-nn-training/ml_train.py">ml_train.py.</a> 2) The ML datasets were in turn generated using the input profiles (in NetCDF format) inside inputs_to_RRTMGP.zip by running the Fortran programs <code>rrtmgp_sw_gendata_rfmipstyle.F90 and rrtmgp_lw_gendata_rfmipstyle.F90 </code>in rte-rrtmgp-nn/examples/rrtmgp-nn-training, which call the RRTMGP gas optics scheme, The input profiles contain millions of columns, hundreds of perturbation experiments (including hypercube-sampled gas concentrations), are derived from several different data sources (including CAMS reanalysis, GCM, and CKDMIP-MMM), and span present-day, preindustrial, and future atmospheric conditions. They could be used to generate training data for developing emulators of the full RTE+RRTMGP radiation scheme, not just gas optics (see nn_dev on the <a href="https://github.com/peterukk/rte-rrtmgp-nn">RTE+RRTMGP-NN repository on Github</a>, used in a previous paper where different emulation methods were compared) 3) The Fortran and Python code used for data generation and NN training are found in<a href="https://github.com/peterukk/rte-rrtmgp-nn/tree/main/examples/rrtmgp-nn-training"> rte-rrtmgp-nn/examples/rrtmgp-nn-training </a>on the main branch on Github; an archived version is also included here (rte-rrtmgp-nn-2.0.zip). See the readme in the above sub-directory for further information.

opencc-byDec 2021View details →

Network pharmacology and UPLC-Q-TOF-MS-based method to explore the underlying mechanism of Shaoyao–Gancao decoction acting on atopic dermatitis

This study aimed to investigate the immunomodulation and anti-inflammatory effects of SGD and further explore its pharmacodynamics material basis and potential molecular mechanisms on atopic dermatitis (AD) disorder.

opencc-zeroDec 2022View details →

Mapping trait versus species turnover reveals spatiotemporal variation in functional redundancy and network robustness in a plant‐pollinator community

1. Functional overlap among species (redundancy) is considered important in shaping competitive and mutualistic interactions that determine how communities respond to environmental change. Most studies view functional redundancy as static, yet traits within species – which ultimately shape functional redundancy – can vary over seasonal or spatial gradients. We therefore have limited understanding of how trait turnover within and between species could lead to changes in functional redundancy or how loss of traits could differentially impact mutualistic interactions depending on where and when the interactions occur in space and time. 2. Using an Arctic bumblebee community as a case study, and 1,277 individual measures from 14 species over three annual seasons, we quantified how inter- and intraspecific body-size turnover compared to species turnover with elevation and over the season. Coupling every individual and their trait with a plant visitation, we investigated how grouping individuals by a morphological trait or by species identity altered our assessment of network structure and how this differed in space and time. Finally, we tested how the sensitivity of the network in space and time differed when simulating extinction of nodes representing either morphological trait similarity or traditional species groups. This allowed us to explore the degree to which trait-based groups increase or decrease interaction redundancy relative to species-based nodes. 3. We found that i) groups of taxonomically and morphologically similar bees turn over in space and time independently from each other, with trait turnover being larger over the season; ii) networks composed of nodes representing species versus morphologically similar bees were structured differently; and iii) simulated loss of bee trait groups caused faster coextinction of bumblebee species and flowering plants than when bee taxonomic groups were lost. Crucially, the magnitude of these effects varied in space and time, highlighting the importance of considering spatiotemporal context when studying the relative importance of taxonomic and trait contributions to interaction network architecture. 4. Our finding that functional redundancy varies spatiotemporally demonstrates how considering the traits of individuals within networks is needed to understand the impacts of environmental variation and extinction on ecosystem functioning and resilience.

opencc-by-4.0Dec 2022View details →

Cytoscape files - Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation

Cytoscape files of pathway enrichment analyses and PPI mapping associated with manuscript https://www.nature.com/articles/s41467-023-39241-7 Systems-level analyses of protein-protein interaction network dysfunctions via epichaperomics identify cancer-specific mechanisms of stress adaptation Anna Rodina1,11, Chao Xu1,11, Chander S. Digwal1,11, Suhasini Joshi1,11, Anand R. Santhaseela1, Sadik Bay1, Swathi Merugu1, Aftab Alam1, Pengrong Yan1, Chenghua Yang1,12, Tanaya Roychowdhury1, Palak Panchal1, Liza Shrestha1, Yanlong Kang1, Sahil Sharma1, Yogita Patel2, Justina Almadovar1, Adriana Corben3,13, Mary Alpaugh1,14, Shanu Modi4, Monica L. Guzman5, Teng Fei6, Tony Taldone1, Stephen D. Ginsberg7,8, Hediye Erdjument-Bromage9, Thomas A. Neubert9, Katia Manova-Todorova10, Jason C. Young2, Meng-Fu Bryan Tsou10, Tai Wang1,*, Gabriela Chiosis1,4,* Abstract Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of first-in-class chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based ‘omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.