Find research datasets worth reusing

River network and hydro-geomorphological parameters at 1/12° resolution for global hydrological and climate studies

Global scale river routing models (RRMs) are commonly used in a variety of studies, including studies on the impact of climate change on extreme flows (floods and droughts), water resources monitoring or large scale flood forecasting. Over the last two decades, the increasing number of observational datasets, mainly from satellite missions, and the increasing computing capacities, have allowed better performances of RRMs, namely by increasing their spatial resolution. The spatial resolution of a RRM corresponds to the spatial resolution of its river network, which provides flow direction of all grid cells. River networks may be derived at various spatial resolution by upscaling high resolution hydrography data. This paper presents a new global scale river network at 1/12° derived from the MERIT-Hydro dataset. The river network is generated automatically using an adaptation of the Hierarchical Dominant River Tracing (DRT) algorithm, and its quality is assessed over the 70 largest basins of the world. Although this new river network may be used for a variety of hydrology-related studies, it is here provided with a set of hydro-geomorphological parameters at the same spatial resolution. These parameters are derived during the generation of the river network and are based on the same high resolution dataset, so that the consistency between the river network and the parameters is ensured. The set of parameters includes a description of river stretches (length, slope, width, roughness, bankfull depth), floodplains (roughness, sub-grid topography) and aquifers (transmissivity, porosity, sub-grid topography). The new river network and parameters are assessed by comparing the performances of two global scale simulations with the CTRIP model, one with the current spatial resolution (1/2°) and the other with the new spatial resolution (1/12°). It is shown that CTRIP at 1/12° overall outperforms CTRIP at 1/2°, demonstrating the added value of the spatial resolution increase. The new river network and the consistent hydro-geomorphology parameters may be useful for the scientific community, especially for hydrology and hydro-geology modelling, water resources monitoring or climate studies.

opencc-by-4.0May 2022View details →

Data Sets for SNR Estimation in Flexible Optical Networks: Lightpath, Link, and Span Levels

These data sets have been generated based on the analytic models [1,2] to estimate signal to the noise ratio (SNR) for spans, links, and lightpaths of a Flexible Optical Network (FON) over standard single-mode fiber (SSMF). For PM-BPSK and PM-QPSK modulation format levels, equation 41-43 [1], and for PM-8-64QAM modulation format levels, equation 7.32 [2], are applied. [1] P. Poggiolini, G. Bosco, A. Carena, V. Curri, Y. Jiang and F. Forghieri, "The GN-Model of Fiber Non-Linear Propagation and its Applications," in Journal of Lightwave Technology, vol. 32, no. 4, pp. 694-721, Feb.15, 2014, DOI:  10.1109/JLT.2013.2295208. [2]  P. Poggiolini, Y. Jiang, A. Carena and F. Forghieri, "Analytical modeling of the impact of fiber non-linear propagation on coherent systems and networks" in Enabling Technologies for High Spectral-Efficiency Coherent Optical Communication Networks, New York, NY, USA:Wiley, pp. 247-310, 2016.

opencc-by-4.0May 2022View details →

Neural network dataset, script and model

This data was collected from several sources and compiled into a single text file called NN.txt Ten quiet and five disturbed days in each month from 2009 to 2019 were used to develop the dataset. These days were got from the list of International Q and D days accessed on the website of the World Data Centre of Geomagnetism, Kyoto (<a href="https://wdc.kugi.kyoto-u.ac.jp/qddays/index.html">https://wdc.kugi.kyoto-u.ac.jp/qddays/index.html</a>). The dataset has seventeen columns defined as Date, Year (Y), Day of the year (DOY), hour of the day (HH), cosine and sine components of the day of the year for annual variation (DAC and DAS), cosine and sine components of the day of the year for semi-annual variation (DSC and DSS), cosine and sine components of the hour for daily variation (HRC and HRS), geographical coordinates (Lat, Lon), geomagnetic coordinates (Glat, Glon),  Dst Index, F10.7 Index, TEC and sunspot number (SSN). The geographical coordinates were converted to geomagnetic coordinates using quasi dipole coordinates (Emmert et al., 2010; Richmond, 1995). Dst index, F10.7 index, and SSN were downloaded from the OmniWeb database (https://omniweb.gsfc.nasa.gov/form/dx1.html). Jason TEC data was downloaded from the FTP access of the CEDAR Madrigal database (http://cedar.openmadrigal.org/ftp/).  The data resolution was an 18-second interval.  It should be noted that any day that had a missing value was eliminated from the database. The data downloaded from Omni web was on a resolution of 1 hour and it was put at an 18-second interval by repeating the same value. The TEC data from the CEDAR Madrigal database is in a second interval and therefore it was averaged at an 18-second interval.  The dataset was trained with MATLAB software, the MATLAB script (NN script) is attached. The output was a Neural network model also attached

opencc-by-4.0Dec 2021View details →

PMD hypomethylation human (hg19) neural network scores

Global loss of DNA methylation in mammalian genomes occurs cumulatively as a mitotic process during aging and cancer, primarily in Partially Methylated Domains (PMDs). It has been shown that local sequence context (100bp) has a strong effect on the rate of demethylation of individual CpG dinucleotides within PMDs. Here, we train a deep learning model to characterize this sequence dependence further, finding that methylation loss can be predicted from a CpG’s 150bp sequence context alone with an AUC of 0.95. We use re-methylation rates of newly synthesized DNA to show that CpGs with fast-loss sequence context are inefficiently re-methylated. Interestingly, we find that the 10% of CpGs predicted to have the “slowest” rate of loss lose almost no DNA methylation in healthy cell types. These same slow-loss CpGs lose a significant amount of DNA methylation in cancer, suggesting that they could be responsible for deregulation of genes and transposable elements that are associated with DNA hypomethylation in cancer. This directory contains the Nov. 18, 2020 version of the human (hg19) CpG hypomethylation Neural network scores in a single tab-delimited (bedgraph) file: multitissue-nn-scores.allCGs.0based.hg19.bedgraph.gz with the following columns: 1: chromosome (hg19) 2: start coord (hg19, 0-based) 3: end coord (hg19, 0-based) 4: multi-tissue NN score (0-1). Close to 0 is classified as slow-loss CpG, close to 1 is classified as fast loss CpG5: Num CpGs in 150 bp window (including central CpG, so minimum is 1).   The full version of the NN scores with additional details are in the file zhou-bian.allCGs.1based.hg19.tsv.gz Each row is a CG which provides (1) chromosome, (2) the corresponding C coordinate on the forward (watson) strand of the reference genome in one-based coordinates, (3) Neural network score, (4) number of CpGs within the 150bp sequence centered on this CpG, including the center CpG, (5) CpG is within a CpG island (0, no; 1, yes), CpG is within ENCODE blacklist (0, no; 1, yes)  Here the CpG islands are the union set of Irizarry (Irizarry et al. 2009, Nat Genet), Takai-Jones (Takai et al. 2002, PNAS), Gardner-Gardin CGIs (Gardner-Gardin et al. 1987, J Mol Biol.). The blacklist was downloaded from https://github.com/Boyle-Lab/Blacklist/tree/master/lists. Additional files are included here: zhou_pmds.0based.hg19.bed.gz: Input PMD CpGs from the Zhou (multi-tissue) dataset bian_pmds.crc01.0based.hg19.bed.gz: Input PMD CpGs from the Bian (intra-tumor) dataset zhou_bian_train_test_data.tar.gz: All training and test CpGs, including labels and sequence windows.

opencc-by-4.0Nov 2020View details →

A database of plant-pollinator networks

This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases. Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - CIFAR10

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on CIFAR10. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "cifar_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - STL10 - Raw Datasets

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from STL10. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains the raw model zoos as collections of models (file names beginning with "cifar_"). Zoos are trained with small and large CNN models, in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). Due to the large filesize, the preprocessed datasets are hosted in a separate repository. The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - SVHN

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from SVHN. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "svhn_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - Fashion-MNIST

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from Fashion-MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "fmnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

Model Zoo: A Dataset of Diverse Populations of Neural Network Models - MNIST

Abstract In the last years, neural networks have evolved from laboratory environments to the state-of-the-art for many real-world problems. Our hypothesis is that neural network models (i.e., their weights and biases) evolve on unique, smooth trajectories in weight space during training. Following, a population of such neural network models (refereed to as “model zoo”) would form topological structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can be reveal latent properties of individual models. With such zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of neural network weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of neural networks. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of neural network models for further research. In total the proposed model zoo dataset is based on six image datasets, consist of 24 model zoos with varying hyperparameter combinations are generated and includes 47’360 unique neural network models resulting in over 2’415’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks as mentioned before. Dataset This dataset is part of a larger collection of model zoos and contains the zoos trained on the labelled samples from MNIST. All zoos with extensive information and code can be found at www.modelzoos.cc. This repository contains two types of files: the raw model zoos as collections of models (file names beginning with "mnist_"), as well as preprocessed model zoos wrapped in a custom pytorch dataset class (filenames beginning with "dataset"). Zoos are trained in three configurations varying the seed only (seed), varying hyperparameters with fixed seeds (hyp_fix) or varying hyperparameters with random seeds (hyp_rand). The index_dict.json files contain information on how to read the vectorized models. For more information on the zoos and code to access and use the zoos, please see www.modelzoos.cc.

opencc-by-sa-4.0Mar 2022View details →

Dependency Networks of Open Source Libraries Available Through CocoaPods, Carthage and Swift PM

Third party libraries are used to integrate existing solutions for common problems and help speed up development. The use of third party libraries, however, can carry risks, for example through vulnerabilities in these libraries. Studying the dependency networks of package managers lets us better understand and mitigate these risks. So far, the dependency networks of the three most important package managers of the Apple ecosystem, CocoaPods, Carthage and Swift PM, have not been studied. We analysed the dependencies for all publicly available open source libraries up to December 2021 and compiled a dataset containing the dependency networks of all three package managers. The dependency networks can be used to analyse how vulnerabilities are propagated through transitive dependencies. In order to ease the tracing of vulnerable libraries we also queried the NVD database and included publicly reported vulnerabilities for these libraries in the dataset.

Dataset for 'Valley Networks and the Record of Glaciation on Ancient Mars'

Supplementary information (in pdf format, ~800 kb) containing the model, setup, and parameter analysis supporting the manuscript 'Valley Networks and the Record of Glaciation on Ancient Mars'.  Includes a detailed table of parameters with references.

Event-Based Velocity Prediction for Spiking Neural Networks

This dataset is intended to be used to predict the velocity based on the event pixels present in the data, given in (t, x, y, p) format alongside a ground truth velocity reading. A novel dataset using people and various objects moving in front of an RGB video camera was created. The positions of each entity and the associated times were captured with a Vicon motion tracking system. These two types of data were calibrated so that the movement in the video matched the measurements recorded by the Vicon system. The types of data collected include two different people carrying a calibrated Vicon Active Wand and moving around the room, a Lambda aerial robot with motion tracking markers that flew around the room, a box with motion tracking markers that were tossed back and forth in the air, and the same box was slid across the floor. The video recording was simulated as event camera data. Each pixel changes state independently of all the other pixels. The Open Event Camera Simulator (ESIM) from the Robotics and Perception Group at the University of Zurich and ETH Zurich is used. The event camera simulator used, ESIM, allows for accurate event simulation data. The ground truth velocities were calculated from the position and timestamps recorded by the Vicon system.

opencc-by-4.0Mar 2020View details →

RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"

Corresponding peer-reviewed publication This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in: <ul> <li> Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1. </li> </ul> When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.  Known bugs and limitations in this dataset or the associated manuscript. In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate.  The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location.  As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect.  This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented.  The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name.  Correct figures can also be created using corresponding file names that were not so appended.  Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper.  The slope of the daily discharge STDE trend initially reported as $\alpha = 0.3876$ in Figure 5c changes to $\alpha = 0.4507$ after correction.  The resulting value of the ideal inflation factor hence changes from $I = {1 \over 0.3876} \approx 2.58$ to $I = {1 \over 0.4507} \approx 2.22$. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to $I = 2.58$ than to $I = 1$ or $I = 5$, i.e. the three values that were evaluated. Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended.

opencc-by-4.0Dec 2015View details →

TV Series - Networks of characters

Description. This repository contains three sets of files related to the social networks of characters in the following episodes of 3 TV series: <ul> <li>Breaking Bad (BB): S01--S03;</li> <li>Game of Thrones (GoT): S01--05;</li> <li>House of Cards (HoC): S01--S02.</li> </ul> The three separate Graphml files (`.graphml` extension) contain the static, cumulative, conversational networks at the end of the set of episodes considered. Three images (`.jpg` extension) of the resulting static, cumulative, conversational networks at the end of the first two seasons are also provided. The compressed archives (`.tgz` extension) provide snapshots of the conversational network of characters (Graphml format) in every scene. These files come in three flavors: <ol> <li>Narrative smoothing based networks (denoted `ns`).</li> <li>Time-slice based networks, where all interactions are agglomerated every 10 scenes (denoted `'ts10`).</li> <li>Time-slice based networks, where all interactions are agglomerated every 40 scenes (denoted `ts40`).</li> </ol> The video files (`.mp4` extension) contain short animations of the dynamic networks of characters as they evolve over the whole set of episodes considered: <ul> <li>The nodes are represented by the names of the corresponding characters and the distance between two character names is inversely proportional to the weight of the corresponding edge: the closer they are at some point of the story, the more they interact then.</li> <li>The size of each character name is proportional to the local strength of the corresponding character at the moment considered.</li> <li>The color of each character name corresponds to his community at any moment.</li> </ul> Each snapshot in these animations is based on our narrative smoothing approach. This dataset was used in the following articles: <ol> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Narrative smoothing: dynamic conversational network for the analysis of TV Series plots,” in 2nd International Workshop on Dynamics in Networks (DyNo/ASONAM), 2016, pp. 1111–1118. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01276708">hal-01276708</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2016.7752379">10.1109/ASONAM.2016.7752379</a></li> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction de réseaux dynamiques conversationnels par lissage narratif,” in 7ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01385215">hal-01385215</a>⟩</li> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction and analysis of dynamic conversational networks from TV series,” in Social Network Based Big Data Analysis and Applications, Springer, 2018, pp. 55–84. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01543938">hal-01543938</a>⟩ DOI: <a href="http://doi.org/10.1007/978-3-319-78196-9_3">10.1007/978-3-319-78196-9_3</a></li> </ol> Citation. If you use this dataset, please cite the article [1] <code>@InProceedings{Bost2016,</code> <code>  author    = {Bost, Xavier and Labatut, Vincent and Gueye, Serigne and Linarès, Georges},</code> <code>  title     = {Narrative smoothing: dynamic conversational network for the analysis of {TV} Series plots},</code> <code>  booktitle = {2nd ASONAM International Workshop on Dynamics in Networks},</code> <code>  year      = {2016},</code> <code>  pages     = {1111-1118},</code> <code>  address   = {San Francisco, US},</code> <code>  publisher = {IEEE Publishing},</code> <code>  doi       = {10.1109/ASONAM.2016.7752379},</code> <code>}</code>

Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. Frontiers in Remote Sensing, 1</li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604</li> </ul>

opencc-by-4.0Jul 2022View details →

Dataset: Multi-level network dataset of social-ecological interdependencies in ten Swiss wetlands based on qualitative interviews and quantitative surveys

The dataset originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).

The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - B. Data for 2020 - 2026 - Covid scenario

The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - A. Code and data for 2016-2019

The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries. This repository includes data for years from 2016 to 2019 (hist scenario) and the corresponding labels. Data for years 2020 to 2026 are stored in the corresponding repositories: <ul> <li>covid: <a href="https://doi.org/10.5281/zenodo.5713825">10.5281/zenodo.5713825</a></li> <li>counterfactual: <a href="https://doi.org/10.5281/zenodo.5713839">10.5281/zenodo.5713839</a></li> </ul> Tables are generated using the <a href="https://github.com/TBeaufils/SPIN">SPIN method</a>, based on the <a href="https://doi.org/10.5281/ZENODO.3993659">RMRIO tables</a> for the year 2015, GDP, imports and exports data from the <a href="https://data.imf.org/?sk=4c514d48-b6ba-49ed-8ab9-52b0c1a0179b">International Financial Statistics</a> (IFS) and the World Economic Outlooks (WEO) of <a href="https://www.imf.org/en/Publications/WEO/weo-database/2019/October">October 2019</a> and <a href="https://www.imf.org/en/Publications/WEO/weo-database/2021/April">April 2021</a>. From 2020 to 2026, the dataset includes two diverging scenarios. The covid scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The counterfactual scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as hist. The Projections folder includes the generated tables for years from 2016 to 2019 (hist scenario) and the corresponding labels. The Sources folder contains the data records from the IFS and WEO databases. The Method data contains the data files used to generate the tables with the SPIN method and the following Python scripts: <ul> <li>SPIN_covid19_MRIO_files_preparation.py generates the data files from the source data.</li> <li>SPIN_covid19_RMRIO runs.py is the command to run the SPIN method and generate the dataset.</li> <li>figures.py is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.</li> </ul> All tables are labelled in 2015 US$ and valued in basic prices.

The SPIN covid19 RMRIO dataset: Global trade network data for the years 2016-2026 reflecting macroeconomic effects of the covid19 pandemic - C. Data for 2020 - 2026 - Counterfactual scenario