Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks"

This archive contains supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks". In particular, the following data is provided: <ul> <li>dataset of labeled simulated TDR signals (1 million samples)</li> <li>dataset of labeled experimental signals (16 samples)</li> <li>trained convolutional neural network model</li> </ul>

Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India

This dataset was created as part of our study on polio vaccine hesitancy in Malegaon, India. If using this dataset, please cite this data repository and the publication: Onnela JP, Landon BE, Kahn AL, Ahmed D, Verma H, O'Malley AJ, Bahl S, Sutter RW, Christakis NA. Polio vaccine hesitancy in the networks and neighborhoods of Malegaon, India. Soc Sci Med. 2016 Mar;153:99-106. doi: 10.1016/j.socscimed.2016.01.024. Epub 2016 Feb 4. PMID: 26889952.   A) Main data file data.csv The files below can be constructed from this main file. Column: Variable (Value) Col 0: Respondent ID (Integer) Col 1: Data collection team ID (Integer) Col 3: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles) Col 12: Primary ID for Alter 1 – general questions (Integer) Col 13: Duplicate ID for Alter 1 – general questions (Integer) Col 14: Primary ID for Alter 2 – general questions (Integer) Col 15: Duplicate ID for Alter 2 – general questions (Integer) Col 16: Primary ID for Alter 3 – general questions (Integer) Col 17: Duplicate ID for Alter 3 – general questions (Integer) Col 18: Primary ID for Alter 4 – general questions (Integer) Col 19: Duplicate ID for Alter 4 – general questions (Integer) Col 48: Primary ID for Alter 1 if not household head – general questions (Integer) Col 49: Duplicate ID for Alter 1 if not household head – general questions (Integer) Col 50: Primary ID for Alter 2 if not household head – general questions (Integer) Col 51: Duplicate ID for Alter 2 if not household head – general questions (Integer) Col 52: Primary ID for Alter 3 if not household head – general questions (Integer) Col 53: Duplicate ID for Alter 3 if not household head – general questions (Integer) Col 54: Primary ID for Alter 4 if not household head – general questions (Integer) Col 55: Duplicate ID for Alter 4 if not household head – general questions (Integer) Col 56: Primary ID for Alter 1 – health questions (Integer) Col 57: Duplicate ID for Alter 1 – health questions (Integer) Col 58: Primary ID for Alter 2 – health questions (Integer) Col 59: Duplicate ID for Alter 2 – health questions (Integer) Col 60: Primary ID for Alter 3 – health questions (Integer) Col 61: Duplicate ID for Alter 3 – health questions (Integer) Col 62: Primary ID for Alter 4 – health questions (Integer) Col 63: Duplicate ID for Alter 4 – health questions (Integer) Col 92: Primary ID for Alter 1 if not household head – health questions (Integer) Col 93: Duplicate ID for Alter 1 if not household head – health questions (Integer) Col 94: Primary ID for Alter 2 if not household head – health questions (Integer) Col 95: Duplicate ID for Alter 2 if not household head – health questions (Integer) Col 96: Primary ID for Alter 3 if not household head – health questions (Integer) Col 97: Duplicate ID for Alter 3 if not household head – health questions (Integer) Col 98: Primary ID for Alter 4 if not household head – health questions (Integer) Col 99: Duplicate ID for Alter 4 if not household head – health questions (Integer) Col 101: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education) Col 102: TV (1:Yes, 2:No) Col 103: Phone (1:Yes, 2:No) Col 104: Cooking cylinder (1:Yes, 2:No) Col 105: Number of rooms (Integer) Col 106: Toilet (1:Yes, 2:No) Col 107: Number of people (Integer)   B) Derived network files (edge lists) These directed network edge lists were derived from the main data file. Here N stands for number of nodes and L stands for number of edges. 1) Nomination network (directed, general only): N=8161, L=7357; edgelist_dg.txt 2) Nomination network (directed, health only): N=7223, L=5744; edgelist_dh.txt 3) Nomination network (combined, directed): N=11828, L=11655; edgelist_d.txt 4) Nomination network (combined, directed) LCC: N=6113, L=6647; edgelist_d_lcc.txt 5) Vaccine network (directed): N=2428, L=1355; edgelist_g_vstatusok.txt 6) Vaccine network (directed) LCC: N=710, L=813; edgelist_g_vstatusok_lcc.txt   C) Derived nodal attribute file final_node_data.csv This nodal attribute file was derived from the main data file. Column: Variable (Value) Col 0: Respondent ID (Integer) Col 1: Data collection team ID (Integer) Col 2: Vaccine status of household (1:Accepting, 2:Reluctant, 3:Refusing, 4:P0 House – no vaccine eligibles) Col 3: In-degree – general questions (Integer) Col 4: In-degree – health questions (Integer) Col 5: In-degree –  combined (Integer) Col 6: Out-degree – general questions (Integer) Col 7: Out-degree – health questions (Integer) Col 8: Out-degree – combined (Integer) Col 9: Education (1:No school, 2:Primary school, 3:Middle school, 4:High school, 5:Intermediate diploma, 6:Graduate or post graduate, 7:Professional, 8:Islamic education) Col 10: TV (1:Yes, 2:No) Col 11: Phone (1:Yes, 2:No) Col 12: Cooking cylinder (1:Yes, 2:No) Col 13: Number of rooms (Integer) Col 14: Toilet (1:Yes, 2:No) Col 15: Number of people (Integer)

Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"

Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript. The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review. Please consult the README file for information on the specifications of the files. These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.

MICrONS: Machine Intelligence from Cortical Networks

A ground truth dataset for 3D neuron segmentation from electron microscopy (EM) images of mouse visual cortex, created for <a href="https://www.iarpa.gov/research-programs/microns">IARPA's Machine Intelligence from Cortical Networks (MICrONS) program</a>. For more information, please visit <a href="https://www.microns-explorer.org/">our MICrONS explorer website</a>.   Citation Functional connectomics spanning multiple areas of mouse visual cortex MICrONs Consortium et al. bioRxiv 2021.07.28.454025; doi: https://doi.org/10.1101/2021.07.28.454025   Data Format Specification Volumes and annotations are stored in a single HDF5 file with the following datasets: <ul> <li>volumes: are stored in row-major format, i.e., with dimensions (z, y, x). Every volume has an attribute resolution that specifies the voxel size in nm. <ul> <li>image</li> <li>segmentation</li> <li>mitochondria (optional)</li> <li>synapse (optional): annotation for postsynaptic densities (PSDs)</li> <li>fold (optional): annotation for folds (a kind of image defect) in images</li> <li>mask: valid/invalid voxels are represented as 1s/0s respectively.</li> </ul> </li> <li>annotations: are incomplete, meaning that each volume may or may not contain proper annotations. <ul> <li>object_ids (optional): small unidentified objects or intracellular organelles that are oversegmented, which may require special handling.</li> <li>myelin_ids (optional)</li> <li>blood_vessel_ids (optional)</li> <li>soma_ids (optional)</li> <li>nucleus_ids (optional)</li> </ul> </li> </ul>

opencc-by-4.0Dec 2022View details →

The gut microbiota affects the social network of honey bees

This dataset contains input files needed to reproduce the automated behavioral tracking data analyses of the research article "The gut microbiota affects the social network of honey bees”. Codes using these data and additional datasets are available at: https://github.com/JoanitoLiberti/The-gut-microbiota-affects-the-social-network-of-honey-bees/

Maritime entities of geographic transportation network of Acclimate

maritime_entities.csv: Centroids of maritime entities and coordination of ports used by geographic transportation network of Acclimate XYZ_coordinates.csv: Polygon coordinates of maritime entites XYZ of the East Asian maritime trading route. Used to compute time tropical cyclone passed by based on observed trajectories from IBTrACS [http://ibtracs.unca.edu/]

opencc-zeroDec 2021View details →

dryad40/100

Group composition of individual personalities alters social network structure in experimental populations of forked fungus beetles

Social network structure is a critical group character that mediates the flow of information, pathogens, and resources among individuals in a population, yet little is known about what shapes social structures. In this study, we experimentally tested whether social network structure depends on the personalities of group members. Replicate groups of forked fungus beetles (Bolitotherus cornutus) were engineered to include only members previously assessed as either more social or less social. We found that individuals behaved consistently across social contexts, exhibiting repeatable numbers of interactions and numbers of partners. At the group level, networks composed of more social individuals had higher interaction rates, higher tie density, higher global clustering, and shorter average shortest paths than those composed of less social individuals. We highlight group composition of personalities as a source of variance in group traits and a potential mechanism by which networks could evolve.

Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, main part

Supplementary files containing datasets needed to reproduce the results of the manuscript "Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks" by S. Choudhury et al. The code to use with these data and reproduce the manuscript results is available at  <a href="https://github.com/EPFL-LCSB/rekindle">https://github.com/EPFL-LCSB/rekindle</a> and <a href="https://gitlab.com/EPFL-LCSB/rekindle">https://gitlab.com/EPFL-LCSB/rekindle</a>. The execution of parts of this code is dependent on the SkimPy toolbox (<a href="https://github.com/EPFL-LCSB/skimpy">https://github.com/EPFL-LCSB/skimpy</a>). Refer to the readme files on the REKINDLE code repositories for more details. Datasets: <ul> <li> models.zip - Datasets parameterizing kinetic nonlinear models of a wild-type E. coli strain used for training generative adversarial networks <ul> <li>subfolder 1: kinetic - contains the kinetic model (kin_varma_curated.yml)</li> <li>subfolder 2:  thermo - contains the thermodynamic model for all the four physiologies (varma_fdp1, varma_fdp2, varma_fdp3, varma_fdp4)</li> <li>subfolder 3:  steady_state_samples: contains the TFA steady state profiles for all four physiologies (samples_fdp1, sample_fdp2, samples_fdp3, samples_fdp4)</li> <li>subfolder 4: parameters - contains the kinetic parameter training dataset for each physiology (.hdf5 files), maximal eigenvalues (training labels)  (maximal_eigenvalues.csv) and the minimum eigenvalues (minimal_eigenvalues.csv)</li> </ul> </li> <li>vanilla_learning_training.zip: contains 4 folders for each of the 4 physiologies. <ul> <li>each of these folders contains 6 subsubfolders in the format N-{n} ( N-10, N-50, N-100, N-500, N-1000, N-72000), where {n} represents the number of used training data samples.</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy (Note: eigenvalues were not calculated for N=10, 50, 100 as traning failed)/</li> </ul> </li> </ul> </li> <li>transfer_learning_training.zip - contains 12 subfolders "tl_fdpi_fdpj" where i,j ={1,2,3,4} for each of the 12 transfer learning case <ul> <li>each of these folders contains 5 subsubfolders N-10, N-50, N-100, N-500, N-1000</li> <li>every subsubfolder N-{n} contains 5 repeats folders. Each repeat folder contains, <ul> <li>E_-1.npy - GAN generated kinetic parameters at E-th epoch/</li> <li>E_-1_max_eig.csv - the maximal eigenvalues of Jacobian for E_-1.npy </li> </ul> </li> </ul> </li> </ul> <ul> <li>best_generators.zip <ul> <li>The best generators (with the highest incidence of relevant models) for each physiology (generator1- 4.h5)</li> <li>The normalizing scaling parameters for each generator (d_scaling.pkl).</li> <li> </li> </ul> </li> <li>Temporal evolution of perturbations in non-linear ordinary differential equations <ul> <li>vanilla_ODE_sample_parameters.zip - contains (i) 1000 REKINDLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total) (ii) 1000 ORACLE generated kinetic parameter sets for each of the 4 physiologies and their corresponding eigenvalues (4 in total). These parameter sets parameterize the ODEs which are integrated.</li> <li>ode_solutions_physiology1.zip (available at <a href="https://zenodo.org/record/5818192">https://zenodo.org/record/5818192</a>) -  contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li>ode_solutions_physiology1_ORACLE.zip (available at <a href="https://zenodo.org/record/5819669">https://zenodo.org/record/5819669</a>) -  contains 100 subfolders, each subfolder containing the time-series evolution data of 1000 kinetic models parameterized by ORACLE generated parameter sets for physiology 1, each of the 1000 models having a random perturbation.</li> <li>ode_solutions_physiologies2-4.zip - contains 6 subfolders (physiology_2-4, physiology_2-4_ORACLE), with each subfolder containing 10 sub subfolders. Each sub subfolder containing the time-series evolution data of 1000 kinetic models parameterized by REKINDLE / ORACLE generated parameter sets for physiology 2-4, each of the 1000 models having a random perturbation.</li> <li>transfer_learning_ODE_solutions.zip - contains two subfolders N_10, N_50, each subfolder contains 12 subsubfolders titled i_j (where i = {1,2,3,4} and j = {1,2,3,4} where 1_2 represent the transfer learning case from physiology 2 to physiology 1 and when using {n} samples from physiology 2 and so on (where {n}=10 and 50 respectively).  Each subsubfolders contain <ul> <li>i_j.hdf5: contains 300 kinetic parameter sets generated using (i) REKINDLE for this transfer learning case</li> <li>i_j.csv: the maximal eigenvalues of the parameter sets</li> <li>solutions.csv: ODE integrated time series data for the relevant kinetic parameters out of the 300 generated.</li> </ul> </li> </ul> </li> </ul>

opencc-zeroJan 2022View details →

dryad40/100

Using convolutional neural networks to efficiently extract immense phenological data from community science images

Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.

Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 2

Supplementary files for Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks, additional part 1

# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.

The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse

The Reddit Politosphere is a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. Based on the <a href="https://doi.org/10.5281/zenodo.3608135">Pushshift Reddit Dataset</a>, it is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data. We also release annotated metadata for subreddits and users. Documentation and scripts for easy data access are provided in an associated <a href="https://github.com/valentinhofmann/politosphere">repository</a> on GitHub.

opencc-by-4.0Jun 2021View details →

Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks

This Data set contain 29 table of weighted interaction network between plants (columns) and frugivore birds from the Brazilian Atlantic Forest used in the manuscript "Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks" published in Oikos Journal.

Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).

Pretrained network for segmentation of kidneys and exophytic cysts in subjects with autosomal dominant polycystic kidney disease (ADPKD)

This pretrained network based on the 3D U-Net is for segmentation of kidneys and exophytic cysts in subjects with autosomal dominant polycystic kidney disease (ADPKD). The network was trained with 157 (including 53 cases with exophytic cysts) subjects with ADPKD. The details of trained dataset and the performance of the network will be updated after our manuscript is accepted for publication.

Gulf Stream Daily Temperature, Salinity and Mixed Layer Depth fields from Ocean Stratification network (OSnet).

The OSnet Gulf Stream product consists of 4D daily Temperature, Salinity and Mixed Layer Depth, available on a 1/4 degree regular grid and on 51 depth levels from the surface down to 1000m, in the Gulf Stream region from 01-01-1993 to 31-12-2019. We introduce OSnet (Ocean Stratification network) in the Gulf Stream region, a new ocean reconstruction system aimed at providing a physically consistent analysis of the upper ocean stratification. The interpolation scheme is a bootstrapped multilayer perceptron trained to predict simultaneously temperature and salinity (T-S) profiles down to 1000m and the Mixed Layer Depth (MLD) from satellite data covering 1993 to 2019. The inputs are sea surface temperature and sea level anomaly, complemented with mean dynamic topography, bathymetry, longitude, latitude and the day of the year. The in-situ profiles are from the CORA database and include Argo floats and ship-based profiles. The prediction of the MLD is used to adjust a posteriori the vertical gradients of predicted T-S profiles, thus increasing the accuracy of the solution and removing vertical density inversions. The prediction is generalized on a 1/4 degree daily grid, producing four-dimensional fields of temperature, salinity and mixed layer depth, with their associated confidence interval issued from the bootstrap. The full dataset is downloadable with the <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> tool and the command :<code> zenodo_get 6011144</code>

Network of the European FAIR initiatives: relationships and outputs

This network is an attempt at comprehensively describing the FAIR projects and initiatives in Europe, together with their area of focus, outputs and relationships with one another

Supplementary Data for "Interplay of river and tidal forcings promotes loops in coastal channel networks"

This dataset contains supplemental data required to reproduce the results of the paper Interplay of river and tidal forcings promotes loops in coastal channel networks (in review at Geophysical Research Letters). We provide raw and extracted channel network data for 19 river deltas/coastal marsh sites. For each site, the following files are provided: XXX_base.tif : the raw binary mask of the river channel network XXX_clipper.shp (and associated .dbf, .prj, .qpj, and .shx files) : polygon(s) used to clip the raw mask XXX_clipped.tif : the binary mask of the river channel network after being clipped by XXX_clipper.shp XXX_filled.tif : the binary mask after filling islands via the method specified in the paper XXX_inlet_nodes.shp (and associated .dbf, .prj, .qpj, and .shx files) : locations of the inlet nodes; used by RivGraph XXX_shoreline.shp (and associated .dbf, .prj, .qpj, and .shx files) : location of the shoreline; used by RivGraph XXX_links.json : GeoJSON file containing the geometries, connectivities, and widths of each link in the network XXX_nodes.json : GeoJSON file containing the locations of each node of the network process_XXX.py : the python script used to generate the above files All files listed below (except .py files) are georeferenced (i.e. can be opened with QGIS, ArcGIS or another GIS). Exceptions to the provided files include: Barnstable: no "base.tif" is provided. Use "filled.tif". GBM: some hand-cleaning was performed on "filled.tif". Mackenize: "clipper.shp" is not provided, but "clipped.tif" is. Mississippi: "clipper.shp" is not provided as the mask was made from a shapefile. In order to run process_XXX.py, the RivGraph package will need to be installed. Instructions can be found at https://github.com/jonschwenk/RivGraph.

Data set for "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2"

Data sets for the publication "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2", doi:10.1021/acsnano.1c07065