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9,152 results for “Networking”
Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron
<p>This is the data set for Zhang et al. 2019 "Local synaptic inputs support opposing, network-specific odor representations in a widely projecting modulatory neuron" published at eLife.</p>
Supervised learning is an accurate method for network-based gene classification - Data
<p>This file contains the data that was used in the paper titled "Supervised learning is an accurate method for network-based gene classification" (https://doi.org/10.1093/bioinformatics/btaa150). Some data was excluded if the license was not permissive enough.</p>
Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks
<p>Data related to publication:</p> <p>Heider et al.: Pyridinylimidazoles as GSK3β inhibitors: the impact of tautomerism on compound activity via water networks.</p> <p>The files include:</p> <p>1) Output conformations of the QM Tautomer & Conformation Predictor of Maestro (Schrödinger, LLC, New York, NY, 2018) of the compounds <strong>1</strong>, <strong>3a</strong>, <strong>3b</strong>, <strong>3c</strong>, <strong>3d</strong>, <strong>3e</strong>, <strong>3f</strong>, <strong>3g</strong>, <strong>3h</strong>, <strong>3j</strong>, <strong>3k</strong>, <strong>3l</strong>, <strong>3m</strong>, <strong>6f</strong> and <strong>6g</strong> [.mae and .sdf files].</p> <p>2) Movies of the MD simulations of compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> (6GN1) and movies (*_2) of <strong>3a</strong>, <strong>3j</strong>, <strong>6b</strong>, <strong>6g</strong> (4PTC) [.mpg files] .</p> <p>3) Raw Desmond trajectory files of the MD simulations of the compounds <strong>3a</strong>, <strong>3j</strong>, <strong>4a</strong>, <strong>4b</strong>, <strong>6b</strong>, <strong>6g</strong> [out.cms and the full trj files].</p> <p> </p> <p><br> </p>
GIS data - Model sítě vodních toků v ČR | GIS data - Water Stream Network Model for the Czech Republic
<p>GeoTIFF layers (8 x 8 m) containing a modeled network of watercourses in the landscape of the Czech Republic: (1) "streams_def_cr.tif" layer - network of natural watercourses with Strahler order determination; (2) "vodni_toky.tif" layer - model of potentially navigable watercourses. For a detailed description of layers, see https://doi.org/10.5281/zenodo.3367296</p>
GIS data - Model přirozené komunikační sítě pro území ČR | GIS data - Model of Natural Path Network for the Czech Republic
<p>GeoTIFF layer (8 x 8 m) containing a model of natural routes through the landscape of the Czech Republic. For a detailed description of layers, see https://doi.org/10.5281/zenodo.3367296</p>
Protein interaction networks are substantially rewired across evolution
<p>Complete data and source code deposit for Protein interaction networks are substantially rewired across evolution.</p>
Simplified Approach for Structural Evaluation of Flexible Pavements at the Network Level
<p>Corresponding data set for Tran-SET Project No. 17PUTA02. Abstract of the final report is stated below for reference:</p> <p>"Currently, there are few available simple procedures to identify structurally weak pavement sections utilizing Falling Weight Deflectometer (FWD) data at the network level (e.g., city, state or province). A simple method is required to determine the structural condition of pavement sections that can be directly implemented and automated in current pavement databases. The objective of this research study is to develop a simple analysis method to determine the structural condition of pavement sections utilizing the currently available non-destructive testing (NDT) deflection measurement devices at the network level that can be directly implemented and automated in the database of a typical transportation agency. In addition, the study had conducted an advanced mechanistic analysis to mimic the FWD deflection bowl obtained from the field. The developed structural condition parameters can be easily implemented in pavement management systems (PMS). This will aid Departments of Transportation (DOTs) and local highway agencies to make more informed decisions about the most suitable maintenance and rehabilitation strategies. Those parameters were also utilized to predict the remaining fatigue lives of the studied pavement sections."</p>
Relationship between Road Network Characteristics and Traffic Safety
<p>Corresponding data set for Tran-SET Project No. 17ITSTSA01. Abstract of the final report is stated below for reference:</p> <p>"The Transportation and Capital Improvement of the City of San Antonio, Texas Department of Transportation (TxDOT) and other related agencies often make several efforts based on traffic data to improve safety at intersections, but the number of intersection crashes is still on the high side. There is no one size fits all solution for intersections and the City is often usually confronted with doing best value option analysis on different solutions to choose the least expensive yet more advancements. The goal of this project was to obtain the relationship between road network characteristics and public safety with a focus on intersections; perform a thorough analysis of critical intersections with high crash incidents and crash rates within the city of San Antonio, Texas, and analyze key factors that lead to crashes and recommend effective safety countermeasures. Researchers conducted the following tasks: literature review, crash data analysis, factors affecting crashes at intersections, and the development of possible solutions to some of the identified challenges. Several variables and factors were analyzed, including driver characteristics, like age and gender, road-related factors and environmental factors such as weather conditions and time of day ArcGIS was used to analyze crash frequency at different intersections, and hotspot analysis was carried out to identify high-risk intersections. The crash rates were also calculated for some intersections. The research outcome shows that there are more male drivers than female drivers involved in crashes, even though we have more licensed female drivers than male drivers. The highest number of crashes involved drivers within the age range of 15 – 34 years; this is an indication that intersection crash is one of the top threats to the young generation. The study also shows that the most common crash type is the angle crash which represents over 23% of the intersection crashes. Driver’s inattention ranked first among all the contributing factors recorded. The highrisk intersections based on crash frequency and crash rate show that the intersection along the Bandera Road and Loop 1604 is the worst in the city, with 399 crashes and 8.5 crashes per million entering vehicles. The research concluded with some suggested countermeasures, which include public enlightenment and road safety audit as a proactive means of identifying high-risk intersections."</p>
A neural network-based estimate of the seasonal variability of total alkalinity in the East China Sea shelf
<p>In order to estimate the seasonal variability of total alkalinity in the ECS shelf, an artificial neural network (ANN) model was developed using 5 cruise datasets from 2008 to 2018. The model used temperature, salinity, and dissolved oxygen to estimate A<sub>T</sub> with a root-mean-square error of ~7 umol kg<sup>-1</sup>, and was applied to fill missing alkalinity data for 8 cruises during 2013-2016. In addition, monthly water column A<sub>T</sub> for the period 2000-2016 was also obtained passing temperature, salinity, and dissolved oxygen from the Changjiang Biology Finite-Volume Coastal Ocean Model (FVCOM) Data. Spatial distributions, seasonal cycles and correlations of surface A<sub>T</sub> indicated that the seasonal fluctuation of the Changjiang River discharge is the major factor affecting seasonal variation of surface total alkalinity in the ECS shelf. The largest seasonal fluctuation of surface total alkalinity was found on the inner shelf near the Changjiang Estuary.</p>
Retrieving monthly and interannual pHT in the East China Sea shelf using an artificial neural network: ANN-pHT-v1
<p><br> The reliability of the artificial neural network model was evaluated by independent sampled data from 3 cruises in 2018.</p> <p>Monthly water column pHT for the period 2000-2016 was obtained passing T, S, DO, N, P, and Si from the Finite-Volume Coastal Ocean Model with the European Regional Sea Ecosystem Model through the artificial neural network. The spatiotemporal resolution of monthly pHT is 1-10 km in the horizontal, 10 depth levels in the vertical, and 12 months. Seasonal pHT dynamics in the East China Sea shelf can be primarily attributed to temperature changes and the shifting balance of production and respiration processes.</p>
Modelling riparian forest distribution and composition to entire river networks
<p><strong>Aim: </strong>Developing a methodology to map the distribution of riparian forests to entire river networks and determining the main environmental factors controlling their spatial patterns.</p> <p><strong>Location: </strong>Cantabrian region, northern Spain.</p> <p><strong>Methods: </strong>We mapped the riparian forests at a physiognomic and phytosociological levels by delimiting riparian zones and generating vegetation distribution models based on remote sensing data (Landsat 8 OLI and LiDAR PNOA). We built virtual watersheds to define a spatial framework where the catchment environmental information can be routed to each river reach, jointly with the vegetation map. In order to determine the drivers playing a significant role on the observed spatial patterns in the riparian forest we modelled interactions between these datasets of environmental information and riparian vegetation by using the Random Forest algorithm.</p> <p><strong>Results: </strong>The modelling results obtained reproduced a reliable variation of riparian forest structure and composition across Cantabrian watersheds. The produced maps were highly accurate, with more than a 70% overall accuracy for the forest occurrence. A clear differentiation between Eurosiberian (91E0 and 9160 habitats) and Mediterranean (92E0) riparian forests was shown on both sides of the mountain range. Topography and land use were the main drivers defining the distribution of riparian forest as a physiognomic unit. In turn, altitude, climate and percentage of pasture were the most relevant factors determining their composition (phytosociological approach).</p> <p><strong>Conclusions: </strong>Our study confirms that the anthropic control ultimately defines the distribution of the vegetation in the riparian area at a regional to local scale. Human disturbances constrain the extension of forest patches across their potential distribution defined by topoclimatic boundaries, which establish a clear limit between Mediterranean and Eurosiberian biogeographical regions.</p>
Channel State Information (CSI) analysis for predictive maintenance using Convolutional Neural Network (CNN)
<p>Dataset manual:</p> <p>This dataset contains CSI amplitude values for rotating motors in an office environment. Details of the experiments may be found in the corresponding paper published in the DATA'19 workshop, SenSys (<a href="https://doi.org/10.1145/3359427.3361917">https://doi.org/10.1145/3359427.3361917</a>). </p> <p>Folder structure:<br> The folders for servo motor and stepper motor contains separate folders for network reconnection conditions (w_recc: with reconnections, wo_recc: without recconnections) and load conditions (w_load: with load and wo_load: without load). The data is stores as Matlab files with .mat extentions. </p> <p>File structure:<br> In each file name, the digits after the '_' at the end of the file name correspond to the speed of the motor. In case of stepper motor these numbers could be directly interpreted as rpm. Ex: table_inj_with_load_5_0.mat corresponds to stationary motor (0 rpm) and table_inj_with_load_5_250.mat corresponds to motor rotating with 250 rpm speed. In the case of servo motor these numbers should be mapped with the following table in order to get the speeds.</p> <p>0: 0 rpm<br> 50: 14.45 rpm<br> 100: 8.02 rpm<br> 150: 5.38 rpm<br> 200: 4.05 rpm<br> 250: 3.26 rpm<br> 300: 2.67 rpm<br> Ex: table_inj_with_load_50.mat corresponds to motor running with 14.45 rpm.</p> <p>Each file has 3 columns, each corresponding to CSI value, labels (speed/last digits in the file name) and the data sample number (not in sequence as a result of packer loss) respectively. CSI values are typically a matrix of size 3000*180 (3000 CSI samples for 3sec data @1kHz sampling rate and 180 channels for 6 antenna pairs @ 30 subcarrier data per antenna).</p>
Speed Prediction in Large and Dynamic Traffic Sensor Networks
<p>Aggregated traffic sensor data from Fortaleza (Brazil) in 2014.</p> <p>Please cite the following paper when using the dataset:</p> <p>R.P. Magalhaes, F. Lettich, J.A. Macedo, F.M. Nardini, R. Perego, C. Renso, R. Trani., <strong>Speed prediction in large and dynamic traffic sensor networks</strong>, Information Systems (2019) 101444, <a href="https://doi.org/10.1016/j.is.2019.101444">https://doi.org/10.1016/j.is.2019.101444</a></p> <p>You can also check details regarding the dataset in the paper.</p>
Interaction-Based Behavioral Analysis in Twitter Social Network
<p>Literature studies usually use data sets consisting of data collected from many different metrics and user counts collected over different time periods. The data set used in this article was formed using completely up-to-date data obtained as a result of metrics measured in terms of scope and efficiency, sufficient and effective user counts, and filtering processes. To classify users correctly and make the classification performance high—in addition to parameters used in the literature such as tweets, account age, follower rank, average retweets and average likes—other parameters such as diameter, density, reciprocity, centralization and modularity were used. These metrics are the parameters that focus on a different area to reveal many aspects in which social network users interact. The data used to create the data set was collected from Twitter. The metric data forming the data set was extracted using Twitter Rest API V1.1 supporting search/tweet endpoints by means of the SocialBlade and Netlytic platforms.</p>
Stream network for the SAFE Project derived from SRTM data
<b>Description: </b><p>This zipfile contains linear geometries showing a channel network across the SAFE Project landscape. The network was calculated using the GRASS hydrology tool <code>r.stream.extract</code> from SRTM elevation data and the resulting flow accumulation predictions (see <a href="https://zenodo.org/record/3490488">https://zenodo.org/record/3490488</a> and <a href="https://zenodo.org/record/3490687">https://zenodo.org/record/3490687</a>).<br><br>Note that these networks are derived entirely from remotely sensed data, but do form a single interconnected network across the wider SAFE landscape. Other stream network data are available () but the provenance of these are not well known and they only cover part of the SAFE network.<br><br>Details of the geoprocessing can be found here: <a href="https://www.safeproject.net/dokuwiki/safe_gis/stream_networks">https://www.safeproject.net/dokuwiki/safe_gis/stream_networks</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492009">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_SRTM_Stream_network_metadata.xlsx, SRTM_Channels_network.zip</p><p><b>SAFE_SRTM_Stream_network_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_Channels_network.zip</b></p><p>Description: Shapefile containing 54413 calculated segments forming a channel network across the wider SAFE landscape.</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet Properties)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 17</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>cat</b>: Identity of segment (Field type: id)</li><li><b>type_cd</b>: Segment type (see also type) (Field type: numeric)</li><li><b>ID</b>: Identity of segment (Field type: id)</li><li><b>length</b>: Length of network segment (Field type: numeric)</li><li><b>n_ponts</b>: Number of points in segment (Field type: numeric)</li><li><b>sourceX</b>: X coordinate of source point of segment (Field type: numeric)</li><li><b>sourceY</b>: Y coordinate of source point of segment (Field type: numeric)</li><li><b>sinkX</b>: X coordinate of sink point of segment (Field type: numeric)</li><li><b>sinkY</b>: Y coordinate of sink point of segment (Field type: numeric)</li><li><b>type</b>: Segment type - is the segment a source or outflow segment (connected at one end only, type_cd = 0) or internal (connected at both ends, type_cd=1) (Field type: categorical)</li><li><b>snkChnn</b>: Identity of sink channel for this segment (Field type: id)</li><li><b>srcChnn</b>: Identities of source channels for this segment (Field type: id)</li><li><b>nSourcs</b>: Count of source channels (Field type: numeric)</li><li><b>sorcElv</b>: Elevation of start point of segment (Field type: numeric)</li><li><b>sinkElv</b>: Elevation of sink point of segment (Field type: numeric)</li><li><b>sorcFlw</b>: Incoming flow at segment source (Field type: numeric)</li><li><b>sinkFlw</b>: Outgoing flow at segment sink (Field type: numeric)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Alternative stream network data for the SAFE Project
<b>Description: </b><p>This zipfile contains three shapefiles of linear geometries showing channel networks across the SAFE landscape.<br></p><ul><br><li>RIVERS_UTM.shp: From Igor Lysenko's SAFE pack, this is a river network with two areas, covering Maliau and the area around the SAFE experimental region. The network density is much higher in Maliau than SAFE and the channel network around SAFE is not complete. This is projected in UTM50N WGS84.</li><br><li>LFEriver_SL.shp: This is provided (by Sarah Luke?) through Clare Wilkinson's GIS files and adds a critical missing stream for the Logged Forest Edge (LFE) catchment. This is projected in UTM50N WGS84.</li><br><li>all_rivers.shp: This is provided through Clare Wilkinson's GIS files and provides streams for a single region covering Danum and the SAFE experimental region but not sampled watersheds in Oil Palm plantations to the south of SAFE. The original file is missing projection information (no .prj file) but other files in the same dataset are projected in RSO Timbalai 1948 and using this projection fits with the context of other data. The version uploaded onto Zenodo has been reprojected into UTM50N WGS84.</li><br></ul><br><br>Although the files are not consistent, they do contain channels and channel data that are referenced in some studies. The provenance of these files are unknown, although the following suggests that they may be traced using GPS or from imagery:<br><ul><br><li>Incomplete coverage within the regions they cover.</li><br><li>Treatment of larger rivers, changing from a single line feature showing the stream centreline (?) to double lines showing the river banks.</li><br><li>Variation in network density: the western edge of all_rivers.shp shows a vertical band about 4 km wide of higher stream density than the rest of the region; RIVERS_UTM.shp shows marked differences in stream density between SAFE and Maliau.</li><br></ul><p></p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3492064">here</a></p><p><b>Files: </b>This dataset consists of 2 files: SAFE_Alternative_Stream_network_metadata.xlsx, Preexisting_SAFE_river_files.zip</p><p><b>SAFE_Alternative_Stream_network_metadata.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>Preexisting_SAFE_river_files.zip</b></p><p>Description: Contains three shapefiles of channel networks.</p><p>This file contains 3 data tables:</p><ol><li><p></p><p><b>Feature properties</b> (described in worksheet LFEriver_SL)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 1</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>Id</b>: Identity of river (Field type: id)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet RIVERS_UTM)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 1</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>NAME</b>: Local name of segment (Field type: comments)</li></ul><p></p></li><li><p></p><p><b>Feature properties</b> (described in worksheet all_river_UTM50N_WGS84)</p><p>Description: Field descriptions for shapefile properties</p><p>Number of fields: 10</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>FNODE_</b>: Unknown (Field type: numeric)</li><li><b>TNODE_</b>: Unknown (Field type: numeric)</li><li><b>LPOLY_</b>: Unknown (Field type: numeric)</li><li><b>RPOLY_</b>: Unknown (Field type: numeric)</li><li><b>LENGTH_MET</b>: Length of channel segment in metres (Field type: numeric)</li><li><b>RIV_YSC_</b>: Unknown (Field type: numeric)</li><li><b>RIV_YSC_ID</b>: Unknown (Field type: numeric)</li><li><b>CODE</b>: Unknown (Field type: numeric)</li><li><b>NAME</b>: Local name of segment (Field type: comments)</li><li><b>AREA</b>: Local area of segment (Field type: comments)</li></ul><p></p></li></ol><p><b>Date range: </b>2010-10-01 to 2019-10-01</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation [Models]
<p>This file contains the pretrained models and the evaluation of the pipelines described in the paper <em>Convolutional Neural Networks for Classification of Alzheimer’s Disease: Overview and Reproducible Evaluation</em>.</p> <p>Source code can be downloaded at: <a href="https://github.com/aramis-lab/AD-DL">https://github.com/aramis-lab/AD-DL</a></p> <p>Also, single files can be obtained at: <a href="https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/">https://aramislab.paris.inria.fr/clinicadl/files/models/v0.0.1/</a></p> <p>The structure of the compressed file is as follows:</p> <p>clinicadl_models/<br> ├── 2D_slice<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── AD_CN_dataleakage<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ └── AD_CN<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_patch<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_ROI_based<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── 3D_subject<br> │ ├── baseline<br> │ │ ├── AD_CN<br> │ │ │ ├── best_model<br> │ │ │ └── performances<br> │ │ └── sMCI_pMCI<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── longitudinal<br> │ ├── AD_CN<br> │ │ ├── best_model<br> │ │ └── performances<br> │ └── sMCI_pMCI<br> │ ├── best_model<br> │ └── performances<br> ├── autoencoders<br> │ ├── 3D_patch<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ ├── 3D_ROI_based<br> │ │ ├── baseline<br> │ │ │ └── best_model<br> │ │ └── longitudinal<br> │ │ └── best_model<br> │ └── 3D_subject<br> │ └── baseline<br> │ ├── extensive<br> │ └── minimal<br> └── svm<br> ├── baseline<br> │ ├── AD_CN<br> │ │ ├── all_subjects.tsv<br> │ │ └── classifier<br> │ └── sMCI_pMCI<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── longitudinal<br> ├── AD_CN<br> │ ├── all_subjects.tsv<br> │ └── classifier<br> └── sMCI_pMCI<br> ├── all_subjects.tsv<br> └── classifier</p> <p>We provide the pretrained CNN models for the frameworks 3D subject-level, 3D ROI-based, 3D patch-level and 2D slice-level. This models can be found as a <strong><em>.pth.tar</em> </strong>file (<em>Pytorch</em> format) inside the <em>best_model</em> folder for each framework (and for each fold). We also provide the autoencoders that initialize the training stage of the CNN networks. The <em>performances </em>folder contains the computed metrics for the correponding model (ACC, BA, etc). <em> </em></p> <p>For the svn classification, we provide files with the dual coefficients, the support vector indices and the weights. Also, <em>tsv</em> files with the subject list.</p>
Benchmark circuits used in the work entitled "Don't-care-based node minimization for threshold logic networks"
<p>Benchmark circuits used in the work entitled "Don't-care-based node minimization for threshold logic networks".<br> There are two sets of threshold logic networks (TLNs), K6 and K15, generated with the method proposed in Effective Logic Synthesis Flow for Threshold Logic Circuit Design (https://ieeexplore.ieee.org/document/8355999) by Augusto Neutzling, et al.<br> In K6, each node has at most 6 fanins.<br> In K15, each node has at most 15 fanins.</p> <p>Contents:<br> ori/blif: Original TLNs in the blif format.<br> ori/th: Original TLNs in the threshold logic format.<br> opt/blif: Optimized TLNs in the blif format.<br> opt/th: Optimized TLNs in the threshold logic format.</p>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
Machine learning pipeline to train toxicity prediction model of FunTox-Networks
<p>Machine Learning pipeline used to provide toxicity prediction in FunTox-Networks</p> <p>01_DATA # preprocessing and filtering of raw activity data from ChEMBL<br> - Chembl_v25 # latest activity assay data set from ChEMBL (retrieved Nov 2019)<br> - filt_stats.R # Filtering and preparation of raw data<br> - Filtered # output data sets from filt_stats.R<br> - toxicity_direction.csv # table of toxicity measurements and their proportionality to toxicity</p> <p>02_MolDesc # Calculation of molecular descriptors for all compounds within the filtered ChEMBL data set<br> - datastore # files with all compounds and their calculated molecular descriptors based on SMILES<br> - scripts<br> - calc_molDesc.py # calculates for all compounds based on their smiles the molecular descriptors<br> - chemopy-1.1 # used python package for descriptor calculation as decsribed in: https://doi.org/10.1093/bioinformatics/btt105</p> <p>03_Averages # Calculation of moving averages for levels and organisms as required for calculation of Z-scores<br> - datastore # output files with statistics calculated by make_Z.R<br> - scripts<br> -make_Z.R # script to calculate statistics to calculate Z-scores as used by the regression models<br> <br> 04_ZScores # Calculation of Z-scores and preparation of table to fit regression models<br> - datastore # Z-normalized activity data and molecular descriptors in the form as used for fitting regression models<br> - scripts<br> -calc_Ztable.py # based on activity data, molecular descriptors and Z-statistics, the learning data is calculated</p> <p>05_Regression # Performing regression. Preparation of data by removing of outliers based on a linear regression model. Learning of random forest regression models. Validation of learning process by cross validation and tuning of hyperparameters.</p> <p>- datastore # storage of all random forest regression models and average level of Z output value per level and organism (zexp_*.tsv)<br> - scripts<br> - data_preperation.R # set up of regression data set, removal of outliers and optional removal of fields and descriptors<br> - Rforest_CV.R # analysis of machine learning by cross validation, importance of regression variables and tuning of hyperparameters (number of trees, split of variables)<br> - Rforest.R # based on analysis of Rforest_CV.R learning of final models</p> <p>rregrs_output<br> # early analysis of regression model performance with the package RRegrs as described in: https://doi.org/10.1186/s13321-015-0094-2</p>
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.