Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,152
datasets available to search
ShareScore release 0.9.0
Dataset results
9,152 results for “Networking”
On the flexibility of the cellular amination network in E. coli
<p>Ammonium (NH4<sup>+</sup>) is essential to generate the nitrogenous building blocks of life. It gets assimilated via the canonical biosynthetic routes to glutamate and is further distributed throughout metabolism via a network of transaminases. To study the flexibility of this network, we constructed an <em>Escherichia coli</em> glutamate auxotrophic strain. This strain allowed us to systematically study which amino acids serve as amine source and found that several amino acids complement the auxotrophy, either by producing glutamate via transamination reactions or by their conversion to glutamate. In this network, we identified aspartate transaminase AspC as a major connector between many amino acids and glutamate. Additionally, we extended the transaminase network by the amino acids β-alanine, alanine, glycine and serine as new amine sources and identified D-amino acid dehydrogenase (DadA) as an intracellular amino acid sink removing substrates from transaminase reactions. Finally, ammonium assimilation routes producing aspartate or leucine were introduced. Our study reveals the high flexibility of the cellular amination network, both in terms of transaminase promiscuity and adaptability to new connections and ammonium entry points.</p>
10Be concentrations constraining surface age and valley growth rate in a seepage-derived drainage network in the Apalachicola River basin, Florida
<p class="Head1"><span><span>Measuring rates of valley head migration and determining the timing of canyon-opening are insightful quantifications for the history and evolution of planetary surfaces. Horizontal spatial gradients of <em>in situ-</em>produced cosmogenic nuclide concentrations provide a framework for assessing the migration of these and similar topographic features. We developed a theoretical model for the concentration of <em>in situ</em> produced cosmogenic radionuclides in valley walls during retreat of a valley head. The retreat rate is inversely proportional to the magnitude of the spatial concentration gradient and proportional to local nuclide accumulation rates. By solving for a spatial gradient in concentration along a valley parallel transect, we created an expression for the explicit determination of valley head retreat, termed unzipping. We applied this theory to a developing seepage-derived drainage network along the Apalachicola River, Florida, USA. Sample concentrations along a valley margin transect vary systematically from 2.9 x 10<sup>5</sup> atoms/g to 3.5 x 10<sup>5</sup> atoms/g resulting in a gradient of 160 atoms/g/m, and from this value a valley head retreat rate of 0.025 m/y is found. The discrepancy between overall network age and current rates of valley head migration suggests intermittent network growth which is consistent with glacial-interglacial precipitation variations during the Pleistocene. This method can be applied to a wide range of Earth-surface environments. For the <sup>10</sup>Be system, this method should be sensitive to unzipping rates bounded between 10<sup>-6</sup> m/y and 10<sup>0</sup> m/y.</span></span></p>
TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet
<p>We develop a Convolutional Neural Network, named SIFnet, that increases the spatial resolution of SIF from the TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations together with a broad range high resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.</p> <p>Temporal coverage: April 2018 until March 2021, 16 day time steps</p> <p>Data for other regions can be requested and produced by the authors. Please refer for further information to: </p> <p>Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., & Chen, J. (2022). A Convolutional Neural Network for Spatial Downscaling of Satellite-Based Solar-Induced Chlorophyll Fluorescence (SIFnet). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). <em>Biogeosciences</em>, <em>19</em>(6), 1777-1793. DOI: https://doi.org/10.5194/bg-19-1777-2022</p>
Peat macropore networks – new insights into episodic and hotspot methane emission
<p>The data and scripts are related to the manuscript "Peat macropore networks – new insights into episodic and hotspot methane emission" by Petri Kiuru, Marjo Palviainen, Tiia Grönholm, Maarit Raivonen, Lukas Kohl, Vincent Gauci, Iñaki Urzainki, and Annamari (Ari) Laurén submitted to Biogeosciences.</p>
Training dataset of Hela cells for oneat network training
<p>Here we present the tif files to be used to trainthe Oneat networks for detection of division events for Hela cells imaged under different imaging modalities and presented originally here: https://zenodo.org/record/6139958#.Yjcjl3rMJD8</p>
Nanosecond Optical Switching and Control System for Data Center Networks(data)
<p>This file contains the data and codes for the paper "Nanosecond Optical Switching and Control System for Data Center Networks" </p>
Supplementary material 1 from: Fernandes N, Ferreira EM, Pita R, Mira A, Santos SM (2022) The effect of habitat reduction by roads on space use and movement patterns of an endangered species, the Cabrera vole Microtus cabrerae. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 177-196. https://doi.org/10.3897/natureconservation.47.71864
The effect of habitat encroachment by roads on space use and movement patterns of an endangered vole
Self-generated gradients steer collective migration on viscoelastic collagen networks
<p>Minimal Raw Data Set for:</p> <p>Self-generated gradients steer collective migration on viscoelastic collagen networks</p> <p>Clark et al, 2022</p>
Supplementary data for the article "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."
<p>Supplementary figures, tables and raw data for the article " "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."</p> <p><strong>Food sharing can occur in both social and non-social species but is crucial in eusocial species in which only some group members collect food. This food collection but also intranidal food distribution through trophallactic (i.e. mouth-to-mouth) exchanges are fundamental issues in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchanges are poorly understood. In this study, we provide new insights </strong><strong>into</strong><strong> the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective </strong><strong>levels</strong><strong>. We also test the alternative hypotheses (no variability and no division of labour). Division of labour with inter-individual variability predicts contrary to other models the food dynamics and exchange networks. We establish the links between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and network of trophallactic events</strong>.<strong> Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the ones observed in ants leading to better understanding of evolution of such societies.</strong></p> <p> </p>
Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks
<p>Supplementary Tables - Version 2</p>
Neural Network Adaption for Depth Sensor Replication (Datasets)
<p>This repository contains the datasets used in the paper "Neural Network Adaption for Depth Sensor<br> Replication". They contain RGB-D data that was recorded using iPads where the Structure_Sensor set was recorded using an Occipital Structure Sensor while Apple_Lidar used the inbuilt iPad LiDAR sensor with enabled smoothing by ARKit. The Structure Sensor data is scaled so that the maximum value is 4 meters while the LiDAR data is scaled to 8 meters as it provides more accuracy at longer ranges.</p>
Dataset of bootup power consumption traces for four networking equipments.
<p>This dataset contains a collection of four NumPy files (.npy) that contain around 500 examples of power consumption. These power consumptions corresponds to the bootup sequences for four different networking equipment (two routers and two switches).</p>
Network Pharmacology-based Approach combined with Bioinformatic Analytics to Elucidate the Potential of Curcumol Against Hepatocellular Carcinoma
<p>File S1: Expression distribution of common targets; File S2: GSEA result of common targets, File S3: Rare data for top 10 most enriched GO terms in biological process, cellular component, and molecular function of common targets, File S4: Rare data for KEGG enrichment result of common targets, File S5: Rare data for cluster classification performed by MCODE, File S6: Rare data for GO terms and KEGG pathways enrichment analyses of cluster 1 and cluster 2, File S7: Prognosis-related genes, Table S1: Putative targets of curcumol.</p>
Network of the Sîn-ilī family (Babylon)
<p>This dataset was created to analyse the social network of the Sîn-ilī family, who lived in Babylon in the 6<sup>th</sup> century B.C., particularly with regard to the distribution of theophoric name elements in this network. This dataset comprises prosopographical information extracted from the published records of this family’s private archive, as well as information on the theophoric elements used in the personal names, patronyms and family names of the individuals.</p> <p>The dataset was created in the Centre of Excellence “Ancient Near Eastern Empires” (ANEE), Helsinki in 2020. I thank the members of the ANEE for their technical support, and also Cornelia Wunsch, who kindly discussed the family relations of the Sîn-ilī’s with me.</p> <p> </p>
Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks
<p>The rapid and accurate taxonomic identification of fossils is of great significance in paleontology, biostratigraphy, and other fields. However, taxonomic identification is often labor-intensive and tedious, and the requisition of extensive prior knowledge about a taxonomic group also requires long-term training. Moreover, identification results are often inconsistent across researchers and communities. Accordingly, in this study, we used deep learning to support taxonomic identification. We used web crawlers to collect the Fossil Image Dataset (FID) via the Internet, obtaining 415,339 images belonging to 50 fossil clades. Then we trained three powerful convolutional neural networks on a high-performance workstation. The Inception ResNet v2 architecture achieved an average accuracy of 0.90 in the test dataset when transfer learning was applied. The clades of microfossils and vertebrate fossils exhibited the highest identification accuracies of 0.95 and 0.90, respectively. In contrast, clades of sponges, bryozoans, and trace fossils with various morphologies or with few samples in the dataset exhibited a performance below 0.80. Visual explanation methods further highlighted the discrepancies among different fossil clades and suggested similarities between the identifications made by machine classifiers and taxonomists. Collecting large paleontological datasets from various sources, such as the literature, digitization of dark data, citizen-science data, and public data from the Internet may further enhance deep learning methods and their adoption. Such developments will also possibly lead to image-based systematic taxonomy to be replaced by machine-aided classification in the future. Pioneering studies can include microfossils and some invertebrate fossils. To contribute to this development, we deployed our model on a server for public access at www.ai-fossil.com.</p>
Shared Data for De-scattering Deep Neural Network
<p>These images show mouse cortical layer 2/3 pyramidal neurons sparsely labeled with a cell fill (eYFP or mScarlet-I) to visualize the dendritic arbor, including dendritic spines. Cells were imaged in vivo using point-scan two-photon microscopy (PSTPM) and temporal focusing microscopy (TFM). These images were used for training and testing the De-Scattering Deep Neural Network (<a href="https://github.com/eleweiz/DeScattering_NN">https://github.com/eleweiz/DeScattering_NN</a>).</p>
Source data and code for manuscript 'An executive network for the control of sequence-behavior in pigeons'
<p>The contents of this folder are part of the submission of the manuscript entitled 'An executive network for the control of sequence-behavior in pigeons', by Lukas Alexander Hahn & Jonas Rose</p> <p>Contact: lukas.hahn@ruhr-uni-bochum.de</p> <p>Data and code have been compressed into a .zip folder each. Unpack the contents of the folders to use the dataset. The dataset is split into two main folders and one Matlab file:</p> <p>'code'<br> contains all analysis code to produce all figures and reported statistics of the manuscript (refer to the<br> MATLAB live script 'manuscriptResultsLiveScript.mlx' to run the analysis, please adjust the path information of where the data is stored on your computer).</p> <p>'RESULTSSTATISTICS.mat'<br> contains all reported statistical values (generated by 'manuscriptResultsLiveScript.mlx')</p> <p>'sourceData'<br> Contains all required source data files (i.e. pre-processed data) required to run the analyses stored in 'code'.</p> <p>Data related to animal behavior was recorded using MATLAB (R2016b). Electrophysiological data was recorded by NeuroNexus microelectrodes and an INTAN RHD2000 headstage on an INTAN USB-Interface board, with a sampling rate of 30 kHz and was subsequently filtered for spike sorting at bandpass 0.5 - 7.5 kHz.</p> <p>Data format is the MATLAB '.mat' type (which can be loaded in by MATLAB, or alternatively by the freely available Octave Software (https://www.gnu.org/software/octave)).<br> Data is organized in MATLAB structures, one file per session for behavioral results, one file per neuron for different alignments and preprocessing conditions (refer to manuscriptResultsLiveScript).<br> Structures contain individual matrices (labelled by a descriptive name) that contain numerical values or character strings.<br> Matrices labelled by the keyword 'Info' contain character strings that give a brief description of the loaded data.<br> Source data contains two separate folders containing data of animal 1 ('P855'), and animal 2 ('T1003').</p> <p>Data was sorted into different subsets, for analysis of individual task phases. Subfolder 'NCL' refers to 'nidopallium caudolaterale', 'NIML' refers to 'nidopallium intermedium mediale pars laterale', the recorded brain regions.</p> <p> </p>
Supplementary Table of Mechanism Unravelling of Sodium-glucose Cotransporter-2 Inhibitors against Diabetic Nephropathy via Network Pharma-cology and Molecular Docking
<p>This is the supplementary tables of article named"Mechanism Unravelling of Sodium-glucose Cotransporter-2 Inhibitors against Diabetic Nephropathy via Network Pharmacology and Molecular Docking"</p>
Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads
<p>Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads</p> <p>Please see the README for information on parsing and data structure.<br> Some of the data might need additional explanations. In case of confusion don't hesitate to contact me under jacob.palecek@outlook.com</p>
EM27/SUN network data CoMet 2018 Poland Silesia
<p>EM27/SUN network data sampled during the CoMet 2018 measurement campaign in the upper Silesian coal basin in Poland.</p> <p>The data set contains the raw interferograms recorded for all four sites. </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.