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9,152 results for “Networking”
The Virtual Macaque Brain: A macaque connectome for large-scale network simulations in TheVirtualBrain
<p>A whole-cortex macaque structural connectome constructed from a combination of axonal tract-tracing and diffusion-weighted imaging data. Created for modeling brain dynamics using TheVirtualBrain platform. Website: thevirtualbrain.org</p>
Data and code for Freshwater corridors in the conterminous US: a coarse-filter approach based on lake-stream networks
<p>This repository contains various datasets used to map and analyze freshwater connectivity (i.e., corridors) in the conterminous US based on networks of lakes, streams and rivers. We considered lake-stream networks as analogous to habitat corridors. Hub lakes are individual lakes that are disproportionately important for maintaining intact networks. We also analyzed the protection status of freshwater connectivity using the US Protected Areas Database v. 2.0. R analysis scripts can also be found in this repository. Much of the data we used came from published or soon-to-be published sources, which are referenced below.</p>
Sparsified Model Zoo Twins: A Dataset of Sparsified Populations of Neural Network Models - MNIST
<p><strong>Abstract</strong></p> <p>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 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 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.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the sparsified twins of models trained on MNIST. The original population is made available at https://doi.org/10.5281/zenodo.6632086. Sparsification is done using Variational Dropout, starting from the last epoch of the original population. The zip file contains the sparsification trajectory for 25 epochs for all 1000 models. All zoos with extensive information and code can be found at www.modelzoos.cc.</p> <p> </p>
Sparsified Model Zoo Twins: A Dataset of Sparsified Populations of Neural Network Models - SVHN
<p><strong>Abstract</strong></p> <p>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 27 model zoos with varying hyperparameter combinations are generated and includes 50’360 unique neural network models resulting in over 2’585’360 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.</p> <p><strong>Dataset</strong></p> <p>This dataset is part of a larger collection of model zoos and contains the sparsified twins of models trained on SVHN. The original population is made available at https://doi.org/10.5281/zenodo.6632120. Sparsification is done using Variational Dropout, starting from the last epoch of the original population. The zip file contains the sparsification trajectory for 25 epochs for all 1000 models. All zoos with extensive information and code can be found at www.modelzoos.cc.</p>
Influence of social networks as a distribution channel in volatile markets of non-fungible tokens..
<p>Dataset with tweets, prices (ETH & USD), volume (USD) and sentiment from BAYC, WOW and CoolCats NFTs. </p> <p>Obtained from Twitter and from the Ethereum blockchain using Dune Analytics (Dune.xyz)</p>
Functional precision profiling reveals non-mutational rewiring of kinase signaling networks in colorectal cancer
<p>Multi-omics profiling of colorectal cancer (CRC) patients and associated patient-derived organoids. Tumor organoids were characterized in steady-state and perturbed using kinase inhibitors.</p>
A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities
<p>This file contains the data that was used in the paper titled "A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities" which will be published in Frontiers of Pharmacology.</p>
Global sea surface dimethyl sulfide dataset simulated by artificial neural network
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 2005 to 2014 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes; (3) the simulated global monthly sea surface concentrations of DMS ranging from 2005 to 2100 by ANN model and CMIP6 ensemble and the calculated Kt and sea-to-air fluxes; (4) the yearly mean DMS concentration of each grid in different sensitivity experiments exploring the roles different variables play in driving DMS future changes. The input variables of this ANN model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The future projections (2015-2100) are subjected into two Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L<sup>–1</sup>, m s<sup>–1</sup>, and μmol S m<sup>–2</sup> d <sup>–1</sup>, respectively.</p> <p>Compared with the previous version (v1.0), this version is based on an updated ANN model after adjusting the data match-up between satellite and in-situ chlorophyll <em>a</em> for ANN training. In addition, the historical simulation based on CMIP6 only covers the time period from 2005 to 2014, which was from 1850 to 2014 for v1.0.</p>
Data from: Network-based biostratigraphy for the late Permian to mid-Triassic Beaufort Group (Karoo Supergroup) in South Africa enhances biozone applicability and stratigraphic correlation
<p>The Permo-Triassic vertebrate assemblage zones (AZs) of South Africa's Karoo Basin are a standard for local and global correlations. However, temporal, geographical, and methodological limitations challenge the AZs reliability. We analyze a unique fossil dataset comprising 1408 occurrences of 115 species grouped into 19 stratigraphic bin intervals from the <em>Cistecephalus</em>, <em>Daptocephalus</em>, <em>Lystrosaurus</em> <em>declivis</em>, and <em>Cynognathus</em> AZs. Using network science tools we compare six frameworks: Broom, Rubidge, Viglietti, Member, Formation, including a framework suggesting diachroneity of the <em>Daptocephalus</em>/<em>Lystrosaurus</em> AZ boundary (Gastaldo). Our results demonstrate that historical frameworks (Broom, Rubidge) still identify the Karoo AZs. No scheme supports the <em>Cistecephalus</em> AZ, and it likely comprises two discrete communities. The <em>Lystrosaurus</em> <em>declivis</em> AZ is traced across all frameworks, despite many shared species with the underlying <em>Daptocephalus</em> AZ, suggesting the extinction event across this interval is not a statistical artifact. A community shift at the upper Katberg to lower Burgersdorp formations may indicate a depositional hiatus, which has important implications for regional correlations and Mesozoic ecosystem evolution. The Gastaldo model still identifies a <em>Lystrosaurus</em> and <em>Daptocephalus</em> AZ community shift, does not significantly improve recent AZ models (Viglietti), and highlights important issues with some AZ studies. Localized bed-scale lithostratigraphy (sandstone datums), and singleton fossils cannot be used to reject the patterns shown by hundreds of fossils, and regional chronostratigraphic markers of the Karoo foreland basin. Meter-level occurrence data suggest that 20–50 m sampling intervals capture Karoo AZs, unifying the use of meter-level placements of singleton fossils to delineate biozone boundaries and make regional correlations.</p>
Comparing the representation of a simple visual stimulus across the cerebellar network [dataset]
<p>This repository contains all the raw data that has been used to generate the figures in the paper "Comparing the representation of a simple visual stimulus across the cerebellar network".<br> To use the dataset, select all 10 .zip files provided here and unpack them simultaneously.</p> <p> </p> <p>The dataset contains the following folders:</p> <ul> <li>freely_swimming_beh folder contains the data for the freely swimming behavior experiments. Inside, subfolders for each behavioral experiment run can be found, containing the experiment metadata and the behavioral log.</li> <li>flashes folder contains the data for the imaging experiments with the FLASHES protocol. Within this folder, three subfolders can be found with the data for each one of the three different types of imaged cell types (GCs, IONs and PCs). Within those subfolders, a folder for each imaged fish can be found, containing a data dictionary with the extracted ROI traces, and a folder with the experiment metadata.</li> <li>steps folder contains the data for the imaging experiments with the STEPS protocol. Within this folder, three subfolders can be found with the data for each one of the three different types of imaged cell types (GCs, IONs and PCs). Within those subfolders, a folder for each imaged fish can be found, containing a data dictionary with the extracted ROI traces, and a folder with the experiment metadata.</li> <li>fitting folder contains data dictionaries with the results from the model fitting needed to reproduce the manuscript figures.</li> <li>data_dict4decoding_complete.h5 is a dictionary file containing all necessary data to reproduce the decoding experiments.</li> </ul>
Data From: Emulation of Cardiac Mechanics using Graph Neural Networks
<p>Contains simulation results of the forward displacement from beginning to end-diastole for approximately 3000 synthetically generated left ventricle geometries.</p> <p>The simulation results are split into training, validation and test data.</p> <p>The data is described in detail in a forthcoming publication in <em>Computer Methods in Applied Mechanics and Engineering</em> - further information will be provided upon publication. A GitHub repository will also be made available, with code for processing the simulation data and training a Graph Neural Network emulator.</p>
A waveform dataset in continuous mode of the Montefeltro seismic network (MF) in central-northern Italy from 2018 to 2020
<p>The Montefeltro seismic network (FDSN Network code: 1S) was deployed in the Apennines area of<br> northern Marche and southern Emilia-Romagna regions (central Italy). The network has been set up<br> starting from December 2018, and the array consists of stations equipped with dynamic digitizers<br> and three component short/extended/broad band seismometers (Guralp 3D/40s, Lennartz 3D/5s,<br> SS20 3D/0.5s sensors). The temporary network records in continuous mode at 100 sps. The data are<br> used to analyse the seismicity and the spatio-temporal evolution of small seismic sequences,<br> occurring in the considered area and surrounding zones, strongly clustered in time and space.<br> Stations (registered in ISC) in this Network:<br> Station code Location Station name Data acquisition<br> Lat(N) Lon(E) Ele(m) Start End<br> MF01 43.82150 12.57190 368 Auditore (PU) 2018-11-29 2020-12-31<br> MF02 43.86540 12.21070 626 Sant'Agata Feltria (RN) 2019-04-19 2020-05-30<br> MF03 43.84860 12.47990 541 Monte Grimano (PU) 2019-10-07 2020-12-31<br> MF04 43.81030 12.05620 1043 Verghereto (FC) 2020-10-09 2020-12-31<br> The data of dataset files are miniseed formatted and subdivided by the following tree:<br> (1) the dataset is divided by years;<br> (2) the divided by years dataset is subdivided by stations;<br> (3) finally, the data are divided by days of each year in every station folder.</p> <p><br> Response information:<br> Station code Sensor Recorder Recorder period<br> Start End<br> MF01 Lennartz 3D/5s Reftek 130 2018-11-29 2019-10-07<br> MF01S Lennartz 3D/5s Sara SL06 2019-10-07 2020-12-31<br> MF02 Guralp CMG/20s Reftek 130 2019-04-19 2020-05-30<br> MF03 Guralp CMG/30s Reftek 130 2019-10-07 2020-12-31<br> MF04 Sara SS02/0.5s Sara SL06 2020-10-09 2020-12-31</p> <p>In this dataset the data recorded by MF01 station are been acquisited by different recorders,<br> the firth record period by Reftek 130 (MF01) and the second record period by Sara SL06 (MF01S).</p> <p>List for the responses of the Seismic Instruments<br> Guralp CMG/20s sensor response: RESP_XX_NS444_BHZ_CMG40T_20_50_800.txt<br> Guralp CMG/30s sensor response: RESP_XX_NS041_BHZ_CMG40T_30_800.txt<br> Lennartz 3D/5s sensor response: RESP_XX_NS484_SHZ_LE-3D5sMkIII_5_800.txt<br> Sara SS02/5s sensor response: RESP_XX_NS505_SHZ_SS02_5.txt<br> Reftek 130 datalogger response: RESP_XX_NR008_HHZ_130_1.txt<br> Sara SL06 datalogger response: RESP_XX_NS000_HHZ_SL06_88_L22x3_100_4.txt</p>
Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant-pollinator networks along gradients of climate and landscape conditions
<p>Understanding how niche-based and neutral processes contribute to the spatial variation in plant-pollinator interactions is central to designing effective pollination conservation schemes. Such schemes are needed to reverse declines of wild bees and other pollinating insects and to promote pollination services to wild and cultivated plants. We used data on wild bee interactions with plants belonging to the four tribes Loteae, Trifolieae, Anthemideae, and either spring- or summer-flowering Cichorieae, sampled systematically along a 682km latitudinal gradient to build models that allowed us to (a) predict occurrences of pairwise bee-flower interactions across 115 sampling locations, and (b) estimate the contribution of variables hypothesized to be related to niche-based assembly structuring processes (viz. annual mean temperature, landscape diversity, bee sociality, bee phenology, and flower preferences of bees) and neutral processes (viz. regional commonness and dispersal distance to conspecifics). While neutral processes were important predictors of plant-pollinator distributions, niche-based processes were reflected in the contrasting distributions of solitary bee and bumble bees along the temperature gradient, and in the influence of bee flower preferences on the distribution of bee species across plant types. In particular, bee flower preferences separated bees into three main groups, albeit with some overlap: visitors to spring-flowering Cichorieae; visitors to Anthemideae and summer-flowering Cichorieae; and visitors to Trifolieae and Loteae. Our findings suggest that both neutral and niche-based processes are significant contributors to the spatial distribution of plant-pollinator interactions so that conservation actions in our region should be directed towards areas: near high concentrations of known occurrences of regionally rare bees; in mild climatic conditions; and that are surrounded by heterogeneous landscapes. Given the observed niche-based differences, the proportion of functionally distinct plants in flower-mixes could be chosen to target bee species, or guilds, of conservation concern.</p>
Hypothetical landscapes to evaluate connectivity metrics of protected area networks.
<p>This repo contains the raw datasets (as GIS shapefiles) useful to evaluate connectivity metrics of protected area networks. Please suggest if additional landscapes could be added that would be useful to evaluate an additional class or characteristic of protected area networks. They were created using Google Earth Engine script: <strong><a href="https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160">https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160</a>.</strong></p> <p>Two shapefiles are provided: (1) ProNet_connectivity_library_L1_26pa -- this contains polygons that represent the size and shape of protected areas (PAs); (2) ProNet_connectivity_library_L1_26pae -- this contains polylines that represent "edges" that do not represent any protected area but denotes that two PAs are connected. Note that these landscapes are fictitious, and represented at the global origin (i.e. 0.0 degrees latitude and 0.0 degrees longitude) -- and are quite small so zooming in will be required to see them in GIS software.</p>
TrueFace: a Dataset for the Detection of Synthetic Face Images from Social Networks
<p>TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.</p> <p>Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.</p>
DeepSelectNet: Deep Neural Network Based Selective Sequencing for Oxford Nanopore Sequencing
<p>Curated dataset for the manuscript named "DeepSelectNet: Deep Neural Network Based Selective Sequencing for Oxford Nanopore Sequencing".</p> <p>Five publicly available datasets sequenced on ONT MinION/GridION were used for the experiments (see below for original sources). These datasets contained raw signal data in single-FAST5 format (one file per each read), which were converted to BLOW5 format using slow5tools to enable convenient and efficient file manipulation. Then, 40,000 reads containing at least 4500 signal samples were extracted from each dataset. From each dataset, 20,000 reads are for training (<species>/train-<species>.blow5) and the rest for testing (<species>/test-<species>.blow5). Basecalled reads for the dataset used for testing are also available (test-<species>.fastq). Guppy version 6.1.3 under dna_r9.4.1_450bps_hac mode was used. The reference genomes are also given (<species>/<species>-ref.fasta)</p> <p>Original datasets are from the following sources:<br> SARS-CoV-2: https://community.artic.network/t/links-to-raw-fast5-fastq-data-for-artic-protocol/17<br> Zymo Metagenome: https://github.com/LomanLab/mockcommunity<br> Chlamydomonas: https://sra-download.ncbi.nlm.nih.gov/traces/era20/ERZ/003237/ERR3237140/Chlamydomonas_0.tar.gz<br> Saccharomyces cerevisiae: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA510813</p>
Introduction to the Future Blood Testing Network Plus - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)
<p>This video is the first talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Introduction - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ </p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/YuBsU3NDdB0</p>
Neural network for Lithium Metal Battery
<p>Electric cars are an integral part of our clean energy future – every time one replaces a gas-powered vehicle, it can save 1.5 tons of carbon dioxide per year. But to truly expand the population and reach of electric cars, new energy storage solutions must be developed to produce lighter vehicles with longer ranges and more powerful batteries.</p> <p>A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Irvine recently moved this effort forward with the development of deep-learning algorithms to automate the quality control and assessment of new battery designs.</p> <p>The research team, led by Berkeley Lab’s Daniela Ushizima, a staff scientist in the Applied Mathematics and Computational Research Division and a Berkeley Institute for Data Science Research Affiliate, includes scientists from the National Fuel Cell Research Center (NFRC) at UC Irvine and collaborators from UC Berkeley’s Department of Electrical Engineering and Computer Sciences and School of Information. Together, they created these deep-learning algorithms to automate the inspection of batteries with data acquired using advanced instruments, including those at Berkeley Lab’s Advanced Light Source (ALS). By using X-ray tomography as the input data, as well as prototypes defined by battery experts, the research team developed automated methods to detect battery defects in rechargeable lithium metal batteries and measure their growth during battery cycling.</p> <p>The researchers focused on solid-state lithium metal batteries (LMB), which are different from traditional lithium-ion batteries in that they use solid electrodes and electrolytes, providing superior electrochemical performance and high energy density. Some of the challenges of this new technology are predicting battery cycling stability and preventing the formation of lithium dendrite growth, Ushizima noted. This harmful phenomenon may occur during LMB charge and discharge, when lithium can deposit irregularly, building up dendrites (lithium plating) that lead to failures, such as short-circuiting. These morphologies are key to the LMB quality, and they can be captured and analyzed using X-ray microtomography (XRT) scans. Machine learning algorithms and multiscale representation of XRT from LMB samples enable the quantification of LMB defects.</p>
Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).
<p>This video is the first talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Future Blood Testing Network+ Overview - Dr Weizi (Vicky) Li (Henley Business School, University of Reading).</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/I1xsig9C8w0</p>
Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton)
<p>This video is the sixth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton)</p> <p>Bio: Prof Jeremy Frey Professor of Physical Chemistry, Head of Computational Systems Chemistry, University of Southampton (UoS). He is PI of AI for Scientific Discovery Network+, and co_I on the Internet of Food Things Digital Economy Network+ and has had considerable involvement in the UK e-Science and Digital Economy programmes for many years (e.g., PI of the Digital Economy IT as a Utility Network+. He is a strong proponent of interdisciplinary research and the use of digital technology and ideas to enhance methods of scientific research & development. His own research involves activities across the physical land life sciences, from the application of novel mathematical analysis (e.g., Topological Data Analysis), laser spectroscopy and imagining techniques to chemical and biological problems, with the development of sensors and imagining systems such as the novel soft x-ray microscope. In parallel he works on the integration of these techniques with full provenance environment into laboratory systems using semantic web technologies.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/eNORwfMy5cE</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.