Find research datasets worth reusing

NicheNet-v2: final networks and ligand-target matrices

NicheNet-v2 networks and ligand-target matrices (for human and mouse). Can be used for NicheNet and MultiNicheNet analyses.

opencc-by-4.0Mar 2022View details →

Dataset & Code related to article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'

This record contains the 7768 lung masks manual annotations, implementation code, and pre-trained models related to the article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning' Also we include the visualised, selected top D reliable CT slices for all COVID-19 patients in the test dataset for better understanding.  For the detailed usage of the data and code, please refer to https://github.com/smallmax00/BAGCN-Covid19

The dataset of "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow"

There are the datasets of the research named "Characteristics of Urban Interaction Network in Shandong Province from the Perspective of Traffic Flow and Information Flow".

opencc-zeroSep 2022View details →

dryad32/100

Data from: Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks

River otters (Lontra canadensis) were extirpated from New Mexico by the 1950s. A limited reintroduction occurred during 2008–2010 in which 33 otters sourced from Washington (WA) were translocated to the Upper Rio Grande Basin (URG) of New Mexico. We conducted a noninvasive genetic capture-recapture survey during the winter of 2018 by collecting fecal DNA samples from river otter scats found at latrines in the URG dendritic network of perennial waterways. Our objectives were to: 1) estimate genetic diversity and effective population size; 2) genetic divergence from the WA source population and potential connectivity with regionally proximal populations; 3) spatially explicit population density and size; and 4) population growth rate since the founder event. Between February and April 2018, we collected 1,184 fecal DNA samples from 622 individual scats at 20 latrines; genotyping was attempted at 10 otter-specific microsatellite loci for a subsample of 543 samples. A bottlenecking founder effect was strongly supported, which, combined with genetic drift, reduced genetic diversity and effective population size by 20–26% and 106–170%, respectively, compared with the WA source population. Estimated population density from spatial capture-recapture models was 0.23–0.28 otter/km of waterway, or 1 otter/3.57–4.35 km of waterway, corresponding to a total population size of 83–100 otters across 359 km of the perennial dendritic network from La Mesilla, New Mexico to Alamosa National Wildlife Refuge, Colorado. Estimated average annual population growth rate since the founder event was 1.12–1.15/year. Despite successful population establishment, the URG river otter population remains small, is genetically degraded, and does not yet meet the criteria for long-term reintroduction success. Projections suggested that the population could reach the recommended minimum viable population size of ≥400 otters by the years 2030–2033, though sufficient habitat may not exist in the URG Basin to support that many otters.

opencc-by-4.0Jun 2022View details →

Supplementary Data - Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer network

Supplementary data 1: Peak annotation evaluation between MetDNA1 and KGMN (MetDNA2) Supplementary data 2: 46 standard mixture (46std_mix) and the knowledge-based metabolic reaction network Supplementary data 3: KGMN results of 46std_mix data set and validation results Supplementary data 4: KGMN results of NIST urine data sets and validation results Supplementary data 5: KGMN results of different biological samples Supplementary data 6: Recurrent unknowns of NIST urine via repository-mining Supplementary data 7: Table of adducts, neutral losses, empirical rules in KGMN

Emergence of Supercoiling-Mediated Regulatory Networks through the Evolution of Bacterial Chromosome Organization

Data generated using <a href="https://gitlab.inria.fr/tgrohens/evotsc">EvoTSC</a> and used in the <a href="https://doi.org/10.24072/pci.mcb.100198">Emergence of Supercoiling-Mediated Regulatory Networks through the Evolution of Bacterial Chromosome Organization</a> paper. This data is also used in Chapter 5 of my <a href="https://gitlab.inria.fr/tgrohens/phd">PhD thesis</a>.

opencc-by-4.0Nov 2021View details →

Network Theme: Continuous blood processing for patient specific therapies - Dr Robert Barker (University of Kent)

This video is the third talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: Continuous blood processing for patient specific therapies - Dr Robert Barker (University of Kent) Bio: Dr Robert Barker is an Associate Professor in Chemistry in the School of Physical Sciences, School Director of Public Engagement, project lead for the ‘Future Human’ Signature Research, University of Kent (UoK). He is a Royal Society Industrial Fellow and the PI of the trans-disciplinary EPSRC project Optimising Me Manufacturing Systems [EP/R022534/1], exploring continuous blood processing for healthcare and developing a healthcare micro-factory that provides on-the-body manufacturing of therapeutics, continuously and in response to the body’s needs. His work focuses on the whole blood processing and the development of a manufacturing system for T-cell immunotherapies which present a cure for some of the most aggressive forms of cancer (acute lymphoblastic leukaemia and chronic lymphoblastic leukaemia). His research is at the interface of industry and the NHS to meet their needs for continuous blood monitoring and to help the shift away from the current one-size-fits-all approach to deliver patient-specific therapies. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/T4UVEOM1cZU

opencc-by-4.0Nov 2021View details →

Network Theme: Sensor Technologies - Dr Sergiy Korposh (University of Nottingham)

This video is the second talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: Sensor Technologies - Dr Sergiy Korposh (University of Nottingham) Bio: Dr Sergiy Korposh is an Associate Professor in Electronics, Nanoscale Bioelectronics and Biophotonics at University of Nottingham. His current research focuses on the development of fibre optic sensors and instrumentation for biomedical application from discovery at the interface with physics and chemistry through to application addressing major healthcare challenges. He has published over 100 (h-index 21) peer-reviewed journal and conference papers, book contributions, holds 11 patents (4 licensed to UK and Japanese companies) and given invited presentations at international meetings in the field of optical fibre chemical sensors. He has managed as a PI and Co-I a total funding portfolio of £3.5 million in the area of biomedical point of care sensors. He was a Director of the EPSRC funded Network Cyclops (EP/N026985/1, Closed Loop Control Systems for Optimising Treatment, http://www.healthcaretechnologies.ac.uk/cyclops/); with the aim to facilitate the formation of a community of academics, clinicians and industrialists, across multiple disciplines (photonic sensing, advanced materials, treatment, and mathematical modelling), including international collaborators to address grand challenges in automation of treatment in healthcare. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/tv0JydXOdUI

opencc-by-4.0Nov 2021View details →

Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading)

This video is the fifth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021. Network Theme: Digital and data-driven blood monitoring and analytics for patient centred care pathways - Dr Weizi (Vicky) Li (Henley Business School, University of Reading). 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. Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 YouTube Link: https://youtu.be/egINC9hJifI

Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity

Raw data archive for the manuscript titled "Reversible transformations between the non-porous phases of a flexible coordination network enabled by transient porosity"

opencc-by-4.0May 2022View details →

Management of Chronic Subdural Hematoma: A Systematic Review & Component Network Meta-analysis of 455 Studies With 103,645 Cases

Contains data and analysis code for Management of Chronic Subdural Hematoma: A Systematic Review & Meta-analysis of 455 Studies With 103,645 Cases, accepted for publication in Neurosurgery by Wolters Kluwer (doi: 10.1227/neu.0000000000002144) For replication instructions, see the readme on the associated GitHub page: https://github.com/jckhry/csdh-cnma

Machine learning for Material Science 2022 - Neural Networks Assignment Dataset

Data for the neural network assignment

Fig. 5 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

Fig. 5. Visualization of a feature with high importance and a feature with low importance from a configuration B. The importance of these features for the identification accuracy was determined using permutation tests (see the methods). For each taxon or group (rows) several randomly selected specimens (columns) are shown. For the two selected Global Average Pooling layer features, the corresponding features of the preceding (Max Pooling) layer are visualized as those show specific image parts that had higher activations.Yellow represents the maximal activation strength; dark blue represents the minimal activation strength.

Fig. 2 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

Fig. 2. Schematic representation of the networks used. On top are the convolutional layers of VGG16, grouped into five blocks. Output of each Max Pooling layer is fed into Global Average Pooling layer. Numbers near each block name indicate number of features in the Global Average Pooling layer. Height of layers roughly corresponds to resolution (except for Global Average Pooling layer), while width roughly corresponds to the number of feature maps or features produced.Then, in approach A, outputs of five blocks are concatenated and passed to the linear classifier. In approach B, output of only one block (block 3 in the final configuration) is passed to the linear classifier. In approach C CNN outputs are as in approach A, but instead connected to a DNN with two layers of 320 fully connected (FC) neurons followed by a prediction layer (PL), with number of neurons equal to number of species classified. Finally, approach D features CNN as in approach B which is connected to DNN as in approach C.

Fig. 1 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

Fig. 1. Dorsal habitus photos of males and females of Tuxedo spp., Pygovepres vaccinicola, and Phallospinophylus setosus generated for and used in this study.

Fig. 4 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

Fig. 4. Validation accuracy and accuracy on test data for SVM linear classifier (A, B) and DNN approaches (C, D) for the three datasets.

Fig. 3 in Pretrained Convolutional Neural Networks Perform Well in a Challenging Test Case: Identification of Plant Bugs (Hemiptera: Miridae) Using a Small Number of Training Images

Fig. 3. Identification accuracy for the male (top) and female (bottom) Tuxedo dataset for the five selected resolutions and the individual blocks 1–5 and the concatenated block.

opencc-zeroOct 2022View details →

dryad32/100

Lpnet: Reconstructing phylogenetic networks from distances using integer linear programming

We present Lpnet, a variant of the widely used Neighbor-net method that approximates pairwise distances between taxa by a circular phylogenetic network. We first apply standard methods to construct a binary phylogenetic tree and then use integer linear programming to compute optimal circular orderings that agree with all tree splits. This approach achieves an improved approximation of the input distance for the clear majority of experiments that we have run for simulated and real data. We release an implementation in R that can handle up to 94 taxa and usually needs about one minute on a standard computer for 80 taxa. For larger taxa sets, we include a top-down heuristic which also tends to perform better than Neighbor-net.

opencc-by-4.0Oct 2022View details →

Dataset from: Selecting deep neural networks that yield consistent attribution-based interpretations for genomics

Deep neural networks (DNNs) have demonstrated great promise at taking DNA sequences as input and predicting a wide variety of functional activity. Post hoc attribution analysis has been employed to provide insights into the features learned by DNNs, often revealing patterns such as known motifs. However, attribution maps are noisy in practice to an extent that varies from model to model, even across DNNs that yield similar generalization performance. This makes it challenging to identify which high-performing DNN will provide trustworthy explanations. Here we propose a summary statistic that characterizes the consistency of learned features across a population of attribution maps which can be utilized as an additional criterion for model selection. We demonstrate the efficacy of this approach quantitatively using synthetic data and qualitatively with chromatin accessibility data. Together, this work advances our ability to select optimal DNNs that not only yield high generalization performance but also reliable attribution maps that will, in turn, accelerate scientific discovery in genomics.