Find research datasets worth reusing

BDRC Returned Students Network Data

These two files correspond to the edge list and the node list used to build the network of returned students in the Biographical Dictionary of Republican China (BDRC).

BDRC Interpersonal Network Data

This set of files contains the edge list, the node list and list of node attributes used to build the network of interpersonal relations in the Biographical Dictionary of Republican China (BDRC). The network contains two categories of nodes : bionodes (objects of a biography) and nodes (individuals mentioned in a biography).

BDRC Institutional Affiliation Network Data

These two files correspond to the edge list and the node list used to build the network of institutional affiliations (individuals' positions in institutions) in the Biographical Dictionary of Republican China (BDRC).

HTTPS Brute-force dataset with extended network flows

We are publishing a dataset we created for designing a brute-force detector of attacks in HTTPS. The dataset consists of extended network flows that we captured with flow exporter <a href="https://github.com/CESNET/ipfixprobe">Ipifixprobe</a>. Apart from traditional fields like source and destination IP addresses and ports, each flow contains information (size, direction, inter-packet time, TCP flags) about up to the first 100 packets. The sizes of packets are taken from the transport layer (TCP, UPD); packets with zero payload (e.g., TCP ACKs) are ignored. We publish three files: <ul> <li>flows.csv, which contains raw flow data.</li> <li>aggregated_flows.csv, which contains aggregated flows</li> <li>samples.csv, which contains samples with extracted features. This data can be used for training a machine-learning classification model.</li> </ul>   All IP addresses, source ports, TLS SNIs are sha256-hashed. Column CLASS is 0 for benign samples and 1 for brute-force samples. Brute-force data The brute-force data were generated with three popular attack tools - Ncrack, Thc-hydra, and Patator. Attacks were performed against these applications: <ul> <li>    WordPress</li> <li>    Joomla </li> <li>    MediaWiki</li> <li>    Ghost</li> <li>    Grafana</li> <li>    Discourse</li> <li>    PhpBB</li> <li>    OpenCart</li> <li>    Redmine</li> <li>    Nginx</li> <li>    Apache</li> </ul> The SCENARIO columns indicate which tool and application were used to generate the sample. Benign data Bening data consists of eight captures from a backbone network. The SCENARIO column indicates individual captures.

opencc-by-4.0Dec 2019View details →

Data of "A quantum-logic gate between distant quantum-network modules"

Data published in "A Quantum-Logic Gate between Distant Quantum-Network Modules" Science

Series arcing faults voltage and current signatures in a DC low power network.

The dataset  contains series arc faults voltage and current signatures in a DC low power network. The first apparatus performs a series arc fault by opening contacts (ReadMe_test 1). After breaking, copper electrodes are maintained at a constant gap. The measurements are performed with direct current and resisve/Inductive loads. The second apparatus performs contact breaking under chosen voltage and current supply condions (ReadMe_test2). After breaking, electrodes are maintained at a constant gap during electrical measurement. The measurements are performed with direct current and resisve loads by separating electrodes.

Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon

This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present.

Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains

We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.

LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks

LSTM-REG: regional gridded runoff dataset for northwest Russia. Model: LSTM. Geographical domain: 25–57°E; 55–70°N. Spatial resolution: 0.5°x0.5°. Temporal resolution: daily. Period: 1980-2016.

Room Temperature Self-Healing in Soft Pneumatic Robotics: Autonomous Self-Healing in a Diels-Alder Polymer Network

Healable soft robotic systems have been developed by constructing flexible membranes out of Diels?Alder (DA) polymer networks. In these components, relatively large amounts of damage, on the centimeter scale, can be healed, provided that the temperature is increased to 80?90 ?C. This article presents a new DA polymer network that can heal at room temperature through a smart design of the network that increases the molecular mobility in the material. This new material is used to develop the first healable soft robotic prototype that can autonomously recover from severe, realistic damage. The soft pneumatic hand can recover from various types of injuries, including being cut completely in half, without the need for a temperature increase. After healing, the performance of the soft robotic prototype is recovered.

Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner. Conclusions: The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

openother-openDec 2020View details →

Influence-Disinformation-Networks/PNAS-Narrative-Networks: PNAS Narrative Networks Release

This repository contains additional data used for the paper Automatic detection of influential actors in disinformation networks, Proc. Natl. Acad. Sci. U.S.A., to appear, doi:<a href="https://doi.org/10.1073/pnas.2011216118">10.1073/pnas.2011216118</a>.

opencc-by-4.0Jan 2021View details →

Supplementary material: Prophylactic antibiotics for adults with chronic obstructive pulmonary disease: a network meta-analysis

Supplementary material for Cochrane review: Janjua S , Mathioudakis AG , Fortescue R , Walker RAE , Sharif S , Threapleton CJD , Dias S . Prophylactic antibiotics for adults with chronic obstructive pulmonary disease: a network meta-analysis. Cochrane Database of Systematic Reviews 2021, Issue 1. Art. No.: <a href="https://archie.cochrane.org/sections/documents/CD013198">CD013198</a>. DOI: <a href="https://archie.cochrane.org/sections/documents/10.1002/14651858.CD013198.pub2">10.1002/14651858.CD013198.pub2</a>.

Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive of novel ageing genes - Database of Physical gene-gene Interactions in young adult C.elegans.

This repository contains Supplementary Information for manuscript Suriyalaksh et al Gene Regulatory Network inference in long lived C.elegans reveals modular properties that are predictive  of novel ageing genes corresponding to the curation of physical gene-gene interactions for young adult C elegans worms  We manually curated 239,001 regulatory interactions from 289 young adult wild-type (WT) C.elegans datasets, consisting of 126 genes and 495 unique transcription factors (see TableS1_datasets_for_prior.csv for references).  This repository contains 3 different files: TableS1_datasets_for_prior.csv - contains datasets used as sources for physical gene-gene or TF-gene interactions TableS2_physical_priors.xlsx - contains three tabs: ChIPATAC - contains physical TF-gene interactions from 115 L4 or young-adult ChIP-seq datasets from modERN (Kudron et al., 2018) +  ChIP-seq datasets (GSE28350, GSE81521) from  (Hochbaum et. al, 2011, Li et. al, 2016). eY1HATAC- contains  3,501 TF-gene interactions from eY1H assay by Fuxman Bass et al. (2016). motifATAC - contains 202 unique TF DNA recognition motifs using “direct evidence” option from CiS-BP motif database (Weirauch et al., 2014), obtained through RTFBSDB R package (Wang et al., 2016) - see TableS1 TableS3_WT_functional_priors.csv - contains functional knockdown data that we use as gold standard to validate inferred networks in Suriyalaksh et al. (see TableS1_datasets_for_prior.csv for sources) --- Description of methodology to obtain regulatory interactions in TableS2: Regulatory sequences for each gene were acquired from ENSEMBL (Aken et al., 2017), obtained using biomaRt R package (accessed on 31st Oct 2017). This study used WBcel235/ce11 version of the C. elegans genome, and WormBase WS260 genome annotations. For motifs, TFs whose motifs overlapped with an open ATAC-seq region by at least one base pair were kept. For ChIP-seq, TF binding sites that overlapped with an open ATAC-seq region by at least one base pair were kept using bedtools intersect and bedtools merge commands. An interaction from a TF to a gene was inferred by aligning transcription start sites (TSS) using bedtools window commands with 1000 bp window size to the TF-binding locations from ChIP-seq and motifs. For eY1H data, an interaction is included if the TSS site of the target gene overlaps with an open ATAC-seq region by at least one base pair. For gene-gene interactions, of the 298 studies compiled in WormExp v1.0 database (Yang et al, 2016, updated 27/07/16), 98 studies were included in the database spanning 126 different genes (see Table S1 in this repository).

opencc-by-4.0Feb 2021View details →

Indistinguishable photons from a trapped-ion quantum network node

Dataset of measurements for "Indistinguishable photons from a trapped-ion quantum network node"

opencc-by-4.0Jan 2021View details →

Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences - supplemental material & data

Table and data repository for the manuscript "Doctoral Studies as part of an Innovative Training Network (ITN): Early Stage Researcher (ESR) experiences" Supplemental Tables: <ul> <li>table1_ESIT Project Table</li> <li>table2_TIN-ACT Project Table</li> <li>table3_ITN_tinnitus</li> <li>table4_ITN_other</li> <li>table5_Individual PhDs</li> </ul> Individual-level and de-identified survey data (raw data): <ul> <li>raw_data_ITN_tinnitus (survey results from PhDs as part of an ITN with a focus on tinnitus)</li> <li>raw_data_ITN_other (survey results from PhDs associated to ITNs with another focus)</li> <li>raw_data_Individual_Phds (survey results from PhDs not part of an ITN)</li> </ul>

dryad40/100

Data from: Hierarchical social networks shape gut microbial composition in wild Verreaux's sifaka

In wild primates, social behaviour influences exposure to environmentally acquired and directly transmitted microorganisms. Prior studies indicate that gut microbiota reflect pairwise social interactions among chimpanzee and baboon hosts. Here, we demonstrate that higher-order social network structure—beyond just pairwise interactions—drives gut bacterial composition in wild lemurs, which live in smaller and more cohesive groups than previously studied anthropoid species. Using 16S rRNA gene sequencing and social network analysis of grooming contacts, we estimate the relative impacts of hierarchical (i.e. multilevel) social structure, individual demographic traits, diet, scent-marking, and habitat overlap on bacteria acquisition in a wild population of Verreaux's sifaka (Propithecus verreauxi) consisting of seven social groups. We show that social group membership is clearly reflected in the microbiomes of individual sifaka, and that social groups with denser grooming networks have more homogeneous gut microbial compositions. Within social groups, adults, more gregarious individuals, and individuals that scent-mark frequently harbour the greatest microbial diversity. Thus, the community structure of wild lemurs governs symbiotic relationships by constraining transmission between hosts and partitioning environmental exposure to microorganisms. This social cultivation of mutualistic gut flora may be an evolutionary benefit of tight-knit group living.

opencc-zeroDec 2016View details →

dryad40/100

Data from: Discordant patterns of genetic and phenotypic differentiation in five grasshopper species co-distributed across a microreserve network

Conservation plans can be greatly improved when information on the evolutionary and demographic consequences of habitat fragmentation is available for several co-distributed species. Here, we study spatial patterns of phenotypic and genetic variation among five grasshopper species that are co-distributed across a network of microreserves but show remarkable differences in dispersal-related morphology (body size and wing length), degree of habitat specialization and extent of fragmentation of their respective habitats in the study region. In particular, we tested the hypothesis that species with preferences for highly fragmented microhabitats show stronger genetic and phenotypic structure than co-distributed generalist taxa inhabiting a continuous matrix of suitable habitat. We also hypothesized a higher resemblance of spatial patterns of genetic and phenotypic variability among species that have experienced a higher degree of habitat fragmentation due to their more similar responses to the parallel large-scale destruction of their natural habitats. In partial agreement with our first hypothesis, we found that genetic structure, but not phenotypic differentiation, was higher in species linked to highly fragmented habitats. We did not find support for congruent patterns of phenotypic and genetic variability among any studied species, indicating that they show idiosyncratic evolutionary trajectories and distinctive demographic responses to habitat fragmentation across a common landscape. This suggests that conservation practices in networks of protected areas require detailed ecological and evolutionary information on target species in order to focus management efforts on those taxa that are more sensitive to the effects of habitat fragmentation.

opencc-zeroDec 2014View details →

opencc-by-4.0Jun 2021View details →

A Hierarchical Network-Oriented Analysis of UserParticipation in Misinformation Spread on WhatsApp

#Authors: Gabriel Peres Nobre, Carlos Henrique Gomes Ferreira, Jussara Marques de Almeida #2021 Script to read a Database file of messages and, in the end, extract user communities based on content co-sharing. We provide a database file with the anonymized messages shared in WhatsApp.