Find research datasets worth reusing

Dataset accompaning "Variational inference for correlated gravitational wave detector network noise"

Dataset accompaning the paper "Variational inference for correlated gravitational wave detector network noise"   <h3>Raw data files</h3> <ul> <li>ET_caseA_noise.h5   (correlated noise)</li> <li>ET_caseB_noise.h5   (uncorrelated noise)</li> </ul> These contain: <ul> <li>raw_XYZ (the XYZ channels of ET noise)</li> <li>time (time in seconds, corresponding to raw_XYZ)</li> <li>periodogram: <ul> <li>pdgrm (of the above channels, truncated to 5-128 Hz)</li> <li>freq (in Hz)</li> </ul> </li> <li>true_psd <ul> <li>psd </li> <li>freq</li> </ul> </li> </ul> Note: case C from the manuscript utilised the case B dataset, (but with a model that does not account for the cross-spectrum). It does not have a separate dataset.    <h3>Result file</h3> <ul> <li>ET-CaseA-SGVB-PSD.h5</li> <li>ET-CaseB-SGVB-PSD.h5</li> <li>ET-CaseC-SGVB-PSD.h5</li> </ul> These contain: <ul> <li>psd_quantiles (the lower 0.05, median 0.50, upper 0.95 quantiles of 500 PSD samples)</li> <li>freq (in Hz, associated with the psd_quantiles)</li> </ul>

Deep learning based on convolutional neural networks to classify nanobiomechanical data

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →

FAN-GHETS24: A Flying Ad Hoc Network Dataset for Early Time Series Classification of Grey Hole Attacks

Flying ad-hoc networks (FANETs) consist of multiple unmanned aerial vehicles (UAVs) that rely on multi-hop routes for communication. These routes are particularly susceptible to grey hole attacks, necessitating swift and accurate defense to preserve the network's quality of service. This novel dataset, FAN-GHETS24, is designed for early time series classification of various grey hole attack scenarios. The dataset is derived from sequences of packet interactions between UAVs within the network, generated through multiple simulations. These sequences undergo post-processing via two methods: firstly, an anonymization procedure that replaces IP addresses with standard string variables, allowing for offline model training and universal deployment across UAVs; and secondly, the application of feature engineering techniques to format the data for machine learning model integration. <div> <div>The dataset is split across several zip files, combine and extract them by issuing these command:</div> </div> <div> <div>$ zip -FF fan-ghets24.zip --out fan-ghets24-combined.zip</div> <div>$ unzip fan-ghets24-combined.zip</div> </div>

Data from the article titled Mapping of networks and professional capital in the La Algarabía occupational center.

Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/ AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by the [grant number PRE2021-098075]

Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network - Sample files for creating Mapping TSV

Sample files for the last step on creating a Mapping TSV in the Galaxy Training Network tutorial on "Identifying Mycorrhizal Fungi from ITS2 sequencing using LotuS2"

Network formation as Narrative: Case Study Data

This data is the companion to a forthcoming paper entitled Network Formation As Narrative In Computer Role-playing Games. It consists of several excel sheets and gephi files with some graphs of social network data from a campaign of Mount & Blade: Warband, a campaign of The Exile Princes, and some hand-curated data on networks presented in the narrative of the early part of Baldur's Gate 3.

Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network

Tautomerization plays a critical role in many chemical and biological processes, impacting the molecular stability, reactivity, biological activity, and ADME-Tox properties. Many drug-like molecules exist in multiple tautomeric states in aqueous solutions and complicating drug discovery. Predicting these tautomeric ratios and identifying the predominant species rapidly and accurately is crucial for computational drug discovery. In this study, we introduce sPhysNet-Taut, a deep learning model fine-tuned with experimental data leveraging the Siamese network. This model predicts tautomer ratios in aqueous solution using MMFF94-optimized geometries directly. On an experimental test set, sPhysNet-Taut surpasses all other methods, achieving state-of-the-art performance with an RMSE of 1.9 kcal/mol on the 100-tautomers set and an RMSE of 1.0 kcal/mol on the SAMPL2 challenge, and providing the best ranking power for tautomer pairs. Additionally, our results demonstrate that fine-tuning on experimental data significantly improves model performance compared to training from scratch. This work not only provides a useful deep learning model for predicting tautomer ratios, but also provides a protocol for modeling pairwise data. To facilitate user-friendliness, we developed a readily accessible tool to predict stable tautomeric states in aqueous solutions, enumerating all possible tautomeric states and ranking them using the sPhysNet-Taut model.

opencc-by-4.0Jul 2024View details →

Dataset from: Parental mediation and the use of social networks: A systematic review

Translation of the previous file: aplicación_criterios_elegibilidad_sin_duplicados and modification of an error detected during the review process.

openmit-licenseSep 2024View details →

Dataset for the numerical simulation in the article "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"

This dataset includes the essential source code and the corresponding numerical data for the self-assembly of a M6L4 square-based pyramid (SP) complex.  The associated study is described in  "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network", by T. Abe, S. Takahashi, H. Sato, and S. Hiraoka.

Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking

Comparative analysis of SIRIUS to evaluate our method, Biotransformation-based Annotation Method (BAM). This dataset includes all scripts, data, and results relevant to this analysis. BAM can be found on GitHub (https://github.com/HassounLab/BAM).

Italian Covid-19 Retweet Network (2020-2022)

Description This repository contains directed retweet interactions between anonymized Twitter (now X) users. Data is stored in five csv files, each one relating to a different phase of the pandemic in Italy. <ul> <li>early covid (01/01/2020 – 08/03/2020): from Covid’s first tracing in Wuhan, China, up to the first lockdown in Italy;</li> <li>pre vaccine (09/03/2020 – 31/10/2020): from the first Italian lockdown to the start of the vaccination campaign;</li> <li>early vaccine (01/11/2020 – 16/04/2021): the first months of the vaccination campaign;</li> <li>vaccine drive (17/04/2021 - 31/07/2021): the main phase of intensive vaccination;</li> <li>late vaccine (01/08/2021 – 31/12/2021): phase in which a significant portion of the Italian population was fully vaccinated;</li> </ul> CSV files are structured as follows:  from,to,weight       Acknowledgements This work is supported by (i) the European Union – Horizon 2020 Program under the scheme “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, ”SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” (http://www.sobigdata.eu); (ii) SoBigData.it which receives funding from the European Union – NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: ”SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021; (iii) EU NextGenerationEU programme under the funding schemes PNRR-PE-AI FAIR (Future Artificial Intelligence Research).

opengpl-3.0-or-laterOct 2024View details →

Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture"

Supplemental Dataset Excel files and Source Data Excel file for "START domains generate paralog-specific regulons from a single network architecture" in Nat Comms

The efficacy of hemoglobin spray in wound management: a systematic review and network meta-analysis of comparative studies

Dataset

opencc-by-4.0Dec 2023View details →

Topology Bench: Systematic Graph Based Benchmarking for Optical Networks

TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical analysis to provide a systematic methodology for selecting diverse sets of optical networks for benchmarking. This topology benchmark is comprised of a network dataset and a systematic graph theoretic analysis. The dataset provides (a) 105 real optical networks and (b) synthetic topologies, generated by the SNR-BA model, divided into (i) Syn-small of 900 synthetic networks and (ii) Syn-large of 270,000 synthetic networks. The systematic graph theoretical analysis identifies and analyses structural, spatial and spectral properties of both the real world and synthetic networks. The graph theoretical correlation analysis reveal network design strategies leading to sparse yet efficient networks. An outlier analysis identifies networks that deviate from standard network designs. The analysis also identifies the limitations of real data in terms of network diversity and provides a justification for using synthetic data to complement the real dataset. We conclude the paper by providing a systematic methodology to cluster networks based on unsupervised machine learning and to select a diverse set of topologies for benchmarking. TopologyBench is a novel, high-quality and unified benchmark designed to facilitate research collaborations in long-haul fibre infrastructure by providing a systematic graph theoretical approach to benchmarking optical networks.   If you use any of the data provided, please cite our paper:   <pre>@article{matzner2024topology, title={Topology Bench: systematic graph-based benchmarking for core optical networks}, author={Matzner, Robin and Ahuja, Akanksha and Sadeghi, Rasoul and Doherty, Michael and Beghelli, Alejandra and Savory, Seb J and Bayvel, Polina}, journal={Journal of Optical Communications and Networking}, volume={17}, number={1}, pages={7--27}, year={2024}, publisher={IEEE} } </pre>

CTU Hornet 65 Niner: A Network Dataset of Geographically Distributed Low-Interaction Honeypots

CTU Hornet 65 Niner is a dataset of 65 days of network traffic attacks captured in cloud servers used as honeypots to help understand how geography may impact the inflow of network attacks. The honeypots were placed in nine different geographical locations: Amsterdam, London, Frankfurt, San Francisco, New York, Singapore, Toronto, Bangalore, and Sydney. The data was captured from April 28th to July 1st, 2024. The nine cloud servers were created and configured following identical instructions using Ansible [1] in DigitalOcean [2] cloud provider. The network capture was performed using the Zeek [3] network monitoring tool, which was installed on each cloud server. The cloud servers had only one service running (SSH on a non-standard port) and were fully dedicated to being used as a honeypot. No honeypot software was used in this dataset. The dataset is composed of nine scenarios: <ul> <li>Honeypot-Cloud-DigitalOcean-Geo-1: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-2: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-3: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-4: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-5: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-6: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-7: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-8: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> <li>Honeypot-Cloud-DigitalOcean-Geo-9: has 65 folders (YYYY-MM-DD), each containing 24 Zeek conn.log files and other Zeek files</li> </ul> References: [1] Ansible IT Automation Engine, https://www.ansible.com/. Accessed on 08/28/2024. [2] DigitalOcean, https://www.digitalocean.com/. Accessed on 08/28/2024. [3] Zeek Documentation, https://docs.zeek.org/en/master/index.html. Accessed on 08/28/2024. Funding: The authors acknowledge support by the Strategic Support for the Development of Security Research in the Czech Republic 2019--2025 (IMPAKT 1) program, by the Ministry of the Interior of the Czech Republic under No. VJ02010020 -- AI-Dojo: Multi-agent testbed for the research and testing of AI-driven cyber security technologies.

Adjacency matrices for Carril et al. 'Evolution of avian foot morphology through anatomical network analysis'

The adjacency matrices used in Carril et al. Evolution of avian foot morphology through anatomical network analysis (doi: https://doi.org/10.1038/s41467-024-54297-9), saved both in an Excel file and as individual .csv files for each matrix. The R script for the Anatomical Network Analysis used in the paper uses the .csv files as raw data, as these are easier to read and process in R. Abbreviations can be found in Supplementary Data 12 of the original paper.

Characterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing

<h2>Dataset</h2> Space ranger output (Visium platform) of two human slice culture samples (S1 & S2) injected with GBstarter cells (Tetzlaff et al., 2024).

ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation

ReaxFF simulations, QM calculation input/output files, and Jupyter notebooks for data analysis and visualization.

BOCK: Biological networks and Oligogenic Combinations as a Knowledge graph 2.0

BOCK is a knowledge graph integrating oligogenic disease information (originally from the Oligogenic Disease Database (Natchtegael et al. 2022)) together with multiple biological networks and ontologies. Compared to more generic knowledge graphs, we selected specifically networks relevant to understand the molecular mechanisms of epistasis, placing genes as the central entities, and focused on trusted resources describing a large set of human genes and their interactions. All entities in the KG are linked to their source database entry via an URI (Uniform Resource Identifier) to facilitate integrations within larger bioinformatics linked data repositories. BOCK 2.0 integrates recent versions of the used ontologies and databases, as well as additional pathway-specific (The Reactome Pathway Knowledgebase 2024, Milacic et al.) and tissue-specific information (COXPRESdb v8, Obayashi et al.). Additionally the database used for the coexpression relation between genes, has been replaced by COXPRESdb v8. We provide BOCK 2.0 in three formats: <ol> <li>GraphML (Graph Markup Language): a network format enabling the fast import of the KG by multiple libraries (e.g networkx) and tools (e.g Cytoscape).</li> <li>XML (Extensible Markup Language): a text-encoding system that is human-readable and compatible with many systems.</li> <li>Neo4J import files: tab-separated files that can be easily imported into Neo4J using the neo4j-admin utils.</li> </ol>

Plant-Bee Pollen Interaction Networks Based on Epanthidium tigrinum Nests in Fortaleza, Brazil (2019-2020)

This dataset contains detailed ecological data on the interactions between the solitary bee species Epanthidium tigrinum and various flowering plants in an urban area of Fortaleza, Ceará, Brazil. The data were collected from May 2019 to November 2020, focusing on pollen analysis from nests of E. tigrinum using black cardboard trap nests. Data Includes: <ul> <li>LOCALITY: Geographic location of the study.</li> <li>BEE_SPECIE: The species of bee studied, exclusively Epanthidium tigrinum.</li> <li>FLOWER_SPECIES: Identified plant species based on pollen grains found in the nests.</li> <li>INTERACTION_FREQUENCY: The frequency of interactions between E. tigrinum and flowering plants, quantified through pollen presence across analyzed slides.</li> <li>NESTING_PERIOD: Classification of the nesting periods into High Nesting Period (June to September) and Low Nesting Period (remaining months).</li> <li>YEAR: The years of data collection (2019 and 2020).</li> </ul> Potential Uses: This dataset is valuable for researchers studying plant-pollinator interactions, urban ecology, and the role of solitary bees in ecosystem services. It can be utilized in ecological modeling, conservation planning, and understanding the dynamics of pollinator communities in urban settings. Additionally, it may aid in the assessment of the impact of urbanization on pollinator behavior and plant diversity.