Find research datasets worth reusing

opencc-by-sa-4.0Sep 2021View details →

Cortical oscillations support sampling-based computations in spiking neural networks

This archive contains the scripts for generating the data and figures and the data of the publication: "Cortical oscillations support sampling-based computations in spiking neural networks". The different parts of the material are grouped into separate archives to enable modular usage and can be individually downloaded as required: <ul> <li>The archive spike-based-tempering_scripts.tgz contains all the scripts to reproduce the simulation data and figures.</li> <li>The file software.img contains the third-party software needed to execute the simulations of the current-based experiments.</li> <li>The archive current-based_experiments_data includes the scripts and the simulation data for the current-based experiments.</li> </ul>

Dataset: Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model

Dataset Tensor-network study of correlation-spreading dynamics in the two-dimensional Bose-Hubbard model Ryui Kaneko, Ippei Danshita

Dataset for Anomaly Detection in a Production Wireless Mesh Community Network

CSV dataset generated gathering data from a production wireless mesh community network. Data is gathered every 5 minutes during the interval 2021-04-13 00:00:00 to 2021-04-16 00:00:00. During the interval 2021-04-14 02:00:00 2021-04-14 17:50:00 (both included) there is the failure of a gateway in the mesh (nodeid 24).  Live mesh network monitoring link: <a href="http://dsg.ac.upc.edu/qmpsu">http://dsg.ac.upc.edu/qmpsu</a> The dataset consists of single gzip compressed CSV file. The first line of the file is a header describing the features. The first column is a GMT timestamp of the sample in the format as "2021-03-16 00:00:00".  The rest of the columns provide the comma-separated values of the features collected from each node in the corresponding capture. A suffix with the nodeid is added to each feature. For instance, the feature having the number of processes of node with nodeid 24 is named as "processes-24". In total, 63 different nodes showed up during the samples, each being assigned a different nodeid. Features are of two types: (i) absolute values, for instance, the CPU 1-minute load average, and (ii) counters that are monotonically increased, for instance the number of transmitted packets. We have converted counter-type kernel variables to rates, by dividing the difference between two consecutive samples, over the difference of the corresponding timestamps in seconds, as shown in the following pseudo-code:   feature.rate are columns computed from feature as     feature.rate <- (feature[2:n]-feature[1:(n-1)])/(epoch[2:n]-epoch[1:(n-1)])     feature.rate <- feature.rate[feature.rate >= 0] # discard samples where the counter is restarted    where n is the number of samples features - processes     number of processes - loadavg.m1     1 minute load average - softirq.rate     servicing softirqs - iowait.rate     waiting for I/O to complete - intr.rate       - system.rate     processes executing in kernel mode - idle.rate     twiddling thumbs - user.rate     normal processes executing in user mode - irq.rate     servicing interrupts - ctxt.rate     total number of context switches across all CPUs - nice.rate     niced processes executing in user mode - nr_slab_unreclaimable     The part of the Slab that can't be reclaimed under memory pressure - nr_anon_pages     anonymous memory pages - swap_cache     Memory that once was swapped out, is swapped back in but still also is in the swapfile - page_tables     Memory used to map between virtual and physical memory addresses - swap       - eth.txe.rate     tx errors over all ethernet interfaces - eth.rxe.rate     rx errors over all ethernet interfaces - eth.txb.rate     tx bytes over all ethernet interfaces - eth.rxb.rate     rx bytes over all ethernet interfaces - eth.txp.rate     tx packets over all ethernet interfaces - eth.rxp.rate     rx packets over all ethernet interfaces - wifi.txe.rate     tx errors over all wireless interfaces - wifi.rxe.rate     rx errors over all wireless interfaces - wifi.txb.rate     tx bytes over all wireless interfaces - wifi.rxb.rate     rx bytes over all wireless interfaces - wifi.txp.rate     tx packets over all wireless interfaces - wifi.rxp.rate     rx packets over all wireless interfaces - txb.rate        tx bytes over all ethernet and wifi interfaces - txp.rate        tx packets over all ethernet and wifi interfaces - rxb.rate        rx bytes over all ethernet and wifi interfaces - rxp.rate        rx packets over all ethernet and wifi interfaces - sum.xb.rate    tx+rx bytes over all ethernet and wifi interfaces - sum.xp.rate    tx+rx packets over all ethernet and wifi interfaces - diff.xb.rate      tx-rx bytes over all ethernet and wifi interfaces - diff.xp.rate      tx-rx packets over all ethernet and wifi interfaces

Global Meteor Network observations of Starlink re-entry 2022-02-10

This dataset contains video observations by some stations of the Global Meteor Network of the re-entry of the satellite StarLink-1668 above Spain on 2022-02-10 around 19:50 UTC. There are several types of files: <ul> <li>FF files: these are 10.24 second videos compressed in the four-frame format. They are just FITS files with four frames, containing per pixel 1) the maximum value over 256 frames 2) the frame nr (between 0 and 255) where the maximum occurred 3) the mean value of all 256 frames and 4) the RMS of the 256 values.</li> <li>FR files: compressed video recordings of detected fireballs. These can be read with the RMS software.</li> <li>MP4 files: rendered movies of combined FF and FR files for selected stations.</li> <li>Platepar-files: these contain astrometry corresponding to the FITS files. These can be interpreted by the RMS software.</li> <li>ECSV files: these contain manually picked points (with SkyFit2.py from RMS) along the track of the reentry. For each point, time and apparent coordinates are recorded. These files can be interpreted by the WesternMeteorPyLib trajectory solver.</li> <li>trajectory-points.txt: solutions from the trajectory solver.</li> <li>reentry-map-v2.png: a rendered map of the trajectory (made in QGIS).</li> </ul> The files can be processed with the software in https://github.com/CroatianMeteorNetwork/RMS and https://github.com/wmpg/WesternMeteorPyLib.

opencc-by-sa-3.0Jan 2022View details →

A Social Network of the Prosopography of the Neo-Assyrian Empire

The data used for creating "A Social Network of the Prosopography of the Neo-Assyrian Empire" The networks were created from the text and pdf file versions of the Prosopography of the Neo-Assyrian Empire (PNA). There are two different networks created from the same data. In the two-mode network, persons are connected to the texts in which they are attested. The one-mode network connects two persons if they are attested in the same document.  In order to study and visualise the networks, it is sufficient to download the folder Networks.zip. SupplementaryData.zip contain more in-depth explanations of the different stages of creating the networks, the normalization lists used for that, and the output of different stages of the procedure. The dataset has also been described in Jauhiainen, H., & Alstola, T. (2022). A Social Network of the Prosopography of the Neo-Assyrian Empire. Journal of Open Humanities Data, 8, 8. DOI: <a href="http://doi.org/10.5334/johd.74">http://doi.org/10.5334/johd.74</a>. For the reasons of copyright, we are not able to publish the data from which the networks have been extracted. But we hope that by publishing all the source codes, concordance lists, and outputs with explanations, we give the user a picture of how the networks were created.

BioTAGME: A comprehensive platform for biological knowledge network analysis

A Knowledge Graph containing logical or physical relationships among biological elements.  This Network was built through BioTAGME, a system that combines TAGME, an entity-annotation framework based on Wikipedia corpus with a network-based inference methodology (i.e., DT-Hybrid). BioTAGME exploits several Biological Ontologies as "ground truth" of significant bio-entities, such as DisGeNET, DrugBank, STRING and many more.     We Deployed BioTAGME on PubMed, where the aim was on extracting biological entities and their relations from titles and abstracts. Biological entities are the nodes of our graph, while edges are the relations between them. Edges are of three categories: <ol> <li>Literature edges: interactions derived from publications.</li> <li> STRING: protein-protein associations stored in the STRING database.</li> <li>BioTAGME: interactions predicted by our tool.</li> </ol>   The network is released (BiotagmeNetwork.zip) in a neo4j compatible format. Such archive contains: <ol> <li>Edges.csv and Nodes.csv that contain the nodes and edges of our network, rispectively.</li> <li>Name_Aliases.csv: contains the synonyms list of each biological entity.</li> <li>Wiki_Titles.csv: contains the wikipedia pages title associated with the annotated entities.</li> <li>wid1_wid2_pmid.csv: contains the associations between pairs of entities and pubmed id</li> <li>BioIDs_WikiIDs.csv: contains the association between the biological entities annotated by BioTAGME, and the wikipedia pages that are associated. </li> </ol>

Automatic taxonomic identification based on the Fossil Image Dataset (>415,000 images) and deep convolutional neural networks

This is a Fossil Image Dataset, which contains >415000 images. A total of 50 clades were labeled, with a final 90% accuracy. We used the web crawler to download fossil images from the Internet. We declare that all the collected images are used for academic purposes only. If anyone wants to use this dataset, please agree on the Terms of access for the Fossil Image Dataset (FID). We uploaded two datasets: FID (contains 0.415 million images) and reduced-FID (60 thousand images, 1200 for each clade). Requirements of necessary preinstalled Python libraries, algorithms for analysis, and the model weights are available at <a href="https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset">https://github.com/XiaokangLiuCUG/Fossil_Image_Dataset</a>.

Dataset for "Thresholds in road network functioning on US Atlantic and Gulf barrier islands"

This dataset accompanies the paper "Thresholds in road network functioning on US Atlantic and Gulf barrier islands" (<a href="https://doi.org/10.31223/X55D1G">https://doi.org/10.31223/X55D1G</a>) and is intended to be used to reproduce the analysis.  In this dataset you can find the graphml files generated for each island (103 islands with drivable roads). Each intersection of the island road network is a node, and has an associated elevation and extreme water level value. Also included are the statistics for each network, and a single table with the analysis results for each of the networks with >100 nodes.

opencc-zeroMar 2022View details →

dryad40/100

Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles

Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions. We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions. We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments. We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources. Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.

Data and scripts for: Green turtles highlight connectivity across a regional marine protected area network in West Africa

Data derivates and analysis scripts (in R) used for the paper on analyzing green turtle MPA coverage and connectivity in West Africa.

Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in Sorghum bicolor

Supplementery materials of journal article "Integrating differential expression and weighted correlation network analysis for identifying genes controlling shoot development in Sorghum bicolor"

Data supporting the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence"

This data supports the manuscript "Sexually divergent development of depression-related brain networks during healthy human adolescence" by Dorfschmidt et al. Part of these <a href="https://doi.org/10.6084/m9.figshare.11551602">data</a> were initially released by Vá&scaron;a et al. (2020) as part of their <a href="https://doi.org/10.1073/pnas.1906144117">manuscript</a>. Please cite them when using these data.

TReNCo: Topologically associating domain (TAD) aware regulatory network construction (extended data)

The enclosed files contain all of the extended data from: TReNCo: Topologically associating domain (TAD) aware regulatory network construction

Matching Network of Ontologies: a Pattern Recognition Approach

Networks of Ontologies research deals with the need to combine several ontologies at the same time. In a world of integrated systems (system of systems), isolated systems are increasingly rare in the near future, and their integration creates opportunities to change, validate information and add more value to an information system. This system of systems can contain ontologies to support the corresponding knowledge model. Consequently, new integration requirements may have to deal with network alignment rather than single ontologies. This work delves into the area of network alignment and proposes new ways to approach a particular case of alignment of large ontologies. The contribution of the work is the use of algebraic operations on networks to eliminate candidates before alignment and to use a stochastic search method to discover the relevant nodes. These nodes should be retained as they increase accuracy and final alignment retrieval even though they are identical and removed by the algebraic operation. To find out the particular relevance of each node, we propose a random walk combined with a frequent itemsets approach that overcomes the force brute approaches in processing time, as the size of networks grows, and have close precision. The approach was validated using networks of ontologies created from the OAEI ontologies. The approach selected the entities to send to the matcher without losing significant preexisting alignments. Finally, two different matchers were used to get metrics and compare the results with the pairwise force brute approach.

KGlove+Glove embedding for MAKG citation network

Entity embedding by using KGlove+Glove on MAKG citation network (citation network from <a href="https://zenodo.org/record/4617285/files/08.PaperReferences.nt.bz2?download=1">https://zenodo.org/record/4617285/files/08.PaperReferences.nt.bz2?download=1</a>)

Extreme hydrometeorological events, a challenge for gravimetric and seismology networks

“Data supporting the paper published by Earth's Future: "Extreme hydrometeorological events, a challenge for gravimetry and seismological networks" by Van Camp, de Viron, Dassargues, Delobbe, Chanard, and Gobron, doi: 10.1029/2022EF002737, 2022. The data contains: 1) A NETCDF mvc_data_EF.nc file, and the Python mvc_data_EF.py code for reading it. The nc file contains the time series from and around the Membach station, Belgium, as recorded during the July 2021 flood of the Vesdre River. Time in second since 2021-01-01 00:00: -Gravity [nm/s²]: from the superconducting gravimeter (after correcting for tidal and atmospheric pressure effects), To convert it into mm water: use factor -1/0.39 [mm/nm/s²] -Seismology [counts]: from the broadband Guralp CMG-3ESP seismometer. Calibration factor: 838.86 [count/µm/s] -Rain [mm water]: data inferred from the weather radar -Flow [m³/s]: water entering the Eupen reservoir 2) An ASCII file SG_C021.TSF contains the complete recording of the superconducting gravimeter GWR#C021, since 1995-10-01. The data are corrected for polar motion, tidal, and atmospheric pressure effects (local admittance of -3.3 nm/s²/hPa), and instrumental drift. This drift is determined by using 323 absolute gravity measurements performed at the Membach station by the FG5#202 gravimeter since January 1996. 3) PDF Document "Supplement 1 Other seismic spectra during high flow events" providing moving window spectra of seismic recordings during other significant floods in the Vesdre Valley 4) PDF document "Supplement 2_GRACE_GLDAS_GNSS" providing the observations from GNSS and GRACE, and the predictions of the GLDAS hydrological model during the July 2021 event.

opencc-zeroApr 2022View details →

dryad40/100

Opinion dynamics in social network under competition: the role of influencing factors in consensus reaching

The profitability of opinion and the finiteness of individual attention have already spawned the extensive competition for individual preferences on social networks. It's quite necessary to investigate the opinion dynamics over social networks in a competitive environment. To this point, this paper develops a novel social network DeGroot model based on competition game (DGCG) to characterize the opinion evolution in a competitive opinion dynamics. Based on the DGCG model, we obtain equilibrium results in the stable state of opinion evolution. Consecutively, we analyze what role relevant factors play in the final consensus and competitive outcomes, including the resource ratio of both contestants, initial opinions and network structure. Theoretical analyses and simulation experiments show that these factors can significantly sway the consensus and even reverse competition outcomes.

opencc-by-4.0Apr 2022View details →

DeepLabCut network trained to track mouse 'front' body parts during rotarod running (front-view)

DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. Rotarod, front camera: 29 frames from 18 videos (10 fps) were extracted for a total of 520 labeled pictures. 12 body parts (left, right and mid snout, left/right top/bottom ears, left/right eyes, headmount, left/right foot), 4 corners of the rotarod and 4 points on the rotarod wheels were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 225K iterations (train error: 1.58, test error: 1.63). 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 20 new videos with different recording conditions were labeled. The network was then refined for 331K iterations (from scratch) (train error: 2.34, test error: 5.49). This process was repeated a second time when adding 20 new videos (400 frames) and the network trained to a final 402K (train error: 2.64, test error: 3.98). For this last batch, brightness/contrast were too low to detect body features; brightness/contrast were thus enhanced using custom-written Python scripts. Relevant videos were analyzed at each of the 3 steps, for a total of 152 videos. Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023). Network not included in the final publication.

opencc-by-4.0Apr 2022View details →

DeepLabCut network trained to track mouse body parts during open field locomotion (top-down view)

DeepLabCut (https://github.com/DeepLabCut/) (Mathis et al., 2018; Nath et al., 2019) was used for tracking body parts of mice in an open field arena or in the rotarod. DeepLabCut 2.1.8.2 (local version on Windows with CPU, using the GUI) and 2.1.10.2 (google colab to train the network) were used using default parameters and the pretrained resnet50 network with imgaug augmentation. Frames were extracted with the k-means method and outlier frames with the jump method. Open field: 20 images from 19 videos (10 or 30 fps) were extracted for a total of 380 labeled pictures. 8 body parts (snout, both ears, body center, both side laterals, tail base and tail end) and the 4 corners of the field arena were manually labeled and linked to each other using skeletons. A neural network was trained using these images for 170K iterations. 20 outlier frames were extracted from each video and relabeled. An additional 20 images from 19 videos with different recording conditions were labeled. The network was then refined for 210K iterations (from scratch), yielding a train error of 3.33 pixels and a test error of 8.83 pixels (with a likelihood p-cutoff of 0.6). This process was repeated a second time (using an additional 20 images from 15 new videos) to improve the pixel error; to a final 400 K iterations (train error: 2.65, test error: 3.71). 67 videos from 5 different experiments were analyzed on the final network. Used to analyze videos for a publication (Labouesse et al., Nature Communications 2023)