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48 results for “Network Centrality”
Fine-scale structure of the 2016-2017 Central Italy Seismic Sequence from data recorded at the Italian National Network
<p><strong>Data Set </strong></p> <p>Catalog of 33,983 earthquakes located during the 2016-2017 Central Italy seismic sequence. The velocity model used is the 1D gradient P- and S-wave velocity models (after Carannante et al., 2013). We used the highest quality P- and S-wave arrival times manually picked by analysts of the National Institute of Geophysics and Volcanology (INGV) seismic monitoring room, having an uncertainty lower than 0.6 s. </p> <p>Events were located by means of a 2-step procedure: the INGV routine absolute locations computation for all events with ML ≥ 1.5 that occurred in the study area between August 2016 and January 2018, using the method described in Chiaraluce et al. (2017); the determination of relative locations by applying the HypoDD code (Waldhauser, 2001) to the catalog picks and phase delay times measured from waveform cross correlation.</p> <p>The time domain cross-correlation method (Schaff et al., 2004; Schaff and Waldhauser, 2005) was applied to seismograms of all pairs of events separated by 3 km or less and recorded at common stations. Seismograms were filtered in the 1-15 Hz frequency range using a 4 pole, zero phase band‐pass Butterworth filter. The correlations measurements were performed on 0.7 s long window for P-waves and 1 s windows for S-waves. Only measurements with correlation coefficients greater than 0.7 were kept, resulting in a total of ~4.4 million P and ~1.1 million S wave delay times. </p> <p>We sub-divided the entire dataset in 18 rectangular boxes, containing a maximum of 6000 earthquakes, orthogonal to and centered on the mean strike of the seismic sequence. The overlap between neighboring boxes is 50% with respect to the NW-SE extension. HypoDD is run separately on each box. Resulting relative locations from all boxes were combined into a single catalog, computing the weighted mean of double hypocenters in the overlapping regions (Waldhauser and Schaff, 2008).</p> <p>The final double-difference catalog includes 33,982 events occurring between 24<sup>th</sup> of August 2016 and 18<sup>th</sup> of January 2018.</p> <p>The catalog is in csv format, semicolon separator, ordered by origin time and the header content is the following:</p> <ul> <li>Id-ingv: ingv eventid, useful to link to the QuakeML phase file through the INGV fdsnws/event webservice (<a href="https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2Fwebservices.ingv.it%2Fswagger-ui%2Fdist%2F%3Furl%3Dhttps%3A%2F%2Fingv.github.io%2Fopenapi%2Ffdsnws%2Fevent%2F0.0.1%2Fevent.yaml">http://webservices.ingv.it/swagger-ui/dist/?url=https://ingv.github.io/openapi/fdsnws/event/0.0.1/event.yaml</a>) and to the reported magnitude;</li> <li>Latitude(°) expressed in decimal degrees;</li> <li>Longitude(°) expressed in decimal degrees;</li> <li>Depth(km) hypocentral depth expressed in kilometers;</li> <li>Year of origin time in the format yyyy;</li> <li>Month of origin time in the format mm;</li> <li>Day of origin time in the format dd; </li> <li>Hour of origin time in the format hh;</li> <li>Minute of origin time in the format min;</li> <li>Second of origin time in the format ??.?????? s;</li> <li>Magnitude: the value available at the phases downloading time (see Id-ingv fdsnws/event)</li> </ul> <p> </p> <p> </p> <p> </p> <p><br> </p>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
Network of Marital and Parental Relations of Western and Central European Dynasties, 1350-1550 (Burgundian Dukes Highlighted)
<p>This map visualises the marital and parental connections of princely Western- and Central European dynasties between 1350 and 1550. It features the rulers and consorts born between 1350 and 1550 and their parents. Individuals are undirectedly connected to each other based on either a marital or parental relationship. Red nodes refer to individuals (ruler and/or consort) of the Burgundian State. Nodes are scaled based on the Eigenvector Centrality Count of the individual.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</p>
Network of Marital and Parental Relations of Western and Central European Dynasties, 1350-1550
<p>This map visualizes the marital and parental connections of princely Western- and Central European dynasties between 1350 and 1550. It features the rulers and consorts born between 1350 and 1550 and their parents. Individuals are undirectedly connected to eachother based on either a marital relationship or a parental relationship. Pink nodes refer to women, green nodes refer to men. Nodes are scaled based on their Eigenvector Centrality count.</p><p>See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), <i>Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, </i>2023, pp.13-34.</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>
Impacts of centralized control on mixed traffic network performance: A strategic games analysis
<p>This dataset contains the data that were used to assess the proposed framework within the context of the case study in the paper entitled "Impacts of centralized control on mdaixed traffic network per-formance: A strategic games analysis".</p>
Road network graphs for betweenness centrality algorithm
<p>Weighted graph representation of a road network in selected regions. Derived from Open Street Map <a href="https://www.openstreetmap.org/#map=8/49.817/15.478">https://www.openstreetmap.org</a>. The dataset can be used as input for the betweenness centrality algorithm implemented here: <a href="https://code.it4i.cz/ADAS/betweenness">https://code.it4i.cz/ADAS/betweenness</a>.</p> <p><strong>Archive contents</strong><br> The archive contains following folders.</p> <p><strong>CZE</strong><br> Static graphs of three major cities in the Czech Republic (Praha, Brno, Ostrava) and entire Czech road network. Weighted by length of the road segments in metres.</p> <p><strong>PT</strong><br> Static graphs of Lisbon, Porto and entire Portugese road network. Weighted by length of the road segments in metres.</p> <p><strong>Data format</strong><br> Standard UTF-8 encoded CSV files, separated by semicolon with the following columns:<br> <br> <em>id1: (Type: unsigned long) - </em>start node<br> <em>id2: (Type: unsigned long) - </em>end node<br> <em>dist: (Type: unsigned long) - </em>weight of the edge (length in metres, unless described otherwise)<br> <em>edge_id: (Type: unsigned long) - </em>unique edge identifier<br> <br> <br> <br> </p> <p> </p> <p> </p> <p> </p>
Immune challenges increase network centrality in a queenless ant
<p>Social animals display a wide range of behavioural defences against infectious diseases, some of which inherently increase social contacts with infectious individuals (e.g., mutual grooming), while others decrease them (e.g., social exclusion). These defences often rely on the detection of infectious individuals, but this can be achieved in several ways that are difficult to differentiate. Here, we combine non-pathogenic immune challenges with automated tracking in colonies of the clonal raider ant to ask whether ants can detect the immune status of their social partners and to quantify their behavioural responses to this perceived infection risk. We first show that a key behavioural response elicited by live pathogens (allogrooming) can be qualitatively recapitulated by immune challenges alone. Automated scoring of interactions between all colony members reveals that this behavioural response increases the network centrality of immune-challenged individuals through a general increase in physical contacts. These results show that ants can detect the immune status of their nestmates and respond with a general "caring" strategy, rather than avoidance, towards social partners that are perceived to be infectious. Finally, we find no evidence that changes in cuticular hydrocarbon profiles drive these behavioural effects.</p>
Data and code from: Determinants of species’ centrality in spatially-connected plant-frugivore networks
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Immune challenges increase network centrality in a queenless ant
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(Dataset) Similarity in Consumption Patterns among Peasant Communities in Roman Central Hispania through Network Science
<p>Dataset and R script for the Brainerd-Robinson similarity analyses and dataset of the paper Similarity in Consumption Patterns among Peasant Communities in Roman Central Hispania through Network Science. Journal of Computer Applications in Archaeology. </p>
Expanding the Concept of Comprehensive Area Ratio Parameter to the South-Central States: Towards Simplifying the Structural Evaluation of Flexible Pavements at the Network Level
<p>The surface deflection bowl data collected through falling weight deflectometer (FWD) test is utilized by highway agencies in assessing the performance of the flexible pavement. However, a robust method to evaluate pavement sections utilizing FWD data from all the sensors is seldom developed. There is always a need for DOTs and highway agencies to have a simplified procedure, which can be directly implemented in agencies' databases. This study focuses on expanding and validating the concept of previously developed area ratio parameters towards the pavement section of South-Central States (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas) in effectively analyzing the pavement performances. Simulation-based deflections are utilized to develop enhanced deflection-based parameters and to reduce the need for extensive FWD testing in the field. Ninety-seven pavement sections in these states are considered to implement and validate simplified procedures that will be readily available to various transportation agencies to evaluate their pavement conditions at the network level. Due to this purpose, a pavement ranking chart is proposed for the five South-Central states, which categorizes the pavement section into very good, good, fair, and poor pavement sections. Eventually, load-induced effects concerning developed parameters are effectively analyzed to predict the remaining service life of the flexible pavement structures. The developed methodologies will be helpful for DOTs and highway agencies to carry out the rehabilitation and maintenance work in time and estimate the budget required in these procedures.</p>
Drainage network for a region in central Buenos Aires province, 30m resolution raster, EPSG:5347
<p>This drainage network was obtained using the Copernicus-30 Global Digital-Surface-Model, tiles (S37-38, W60) with QGIS and PCraster plugin: local direction drainage and flow accumulation algorithms. It helps to clarify the surrounding drainage pattern at cities like Azul, Cacharí and Tandil, for flood alleviation schemes.</p>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
Dataset - seismic data from central-western Italy used in the paper on rapid prediction of ground motion using a Convolutional Neural Network
<p>The dataset published here is the central-western Italy dataset used in the paper "<em>Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data"</em> (<a href="https://arxiv.org/abs/2105.05075">https://arxiv.org/abs/2105.05075</a>). The code for the paper is available at <a href="https://github.com/djozinovi/TLpredIM">https://github.com/djozinovi/TLpredIM</a>. The abstract of the paper:</p> <blockquote> <p>In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak ground motion intensity measures (IMs) at distant stations using only recordings from stations near the epicenter. The predictions are made without any previous knowledge concerning the earthquake location and magnitude. This approach differs from the standard procedure adopted by earthquake early warning systems (EEWSs) that rely on location and magnitude information. In the previous study, we used 10 s, raw, multistation waveforms for the 2016 earthquake sequence in central Italy for 915 events (CI dataset). The CI dataset has a large number of spatially concentrated earthquakes and a dense station network. In this work, we applied the CNN model to an area around area near Pisa, Italy. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. We found that the CNN model trained using this smaller dataset performed worse compared to the results presented in the original study by Jozinović et al. (2020). To counter the lack of data, we adopted transfer learning (TL) using two approaches: first, by using a pre-trained model built on the CI dataset and, next, by using a pre-trained model built on a different (seismological) problem that has a larger dataset available for training. We show that the use of TL improves the results in terms of outliers, bias, and variability of the residuals between predicted and true IMs values. We also demonstrate that adding knowledge of station positions as an additional layer in the neural network improves the results. The possible use for EEW is demonstrated by the times for the warnings that would be received at the station PII.</p> </blockquote>
Data Set "Protein network centralities as descriptor for QM region construction in QM/MM simulations of enzymes"
<p>This data set accompanies the publication "Efficient automatic construction of atom-economical QM regions with point-charge variation analysis" by Felix Brandt and Christoph R. Jacob (TU Braunschweig, Germany) </p> <p>It contains:</p> <p>- PDB files of the starting structures</p> <p>- modified AMBER95 force field file</p> <p>- AMS fragment files for the substrates and ions</p> <p>- AMS input files for all geometry optimizations and single point calculations</p> <p>- Python script for WISP and centrality analysis</p>
Data from: Thermal springs and active fault network of the central Colca River basin, Western Cordillera, Peru, published in Journal of Volcanology and Geothermal Research
<p>We used hydrogeochemical analysis of 35 water samples from springs and geysers, together with isotopic (δ<sup>18</sup>O and δD) analysis, chemical and mineral studies of precipitates collected in the field around these outflows, and field observations to study the thermal system of the Colca River basin in S Peru. We aimed to determine the geochemistry of thermal waters, identify fluid sources and their origin, estimate reservoir temperature, and discuss the regional tectonic and volcanic framework. Our findings presented in Tyc et al. (2022; https://doi.org/10.1016/j.jvolgeores.2022.107513) corroborate a heterogeneous and complex geothermal system in the central region of the Colca River basin. This system exhibits contrasting hydrogeochemical and physical characteristics, variable isotope compositions, distinct reservoir temperatures, and associated precipitates near thermal springs. The control of water chemistry in this area is closely linked to the activity of the Ampato-Sabancaya magmatic chamber and the presence of tectonic structures, which enable intricate interactions between meteoric waters, magmatic fluids, and gases.</p> <p>Here, we present datasets used in the article (Tyc et al., 2022; https://doi.org/10.1016/j.jvolgeores.2022.107513), including:</p> <p>- Physicochemical characteristics of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 1)</p> <p>- Chemical and isotopic composition of water samples collected by authors in the field in September 2012 and August–September 2017 (Table 2) and those monitored by INGEMMET in years 2013-2018 (Table 3)</p> <p>- Chosen molecular ratios discussed in Tyc et al., 2022 (Table 4)</p> <p>- Calculated reservoir temperature with the use of different Na/K geothermometers (Table 5)</p> <p>- Mineral phases in efflorescences precipitating at the water sampling sites (Table 6).</p> <p>Thirty-five sets of water samples were collected in the field in September 2012 and August–September 2017 using polyethylene bottles of high density (Table 1). Consequently, these were analyzed in the Water Analysis Laboratory at the University of Silesia in Katowice (Poland; Table 2). Water temperatures, pH, and electrical conductivity were measured in the field using portable pH meter CP-315 and conductivity meter CC-315, both with temperature sensors, with an accuracy of ±0.1 °C, ±0.01 pH, and ± 0.1% (up to 19.999 mS/cm) or ± 0.25% (above 20.00 mS/cm), respectively. Discharge of springs was estimated if possible (Table 1). Both cations and anions were analyzed by ion chromatography using Methron 850 Professional Ion Chromatograph with separate Metrosept C4–150 and A-supp 7–250 columns for cations and anions, respectively (Tables 2 and 4). Analysis of water analyses collected by INGEMMET in years 2013-2018 was performed at the INGEMMET Chemical Laboratory in Lima with the use of ion chromatography (Dionex ICS 5000) for the determination of anions and inductively coupled plasma optical emission spectrometry (ICP-OES) – VARIAN for cations (Table 3). Isotopic analyses (δ<sup>2</sup>H, δ<sup>18</sup>O) of 17 water samples collected in 2017 were performed at the Stable Isotope Laboratory Institute of Geological Sciences Polish Academy of Sciences (Table 2). The δ<sup>2</sup>H values of studied H<sub>2</sub>O were measured using the H-Device peripheral coupled to MAT 253 IRMS (Thermo Scientific) in a dual inlet system. For the determination of δ<sup>18</sup>O in H<sub>2</sub>O, an equilibration technique was used. The analysis used the GasBench II peripheral device (Thermo Scientific) coupled to MAT 253 IRMS with a continuous He flow. The AquaChem 4.0.284 software was used to evaluate the water samples' geochemical properties and calculate reservoir temperature for thermal waters (Table 5). Precipitates found at the water sampling sites were collected separately into plastic bags with strings and sealed boxes. These samples were subsequently analyzed at the Institute of Earth Sciences, University of Silesia in Katowice. The qualitative chemical composition and mineral characteristics were examined using a Philips XL 30 ESEM/TMP scanning electron microscope coupled with an energy-dispersive spectrometer (EDS; EDAX type Sapphire). The phase composition of the precipitates was determined through X-ray diffraction (XRD) using a Philips PW 3710 diffractometer. The XRD data were analyzed and interpreted using the X'Pert HIGHScore Plus software (Table 6).</p>
The upgrade of Waveform Dataset in Continuous Mode by improved Montefeltro Seismic Network (MF) in Central-Northern Italy from 2021 to 2022
<p>The temporary seismic network of Montefeltro (FDSN Network code: 1S),<br>which covers from December 2018 a geographic area between the regions of<br>Emilia-Romagna and the Marches in central-northern Italy, was implemented with a<br>new seismic station (ISC Station code: MF05) located in the surroundings of the<br>town of Perticara (RN), Italy. The new station increases the coverage in the<br>northern sector of the above described area and the recording by the local network<br>combined with that of the National Seismic Network allows to obtain a better<br>resolution in the relocation of events occurred in the area. After this station<br>addition, the array of Montefeltro seismic network actually consists of four<br>stations with the last one equipped by dynamic digitizer (Gaia2 data acquisition<br>unit) and three component extended band seismometer (Lennartz 3D/5s sensor).<br>The local mobile network records seismic data in continuous mode at 100 sps.</p><p>Data availability for all the stations (registered in ISC) of the network 1S<br>during the time period 2021-2022 is tabled and uploaded in an apart pdf file.<br> <br>Seismic Instruments and Dataless information:<br>Station code Sensor Recorder<br>MF01 Lennartz 3D/5s Sara SL06<br>MF03 Guralp CMG/20s Reftek 130<br>MF04 Guralp CMG/30s Reftek 130<br>MF05 Lennartz 3D/5s INGV Gaia2</p><p>The dataless files are separately provided. The dataless format contains<br>a complete and comprehensive history of metadata for seismic networks<br>and their stations, including geographic coordinates and response information.</p><p>List for the dataless files of the Montefeltro seismic network<br>MF01 1S_MF01.dless<br>MF03 1S_MF03.dless<br>MF04 1S_MF04.dless<br>MF05 1S_MF05.dless</p><p>The complete dataset of this study is also uploaded in the Zenodo repository.<br>The repository contains a zip file for each recording year, subdivided by<br>stations belonging to the network 1S, and a single zip file for the dataless files.</p>
Data from:Spatio-temporal networks: reachability, centrality and robustness
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Data and code from: PathVGAE: A path-based variational graph autoencoder framework for ranking centrality in road networks
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ScienceDex guides
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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.