Find research datasets worth reusing

opencc-by-nc-4.0Oct 2016View details →

Indexed Data Set From Molisan Regional Seismic Network Events

Abstract: After the earthquake occurred in Molise (Central Italy) on 31st October 2002 (Ml 5.4, 29 people dead), the local Servizio Regionale per la Protezione Civile to ensure a better analysis of local seismic data, through a convention with the Istituto Nazionale di Geofisica e Vulcanologia (INGV), promoted the design of the Regional Seismic Network (RMSM) and funded its implementation. The 5 stations of RMSM worked since 2007 to 2013 collecting a large amount of seismic data and giving an important contribution to the study of seismic sources present in the region and the surrounding territory. This work reports about the dataset containing all triggers collected by RMSM since July 2007 to March 2009, including actual seismic events; among them, all earthquakes events recorded in coincidence to Rete Sismica Nazionale Centralizzata (RSNC) of INGV have been marked with S and P arrival timestamps. Every trigger has been associated to a spectrogram defined into a recorded time vs. frequency domain. The dataset has been fully indexed in respect of the recorded spectra: list of all records, list of earthquakes, list of multiple earthquakes records. The main aim of this structured dataset is to be used for further analysis with data mining and machine learning techniques on image patterns associated to the waveforms.

openmit-licenseDec 2016View details →

Non-random network connectivity comes in pairs: Code & generated data to reproduce results and figures of the article

Complete research code and generated data for the article to reproduce the figures and computations referenced. Please visit https://non-random-connectivity-comes-in-pairs.github.io/ for documentation of the code.

Supplementary material 3: List of tested and analyzed data sharing tools (non-exhaustive) from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

List of tested and analyzed data sharing tools (non-exhaustive)

Supplementary material 2: Definitions and Concepts from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

Definitions and concepts in the context of the main paper.

Supplementary material 1: List of selected tools. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

List of selected tools.

Figure 7. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

Figure 7. - Mobile app for sporadic observations reporting.

Figure 2. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390

Figure 2. - The Plazi workflow (green) within EU BON.

opencc-by-4.0Mar 2017View details →

Data supplementing the article "Assessing ecological status with diatoms DNA metabarcoding : scaling-up on a WFD monitoring network (Mayotte island, France)" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal

These data supplement the article"Assessing ecological status with DNA metabarcoding or microscopy? Comparison using benthic diatoms in tropical rivers" V. Vasselon, F. Rimet, K. Tapolczai, A. Bouchez submitted to Ecological Indicators journal. The directory contains the following files: 80 PGM sequencing libraries (raw data, fastq files).rar - contains the 80 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments). 80 fastq files information.xlsx - contains the information relative to the 80 samples including: the ID used in Mothur analyses (corresponding to the name of the fastq files), the sample name, the sampling site code, the name of the river, the monitoring network to which rivers belong, the year of sampling and the GPS coordinates of sampling sites. OTU (95 percent of similarity) list of 80 Mayotte samples.xlsx - contains the final OTU list obtained after applying all the bioinformatics treatments (trimming, clustering,...): OTUs created at 95% of similarity, the number of DNA reads per sample was normalized at 5710 reads (the smallest values obtained in one sample). A DNA representative sequence and the taxonomic assignment determined using Mothur (using classify.otu command) are also provided for each OTU.

opencc-by-4.0May 2017View details →

Data for Properties of Kinetic Transition Networks for Atomic Clusters and Glassy Solids

Databases of minima and transition states for Morse clusters, in two and three dimensions at a variety of ranges.

opencc-by-4.0May 2017View details →

Supplemental 3D Model Data - New insights into the evolutionary history of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network (SPIERSView and VAXML format)

Three-dimensional reconstruction models of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network in SPIERSView and VAXML format. 2D and 3D (Red/Cyan) images also provided as a PDF. Notes: <ol> <li>SPIERSView file (.SPV) models can conveniently be viewed using the SPIERSView software, freely available in both Windows and Mac versions from http://www.spiers‐software.org. However, note that low-performance computers may not possess a sufficiently powerful graphics card to render and rotate the model.</li> <li>VAXML file format models are saved as a ZIP-compressed VAXML datasets. VAXML uses one or more .STL files to define the geometry of objects that comprise the dataset, together with one .VAXML file that provides metadata on the dataset as a whole, and specifies how the .STL files should be put together. We recommend using the free SPIERS software to view this model format (http://spiers-software.org/). However, .STL files can be opened independently in several freely available software programs (e.g. MeshLab, Blender). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm.</li> </ol>

opencc-by-4.0Jun 2017View details →

Supplementary Data: The Benefits of Cooperation in a Highly Renewable European Electricity Network

Supplementary Data The Benefits of Cooperation in a Highly Renewable European Electricity Network doi:10.1016/j.energy.2017.06.004 arXiv:1704.05492 The files in this record contain the model-specific code, input data, and output data considered in the Benefits of Cooperation paper. You are welcome to use the provided data under the given open-source licence, and if you do please cite the paper doi:10.1016/j.energy.2017.06.004. Please note that the derivation of the data in data/renewables/ is not open, because it uses the REatlas software [7] which has a closed source server part. (There is an free software implementation of the REatlas at https://github.com/FRESNA/atlite but it wasn't ready in time to be used for this dataset.) The code that is required to generate the output data consists of <ul> <li>the python code opt_ws_network.py that builds and runs the PyPSA [0] model</li> <li>a SLURM script parameter_batch.py to run the model with different parameters</li> <li>a YAML file options.yml with the default parameter settings</li> </ul> The code heavily relies on the python package vresutils which is available at https://github.com/FRESNA/vresutils The record also contains the input data in the data/ directory. They are described in detail in the paper, but a short summary is provided here: <ul> <li>costs: cost and other input parameter assumptions, see Table 1 in the paper.</li> <li>graph: the network topology is given by a list of nodes (country names) and a list of edges connecting two nodes. Based on [1,2].</li> <li>hydro: hydro generation data provided by the Restore2050 project [3] <ul> <li>inflow/: contains a csv files with daily inflow data for each country</li> <li>emil_hydro_capas.csv: country-scale power and energy capacity</li> <li>ror_ENTSOe_Restore2050.csv: the share of run-of-river of the total hydro generation, from ENTSO-E [4] or if unavailable from [3]</li> </ul> </li> <li>load: hourly country-scale consumption for 2011 from ENTSO-E [5]</li> <li>renewables: generation potentials for the renewable technologies onshore wind, offshore wind, and solar per country based on historic weather data [6]. The jupyter-notebook europe_renewables_potentials.ipynb describes the data generation and uses the REatlas software [7] which has open-source client but closed-source server software. The used cutout can therefore not be made available here, but is solely based on data from [8]. The processed data are in: <ul> <li>store_p_nom_max/: installation potential per technology per region</li> <li>store_o_max_pu_betas/: hourly maximum generation per unit of capacity per technology per region</li> </ul> </li> </ul> The output data generated by the model is in sub-folders of the results/ directory following the naming scheme [costsource]-CO[CO2costs]-T[timerange]-[technologies]-LV[linevolume]_c[crossover]_base_[costsource]_solar1_7_[formulation]-[startdate]/, where <ul> <li>costsource = diw2030</li> <li>CO2costs = 0</li> <li>timerange = 1_8761</li> <li>technologies = wWsgrpHb</li> <li>linevolume = [float], None (line volume constraint of float * 5e8 TWkm, or optimised line volume)</li> <li>crossover = 0 (deactivated the cross-over phase of the Gurobi optimiser)</li> <li>formulation = angles, [blank] (power flow formulations: 'angles', or 'cycles')</li> <li>startdate = time the optimisation was started</li> </ul> Footnotes [0] https://pypsa.org/ , https://doi.org/10.5281/zenodo.582307 [1] S Becker, Transmission grid extensions in renewable electricity systems, PhD thesis (2015) [2] ENTSO-E, Indicative values for Net Transfer Capacities (NTC) in Continental Europe. European Transmission System Operators, 2011, https://www.entsoe.eu/publications/market-reports/ntc-values/ntc-matrix/Pages/default.aspx, accessed Jul 2014. [3] A Kies, K Chattopadhyay, L von Bremen, E Lorenz, D Heinemann, Simulation of renewable feed-in for power system studies, RESTORE 2050 project report, https://doi.org/10.5281/zenodo.804244 [4] European Transmission System Operators, Installed Capacity per Production Type in 2015, ENTSO-E (2016), https://transparency.entsoe.eu/generation/r2/installedGenerationCapacityAggregation/show [5] https://www.entsoe.eu/db-query/country-packages/production-consumption-exchange-package [6] D. Heide, M. Greiner, L. Von Bremen, C. Hoffmann, Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation, Renewable Energy 36 (9) (2011) 2515–2523. https://doi.org/10.1016/j.renene.2011.02.009 [7] G. B. Andresen, A. A. Søndergaard, M. Greiner, Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis, Energy 93, Part 1 (2015) 1074 – 1088. https://doi.org/10.1016/j.energy.2015.09.071 [8] S Saha et al., 2014: The NCEP Climate Forecast System Version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1

opencc-zeroOct 2023View details →

Data from: Social networks reveal sex- and age-patterned social structure in Butler's Gartersnakes

Sex- and age-based social structures have been well-documented in animals with visible aggregations. However, very little is known about the social structures of snakes. This is most likely because snakes are often considered non-social animals and are particularly difficult to observe in the wild. Here, we show that wild Butler's Gartersnakes have an age and sex assorted social structure similar to more commonly studied social animals. To demonstrate this, we use data from a 12-year capture-mark-recapture study to identify social interactions using social network analyses. We find that the social structures of Butler's Gartersnakes comprise sex- and age-assorted intra-species communities with older females often central and age segregation partially due to patterns of study site use. In addition, we find that females tended to increase in sociability as they aged while the opposite occurred in males. We also present evidence that social interaction may provide fitness benefits, where snakes that were part of a social network were more likely to have improved body condition. We demonstrate that conventional capture data can reveal valuable information on social structures in cryptic species. This is particularly valuable as research has consistently demonstrated that understanding social structure is important for conservation efforts. Additionally, research on the social patterns of animals without obvious social groups provides valuable insight into the evolution of group living.

Theory and implementation of inelastic Constitutive Artificial Neural Networks: Source code and data

This dataset contains the source code of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication: Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl. Theory and implementation of inelastic Constitutive Artificial Neural Networks. arXiv: <a href="https://doi.org/10.48550/arXiv.2311.06380">https://doi.org/10.48550/arXiv.2311.06380</a> Computer Methods in Applied Mechanics and Engineering: <a href="https://doi.org/10.1016/j.cma.2024.117063">https://doi.org/10.1016/j.cma.2024.117063</a>   01_Example01:  Artificially generated data This example investigates whether the iCANN is able to discover a model for the data generated by a continuum mechanical model.   02_Example02: Discovering a model for the polymer VHB 4910 subjected to cyclic loading Here, we investigate the ability of iCANN to discover and learn a model for the material response of  VHB 4910 polymer subjected to cyclic loading at different stretch rates. The experimental data are taken from the literature: Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. Computational Materials Science, 59, 65-74. <a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a>   03_Example03: Discovering a model for passive skeletal muscle subjected to relaxation In this example, we investigate whether the iCANN is able to discover a model for the material behavior of passive skeletal muscles. A total of five independent experiments are carried out in which the maximum applied compression stretch and the stretch rate are varied. In addition, the learning performance of the iCANN is investigated. Training is first carried out in each of the five experiments and then in each of four of the five experiments. The experimental data are taken from the literature: Van Loocke, M., Lyons, C. G., & Simms, C. K. (2008). Viscoelastic properties of passive skeletal muscle in compression: stress-relaxation behaviour and constitutive modelling. Journal of biomechanics, 41(7), 1555-1566. <a href="https://doi.org/10.1016/j.jbiomech.2008.02.007">https://doi.org/10.1016/j.jbiomech.2008.02.007</a>   python_requirements.txt: File containing a list of installed Python modules used to implement the iCANN

Vesuvio cGNSS Network - Rinex data quality control (2020)

For each cGNSS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:<ol><li>cGNSS station name (Name),</li><li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GNSS week (Week),</li><li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GNSS week (Week),</li><li>the start and end times of the window (time format is year month day hour min),</li><li>window time laps (Hrs),</li><li>observation interval (OI),</li><li>the number of possible observations (#expt) above the elevation mask,</li><li>the number of complete observations (#obs),</li><li>the ratio of complete to possible observations as a percent (DCP),</li><li>the RMS "multipath combinations" values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP2) rounded to two decimal points,</li><li>cycle slips (CS),</li><li>the ratio of complete observations to cycle slips (obs/CS).</li></ol>A full description of cGNSS network is reported in: - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.Please cite this when using the dataset

Campi Flegrei cGNSS Network - Rinex data quality control (2020)

opencc-zeroNov 2023View details →

Data from: Clots reveal anomalous elastic behavior of fiber networks

The adaptive mechanical properties of soft and fibrous biological materials are relevant to their functionality. The emergence of the macroscopic response of these materials to external stress and intrinsic cell traction from local deformations of their structural components is not well understood. Here, we investigate the nonlinear elastic behavior of blood clots by combining microscopy, rheology, and an elastic network model that incorporates the stretching, bending, and buckling of constituent fibrin fibers. By inhibiting fibrin crosslinking in blood clots, we observe an anomalous softening regime in the macroscopic shear response as well as a reduction in platelet-induced clot contractility. Our model explains these observations from two independent macroscopic measurements in a unified manner, through a single mechanical parameter, the bending stiffness of individual fibers. Supported by experimental evidence, our mechanics-based model provides a framework for predicting and comprehending the nonlinear elastic behavior of blood clots and other active biopolymer networks in general.

Data and code from Lamb et al., "Evaluating conservation units using network analysis: a sea duck case study"

This file consists of data and code used to construct network models for scoters in North America and is associated with the manuscript "Evaluating conservation units using network analysis: a sea duck case study" published in Frontiers in Ecology and the Environment.  Continental scoter network mapping is the R script used to run analyses. duck_nodes is the main datafile. Columns are organized as follows:id - unique identifierspecies - species from which the centroid was obtained (BLSC = black scoter, SUSC = surf scoter, WWSC = white-winged scoter)stage - period of the annual cycle to which the centroid belongs (W = winter, B = breeding, S = spring staging, M = fall staging and molt, WM = winter migration, BM = breeding migration, MM = molt migration, SM = spring migration)site - position of centroid within season (i.e., W1 = first site occupied during winter, W2 = second site occupied, etc.)cycle - number of annual cycles following transmitter attachment (1 = first cycle after attachment, 2 = second cycle after attachment, etc.)sex - sex of individual (M = male, F = female)age - age of individual (HY = hatch year, SY = second year, TY = third year, ASY = after second year, ATY = after third year, AHY = after hatch yearcapture_reg - general area where individual was capturedcapture_subreg - specific region within capture region where individual was capturedlon - longitude of centroidlat - latitude of centroidduration - number of days spent at centroidstart - date of arrival at centroidend - date of departure from centroidjstart - Julian date of arrival at centroidjend - Julian date of departure from centroidseason - season of annual cycle in which centroid occurred (W = winter, F = fall, B = breeding, S = spring)year - calendar year in which centroid beganto - node in which centroid is groupedfrom - node in which previous centroid is grouped (i.e., node in which indiviual was located before moving to present node)to_sea - season of annual cycle in which centroid occurredfrom_sea - season of annual cycle in which previous centroid occurredtype - movement type to centroid; the first letter represents the season (coded as in "season" column), and the second represents the nature of the movement (WD = dispersal within a season, M = migration among seasons)type2 - same as "type", but with dispersal movements coded by the stage in which they occur (W = winter, B = breeding, SM = spring migration, WM = winter migration)ew - capture location in eastern (east; Atlantic and Great Lakes) or western (west; Pacific) North Americacount_ind_sp - total number of tracked individuals of the species represented by centroidwt - base centroid weight (all centroids equal, deployments excluded)wt_sp - species-adjusted centroid weight: for centroid x in species s, weightx = (N centroids) × (1 / (n centroids in s))wt_dur - duration-adjusted centroid weight: for centroid x, weightx = (days at centroid location) × 365-1wt_ind -  individual-adjusted centroid weight: for centroid x in individual j, weightx = 1 / (n centroids in j)wt_ind_sp - individual and species adjusted centroid weight: for centroid x, individual j, and species s, weightx = (N centroids / (N species * n individuals in s)) × (1 / (n centroids in j))wt_cap - capture location adjusted centroid weight: for centroid x and capture location c, weightx = (N centroids / N capture locations) / n centroids in cwt_ew - east-west adjusted centroid weight: for for centroid x and region r, weightx = (N centroids / N regions) / n centroids in rwt_spew - species and east-west adjusted centroid weight: for centroid x species s, and region r, weightx = (N centroids / (N species × N regions)) / n centroids for species s in region rwt_indspew - individual, species, and east-west adjusted centroid weight: for centroid x, individual j, species s, and region r, weightx = N centroids / (N species × N regions × n centroids for individual j × n individuals for species s in region r)wt_spewcap - species, east-west, and capture location adjusted centroid weight: for centroid x, species s, capture location c, and region r, weightx = N centroids / (N species × N regions × n centroids for species s in capture location c × n capture locations for species s in region r)

opencc-zeroDec 2023View details →

Data from: Network-level changes in the brain underlie fear memory strength

The strength of a fear memory significantly influences whether it drives adaptive or maladaptive behavior in the future. Yet, how mild and strong fear memories differ in underlying biology is not well understood. We hypothesized that this distinction may not be exclusively the result of changes within specific brain regions, but rather the outcome of collective changes in connectivity across multiple regions within the neural network. To test this, rats were fear conditioned in protocols of varying intensities to generate mild or strong memories. Neuronal activation driven by recall was measured using cfos immunohistochemistry in 12 brain regions implicated in fear learning and memory. The interregional coordinated brain activity was computed and graph-based functional networks were generated to compare how mild and strong fear memories differ at the systems level. Our results show that mild fear recall is supported by a well-connected brain network with small-world properties in which the amygdala is well-positioned to be modulated by other regions. In contrast, this connectivity is disrupted in strong fear memories and the amygdala is isolated from other regions. These findings indicate that the neural systems underlying mild and strong fear memories differ, with implications for understanding and treating disorders of fear dysregulation.

opencc-zeroDec 2023View details →

Data from: Latest Ordovician (Hirnantian) brachiopod faunal lists used for non-matric multidimensional scaling (NMDS) and network analyses

A total of 107 brachiopod genera of Hirnantian age among 42 localities worldwide are compiled into a binary dataset (Table S1; presence =1, absence = 0). The majority of the faunal lists was derived from the well-screened Hirnantian brachiopod faunal data of Rong et al. (2020). In this study, the Hirnantian faunal lists are updated for the following localities: Anticosti Island, eastern Canada; Edgewood region, American Mid-Continent; Mackenzie Mountains, northwestern Canada. Detailed discussions on these faunal update and references are provided in the main paper (section on Paleobiogeography of the Mackenzie Mountains Hirnantian fauna).