Find research datasets worth reusing

opencc-by-nc-4.0Oct 2019View details →

Figure 4 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil

Figure 4. Complex ecological network constructed with information about interactions between frugivorous birds (triangles) and plants (circles) in the Brazilian Cerrado. Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Species with the highest values of connectivity and centrality are circled. Names in triangles and circles are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).

opencc-by-4.0Jun 2021View details →

Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks- dataset

Dataset associated with the paper "Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks"

Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network

This replication package contains the schedule for the first cohort of our program described in the paper titled "Leveraging Diversity in Software Engineering Education through Community Engaged Learning and a Supportive Network".

Rennes Urban Network : Hourly temperature for year 2018 at Melesse station

Hourly temperature for year 2018. Davis Vantage-Pro 2 station at 2 meters above ground (low grass) Location : France, Brittany Latitude: 48.205 ; Longitude : -1.6813   <table> <tbody> <tr> <td> </td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>

DAEMON: Network intelligence for aDAptive and sElf-Learning MObile Networks

The DAEMON H2020 european project develops and implements innovative and pragmatic approaches to Network Intelligence (NI) design that enable high performance, sustainable and extremely reliable zero-touch network system. DAEMON designs an end-to-end NI-native architecture for Beyond 5G (B5G) that fully coordinates NI-assisted functionalities. Main website: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqblFJekN4dzdUcHYzQXlMd19rS0xjSDNleWVlZ3xBQ3Jtc0tsSmpkdWVUaXBSVDRHb2w5WDhGZnN3S05DM0FSRk9VS3RDc1hTT0l6MVhtd1pvWmZ2bEtRRUFxb0xkRmRQa1lVWVF0a1B2M1RwdXlmS2lIWEF0LWt4Q1ZIQndSNHNJcVJaSVNQODgtRThEaHh4TDBuaw&q=https%3A%2F%2Fh2020daemon.eu%2F&v=VNMVe8S-Ees">https://h2020daemon.eu/</a>

Activity at the DAEMON booth in the European Conference on Networks and Communications

@h2020daemon booth and 3 Demos at European Conference on Networks and Communications (@EuCNC) 2022 @Telefonica_En @tudelft @InformaticaUMA @IMDEA_SOFTWARE @UC3M @nec_sws @i2CAT @ADLINK_Tech @IMEC @BellLabs @SrsSystems @wings_ict @zettascaletech <a href="https://www.youtube.com/hashtag/h2020daemon">#h2020daemon</a> <a href="https://www.youtube.com/hashtag/h2020">#H2020</a> @EU_H2020 @5GPPP

Overview of the DAEMON booth at the European Conference on Networks and Communications

Present‑day crustal deformation across the Daliang Shan, southeastern Tibetan Plateau: constrained by a dense GPS network

1. Intensive observations    In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas.In this study, we collected and processed GPS data from three sources to obtain a crustal horizontal velocity field. The dataset from the first source was raw GPS observations primarily from Phase I of the Crustal Movement Observation Network of China (CMONOC), which was resurveyed every 2 or 3 years from 1999 to 2007, and Phase II of the CMONOC, which involved campaign surveys every year from 2009 to 2020 and continuous surveys from 2010. The dataset from the second source was obtained from the National Key Research and Development Program of China. This dataset contained data from 31 continuous-measurement sites located close to the Anninghe–Zemuhe–Daliangshan fault zone, which were operated from August 2019 to August 2021, and 38 campaign sites from the National GPS Geodetic Control Network of China (NGGCNC), which were measured in 2014 and 2019. All of the campaign surveys used dual-frequency GPS receivers and choke ring antennas, with an operation of 3–4 consecutive days. The dataset from the third source consisted of published GPS velocities from existing studies of the Daliang Shan and its adjacent areas. 2. Data processing We employed the GAMIT and GLOBK software (Herring et al., 2015a, 2015b) to process the raw GPS data and derived the GPS positioning time series with respect to the international terrestrial reference frame for 2014 (ITRF2014) (Altamimi et al., 2017). We utilized the GAMIT software to process the double-differenced carrier-phase observations and acquired regional daily loosely constrained solutions for the site coordinates and satellite orbits. The geophysical models used have been described by Hao et al. (2021). In addition, we employed the same strategy to process ~70 evenly distributed ITRF core GPS sites to acquire global daily loosely constrained solutions. Then, we employed the GLOBK software to combine the same regional and global daily solutions to obtain a GPS time series. Three large earthquakes occurred in the study area: the 2004 M 9.1 Sumatra earthquake, the 2008 M 8.0 Sichuan Wenchuan earthquake, and the 2013 M 7.0 Sichuan Lushan earthquake. For the GPS time series for the campaign sites, we utilized the coseismic slip model of the 2004 Sumatra earthquake (Chlieh et al., 2007). We interpolated the coseismic displacements of the 2008 Wenchuan earthquake (Shen et al., 2009) to correct the coseismic offsets. We only used the data observed before 2008 for those GPS sites contaminated by significant postseismic deformation related to the 2008 Wenchuan earthquake (Wang & Shen, 2020). For the GPS sites affected by the coseismic deformation caused by the 2013 Lushan earthquake (Jiang et al., 2014), we also used data observed before the mainshock to mitigate the coseismic and postseismic deformation. After removing the transient deformation caused by the earthquakes, we used the weighted least-squares adjustment method to estimate linear trends of the velocities. We used the linear trend, seasonal variations, coseismic offset, and color noise model for the continuous GPS sites to fit the time series. We utilized the maximum likelihood estimation (MLE) technique and the CATS software (Williams et al., 2004; Williams., 2008) to estimate the characteristics of the noise in the residuals of the GPS time series after removing the linear trend and seasonal variations (Hao et al., 2016). Then, we obtained the GPS velocities with respect to the ITRF2014 and applied Euler rotation to transfer it to the Eurasia-fixed frame (Altamimi et al., 2017). The reference frames of the GPS velocities reported in previous studies are different from ours. Therefore, to transfer the latter to our selected frame, we employed the Helmert transformation with four parameters through common sites for our velocities and the published velocities. We only chose spatially uniformly distributed common sites with post-fit residuals of less than 1.0 mm/yr in the north-ward and east-ward components. Finally, we derived the geodetically consistent GPS crustal movement in the Daliang Shan and its adjacent areas with respect to the stable Eurasian Plate. Additionally, in order to reduce the residual rigid motion caused by the far-field reference of the Eurasian Plate, we chose the stable South China block as the near-field reference frame. Subsequently, our derived GPS velocities were translated into the South China block reference frame using the published Euler rotation vectors (Hao et al., 2019).   References                                                                 Altamimi, Z., Métivier, L, Rebischung, P., Rouby, H., Collilieux, X., 2017. ITRF2014 plate motion model. Geophys. J. Int. 209:1906–1912 Chlieh, M., Avouac, J. P. , Hjorleifsdottir, V. , Song, T. , Ji, C. , Sieh, K., Sladen, A., Hebert, H., Prawirodirdjo, L., Bock, Y., Galetzka, J., 2007. Coseismic slip and afterslip of the great Mw 9.15 Sumatra-Andaman earthquake of 2004. Bulletin of the Seismological Society of America, 97(1A), 152–173. Hao, M., Freymueller, J. T., Wang, Q. L., Cui, D. X., Qin, S. L. 2016. Vertical crustal movement around the southeastern Tibetan Plateau constrained by GPS and GRACE data. Earth and Planetary Science Letters, 437, 1-8. http://dx.doi.org/10.1016/j.epsl.2015.12.038. Hao, M., Li, Y., Zhuang, W., 2019. Crustal movement and strain distribution in east Asia revealed by GPS observations. Scientific Reports, https://doi.org/10.1038/s41598-019-53306-y, 16797. Hao, M., Wang, Q., Zhang, P., Li, Z., Li, Y., Zhuang, W., 2021. “Frame wobbling” causing crustal deformation around the Ordos block. Geophysical Research Letters 48, e2020GL091008. https://doi.org/10.1029/2020GL091008. Herring, T.A., King, R.W., McClusky, S.C., 2015a. GAMIT reference manual, GPS analysis at MIT, Release 10.6. Massachusetts Institute of Technology, Cambridge. Herring, T.A., King, R.W., McClusky, S.C., 2015b. GAMIT reference manual, global Kalman filter VLBI and GPS analysis program, Release 10.6. Massachusetts Institute of Technology, Cambridge. Jiang, Z., Wang, M., Wang, Y., Wu, Y., Che, S., Shen, Z.K., Bürgmann, R., Sun, J., Yang, Y., Liao, H., Li, Q., 2014. GPS constrained coseismic source and slip distribution of the 2013 Mw6.6 Lushan, China, earthquake and its tectonic implications. Geophysical Research Letters 41, 407–413, doi:10.1002/2013GL058812. Shen, Z.K., Sun, J., Zhang, P., Wan, Y., Wang, M., Bürgmann, R., Zeng, Y.H., Gan, W.J., Wang, Q.L., 2009. Slip maxima at fault junctions and rupturing of barriers during the 2008 Wenchuan earthquake. Nat Geosci 2:718–724. Wang, M., Shen, Z.K., 2020. Present-day crustal deformation of continental China derived from GPS and its tectonic implications. J. Geophys. Res. 125 (2) https://doi. org/10.1029/2019JB018774. Williams, S.D.P., 2008. CATS: GPS coordinate time series analysis software. GPS Solutions, 12, 147–153. <a href="http://dx.doi.org/10.1007/s10291-007-0086-4">http://dx.doi.org/10.1007/s10291-007-0086-4</a>. Williams, S.D.P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R.M., Prawirodirdjo, L., Miller, M., Johnson, D.J. 2004. Error analysis of continuous GPS position time series. J. Geophys. Res. 109 (B03412). http://dx.doi.org/10.1029/2003JB002741.

openother-openOct 2022View details →

Analyses, data and figures related to: "Connecting ships: Using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and visualise wood use patterns"

Analyses, data and figures related to: "Connecting ships: using dendrochronological network analysis to determine the wood provenance of Roman-period river barges found in the Lower Rhine region and to visualise patterns of wood use" by Ronald M. Visser (Saxion University of Applied Sciences, Deventer, the Netherlands) and Yardeni Vorst (Vorst wood research, Zaandam, the Netherlands) submitted to the International Journal of Wood Culture

opencc-by-4.0Dec 2012View details →

Supplementary material 1 from: Virkkala R, Rajasärkkä A (2012) Preserving species populations in the boreal zone in a changing climate: contrasting trends of bird species groups in a protected area network. Nature Conservation 3: 1-20. https://doi.org/10.3897/natureconservation.3.3635

Mean densities and number of observations of species in 1981–1999 and in 2000–2009

opencc-by-4.0Oct 2017View details →

Synthetic Networks

Set of synthetic networks generated using the Java JUNG library. Networks divided into folders based on network size, with range of sizes from 1000 to 250000 nodes. Contains small world, scale free and random networks. First line of file reads: 'Seed for weights:[Number]' Provides a seed for any random number generator to allow for consistent edge weights when re-using network. Rest of file made of node pairs written as : [Source node ID]:[Destination node ID]

opencc-by-4.0Oct 2017View details →

Cooperative cortical network for categorical processing of Chinese lexical tone

This dataset contains the ECoG, CT and MRI data for the six subjects associated with the manuscript, "Cooperative cortical network for categorical processing of Chinese lexical tone", as well as stimulus sound files used in the study. data_MRI.rar - MRI scan before the ECoG electrode implantation data_CT.rar - CT images with ECoG electrode implanted data_ECoG.rar - ECoG recording of six patients reported in the manuscript stimuli_BehaviorContinumm.rar - Chinese tone continuum stimuli (Token 1-13 as in Fig 1 of the manuscript) stimuli_ECoG_MMN.rar - Chinese tone stimuli used for oddball paradigm in ECoG experiment (Token 2, 5 and 8, see Fig 1 of the manuscript) Upon email request (hongbo@tsinghua.edu.cn), the authors may provide analysis code. Original manuscript: Si, Zhou and Hong. PNAS, 2017 Cooperative cortical network for categorical processing of Chinese lexical tone Abstract: In tonal languages such as Chinese, lexical tone with varying pitch contours serves as a key feature to provide contrast in word meaning. Similar to phoneme processing, behavioral studies have suggested that Chinese tone is categorically perceived. However, its underlying neural mechanism remains poorly understood. By conducting cortical surface recordings in surgical patients, we revealed a cooperative cortical network along with its dynamics responsible for this categorical perception. Based on an oddball paradigm, we found amplified neural dissimilarity between cross-category tone pairs, rather than between within-category tone pairs, over cortical sites covering both the ventral and dorsal streams of speech processing. The bilateral superior temporal gyrus (STG) and the middle temporal gyrus (MTG) exhibited increased response latencies and enlarged neural dissimilarity, suggesting a ventral hierarchy that gradually differentiates the acoustic features of lexical tones. In addition, the bilateral motor cortices were also found to be involved in categorical processing, interacting with both the STG and the MTG and exhibiting a response latency in between. Moreover, the motor cortex received enhanced Granger causal influence from the semantic hub, the anterior temporal lobe, in the right hemisphere. These unique data suggest that there exists a distributed cooperative cortical network supporting the categorical processing of lexical tone in tonal language speakers, not only encompassing a bilateral temporal hierarchy that is shared by categorical processing of phonemes but also involving intensive speech-motor interactions over the right hemisphere, which might be the unique machinery responsible for the reliable discrimination of tone identities.

opencc-by-4.0Jun 2017View details →

TWEETHER Future Generation W-band Backhaul and Access Network Technology

Paper Presented at EUCNC 2017. Data of lens antenna simulated by 3D simulator and measured by Vector Network analyser. Measurements of chips on wafer.

opencc-by-sa-4.0Nov 2017View details →

Structure, function, and control of the musculoskeletal network - Data

Supplementary data for: Structure, function, and control of the musculoskeletal network Table S8: The assigned homunculus categories and data driven community assignments of muscles. Table S9: The hypergraph of muscles and bones from the Hosford muscle tables used in the main text. Table S10: The hypergraph of muscles and bones from Grant's atlas used in the supplementary text.   Data for Figures: 2e 3a, 3b 4b, 4c, 4d S4a-h S5 S6a, S6b S7a, S7b S8 S9 S10 S11 S12

opencc-by-nc-sa-4.0Dec 2017View details →

Replication Package for "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems"

This is the replication package for the article "An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems" published in the Empirical Software Engineering journal. This package requires Python 3.5 and all the dependencies that are listed in "requirements.txt". The notebooks (in "notebooks" folder) should be opened and executed with Jupyter. The notebooks require the graphs (in "graphs" folder) to be computed first. To do so, execute "helpers.py" with Python. The graphs are built using the data provided by https://libraries.io under CC BY-SA https://creativecommons.org/licenses/by-sa/4.0/ Those data can be found in the "data" folder.

Data Set for the Journal Article "SCINE - Software for Chemical Interaction Networks"

This data archive contains all data and software described and used in the following publication: Thomas Weymuth, Jan P. Unsleber, Paul L. Türtscher, Miguel Steiner, Jan-Grimo Sobez, Charlotte H. Müller, Maximilian Mörchen, Veronika Klasovita, Stephanie A. Grimmel, Marco Eckhoff, Katja-Sophia Csizi, Francesco Bosia, Moritz Bensberg, Markus Reiher, "SCINE --- Software for Chemical Interaction Networks", in preparation. The directory structure is as follows: <ul> <li>software: contains all software used for the example exploration <ul> <li>requirements.txt: lists all Python packages needed to create the virtual environment with which the exploration has been carried out; the virtual environment was created with Python 3.6.8.</li> <li>start.py: script to initialize the database with the reactants</li> <li>step_1.py: script to create the first set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>step_2.py: script to create the second set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>puffin_1.3.0.sif: Singularity image containing a full Puffin instance (version 1.3.0) to execute all calculations of the exploration</li> <li>submit_container.sh: script to submit the Puffin image to the queueing system</li> </ul> </li> <li>data:  contains a complete dump of the database created during the example exploration; additionally, this directory contains a script called "import.sh" which can be used to reimport the data into a MongoDB instance</li> </ul>

Dataset for Spatial Variations in the Osteocyte Lacuno-canalicular Network Density and Analysis of the Connectomic Parameters

This dataset is a representative case of the loaded tibia of a C57BL/6 mouse at the mid-shaft. The image pixel size is 0.303 by 0.303 um, and the z-depth is 0.296 um.  To generate, analyse, and quantify the osteocyte lacuno-canalicular network, it requires 'Tool for Image and Network Analysis (TINA)' which can be acqruied from https://gitlab.mpikg.mpg.de/rummler/TINA.git. A demonstration has been included on using TINA.

Assessing Pairwise Ecological Association Inference using a novel Ecological Network Inference Simulation-Validation Framework - Data Repository

Accompanying data for manuscript entitled "A novel Network Inference Simulation-Validation Framework for Assessment of Ecological Network Inference Performance" whose submission is imminent.

A Global Gridded Municipal Water Withdrawal Estimation Method Using Aggregated Data and Artificial Neural Network

Global gridded municipal water withdrawal estimations for the following WST paper. Jiabao Yan, Shaofeng Jia; A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network. Water Science Technology, 2023; 87 (1): 251–274. <a href="https://doi.org/10.2166/wst.2022.399" target="_blank" rel="noopener">https://doi.org/10.2166/wst.2022.399</a> The representative year of the data is 2015, and the unit of the data is in millimeters (mm).

Seasonal carbon dioxide concentrations and fluxes throughout Denmark's stream network

<h2>About</h2> Repository containing code and data for the study: Title Seasonal carbon dioxide concentrations and fluxes throughout Denmark’s stream network Authors Kenneth Thorø Martinsen1, Kaj Sand-Jensen1*, Victor Bergmann1, Tobias Skjærlund1, Johan Emil Kjær1, Julian Koch2 Affiliations 1Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark 2Department of Hydrology, Geological Survey of Denmark and Greenland, Copenhagen, Denmark Corresponding author *Correspondence: <a href="mailto:ksandjensen@bio.ku.dk">ksandjensen@bio.ku.dk</a> GitHub repository <a href="https://github.com/KennethTM/denmark_stream_co2">https://github.com/KennethTM/denmark_stream_co2</a> <div> <h2>Contents</h2> <a href="https://github.com/KennethTM/denmark_stream_co2#contents"></a></div> The repository contains R and Python scripts for the analysis and figures. The <code>data/insitu_flux</code> directory contains the in-situ CO2 flux measurements in Excel format. The <code>data/modeling</code> directory contains artifacts from modeling. The <code>data/products</code> directory contains products resulting from the analysis (predicted CO2 concentrations and estimated CO2 fluxes throughout Denmark) in csv format.