Find research datasets worth reusing

Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems

Online appendix for the paper - "Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems", containing the power generators, gas producers and demand data as well as physical characteristics of the electrical transmission lines and gas pipelines. The empirically estimated forecast error covariance matrix used in the model is provided as well. The power systems and natural gas systems data is adapted from: C. Ordoudis, P. Pinson and J. M. González, "An integrated market for electricity and natural gas systems with stochastic power producers", European Journal of Operational Research, vol. 272, no. 2, pp. 642-654, 2019.

Individual-based plant-pollinator networks are structured by phenotypic and microsite plant traits

Dataset associated with the manuscript "Individual-based plant-pollinator networks are structured by phenotypic and microsite plant traits" (Arroyo-Correa et al. 2020), including plant-pollinator interactions, individual plant attributes and the plant polygon map created with drone flights.

Figure 4 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 4. Joint field research in Yemen: Project participants sample fish in Socotra Island for studies of connectivity among populations. Th e results are important for fisheries management (photo U. Zajonz).

Figure 3 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 3. Collection management and museum curatorship training workshop: Participants sampling biological specimens aboard the RV "Senckenberg" (photo N. Manasfi).

Figure 2 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 2. Red Sea coral reef: Th e Red Sea is the enclosed sea with the highest biodiversity on Earth (photo F. Krupp).

Figure 1 in The Middle Eastern Biodiversity Network: Generating and sharing knowledge for ecosystem management and conservation

Figure 1. Terrestrial biodiversity in the Middle East is strongly influenced by seasonality: Desert area in northern Saudi Arabia after the winter rainfall (photo F. Krupp).

Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"

DATA FILES from the study below: <a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a> Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a> *.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a> Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning

A quantum network node with crossed optical fibre cavities

Data published in " A quantum network node with crossed optical fibre cavities " Nature Physics (2020)

opencc-by-3.0Feb 2017View details →

Fig. 2. Minimum spanning network depicting relationships among 16S in Which frog's legs do froggies eat? The use of DNA barcoding for identification of deep frozen frog legs (Dicroglossidae, Amphibia) commercialized in France

Fig. 2. Minimum spanning network depicting relationships among 16S haplotypes of Fejervarya cancrivora (Gravenhorst, 1829). The size of each circle is proportional to the haplotype frequency and the lengths of the connecting lines are proportional to the number of mutations. Colors refer to distinct regions (Indonesia: Java, Sumatra, Bali, Kalimantan, Bangka; Malaysia; Taiwan) and commercialized frogs of unknown origin are in black.

opencc-by-4.0May 2020View details →

Functional Brain Networks of Picture Naming in Broca's Aphasia and Healthy Controls

The data were from the picture-naming task with MEG scanning.  Brain networks of ".net" format:  Each network has 776 regions that were derived by subdividing USCBrain Atlas. Phase-locking values (PLV) were calculated between the 776 regions in a gamma-band of 30-45Hz.  PLVs were normalized (z-PLVs) by using the mean and standard deviation of the 200-ms pre-stimulus baseline. The edges were weighted by z-PLVs.  Vector files of ".vec" format:  Each file contains activations, viz., amplitude, of regions. There are two types of amplitude. One is the estimated electric density in a physical unit of picoampere. Another is the z-score of amplitude calculated through comparison with a baseline of –200 ms. We provided both the group-averaged files (named as b999 for the Broca group and c999 for the control group) and the individuals' files (b1 to b5 for the Broca's aphasia and c1 to c5 for the control persons). We also provided two ".clu" files. One is the partition file of eight functional modules in two hemispheres. Another is the partition file of two hemispheres.  The .net, .vec., and .clu files can be imported to Pajek for further interpretations and visualizations.

opencc-by-4.0May 2020View details →

Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources - supporting information

Supporting tables S2 - S5 of "Network analysis reflects the trophic relationship between microbial colonizers and deadwood resources". The file Table_Legends_S2-S5.txt contains all legends, as given below: Table S2: Module-associated trees and OTUs, their relative abundances and identities for the fungal sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples . Table S3: Module-associated trees and OTUs, their relative abundances and identities for the fungal heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples . Table S4: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic sapwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples . Table S5: Module-associated trees and OTUs, their relative abundances and identities for the prokaryotic heartwood network; module – name of the module, present – proportion of network version that the OTU is present in (1 = 1000/1000), inBestModule – proportion of the network versions where the OTU is associated with the respective module, percInBestModule – proportion of the network versions where the OTU is associated with the respective module provided that the OTU is part of the network (= inBestModule/present), meanAbundanceModuleSamples – mean relative abundance of the OTU in all samples belonging to the module, meanAbundanceOtherSamples – mean relative abundance of the OTU in all other samples, relAbundanceModuleSamplesVsOthers – meanAbundanceModuleSamples / meanAbundanceOtherSamples .

opencc-zeroJun 2020View details →

Species occurrence and occupancy in protected areas of the Natura2000 network in Belgium

Context Invasive alien species have been pointed out as an important driver of biodiversity loss. Many policy responses are being developed to address this threat. Protected areas often represent and preserve hotspots of biological diversity and ensure the maintenance of ecosystem services crucial to human livelihoods. The impact of biological invasions can be particularly severe in protected areas and their occurrence and impact in such areas is an important element of the risk they pose. To address this, there is a need for data on the occurrence and extent of alien species invasions in protected areas. Description This dataset contains species occurrence and occupancy in protected areas of the Natura2000 network in Belgium (Special Conservation Areas sensu Habitat Directive and Special Protection Areas sensu Bird Directive). The dataset was generated using the <a href="https://doi.org/10.5281/zenodo.3637911">Belgian occurrence cube at species level</a> and the <a href="https://doi.org/10.5281/zenodo.3635510">Belgian occurrence cube for non-native taxa</a> (both containing GBIF data aggregated using Oldoni et al. 2020), the 1x1km <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">EEA reference grid</a> and the <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-spatial-data/natura-2000-shapefile-1">Natura2000 protected areas shapefiles</a> from the European Environment Agency. Data are grouped by protected area (<code>SITECODE</code>), year (<code>year</code>) and (infra)species (<code>taxonKey</code>, <code>speciesKey</code>). For each group, it provides the number of occurrences found in GBIF (<code>n</code>), the area of occupancy (<code>aoo</code>: number of 1 km2 squares), the coverage (<code>coverage</code>: % of 1 km2 squares), the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (<code>min_coord_uncertainty</code>), and the alien status (<code>is_alien</code>) based on the <a href="https://doi.org/10.15468/xoidmd">Global Register of Introduced and Invasive Species - Belgium</a>. For infraspecific taxa in the latter, the <a href="https://github.com/trias-project/indicators/blob/00e1ae72df3fb98b2a215c3af8769e53fbcd0182/reference/species_of_infraspecific_alien_taxa.tsv">alien status of the species</a> is looked up and included. The dataset is built on open science principles and intended to be completely reproducible: <ul> <li>The input data are publicly available on Zenodo, with the download DOIs listed in the related identifiers of this dataset package.</li> <li>The <a href="https://trias-project.github.io/indicators/10_species_observations_occupancy_in_protected_areas.html">code</a> to process the data is publicly available and documented on GitHub.</li> </ul> Files <ul> <li>protected_areas_species_occurrence.csv: number of occurrences (<code>n</code>), area of occupancy (<code>aoo</code>) and <code>coverage</code> of taxa (<code>taxonKey</code>) in Natura2000 areas of Belgium (<code>SITECODE</code>). Other columns included: <code>speciesKey</code> (for species is <code>speciesKey</code> = <code>taxonKey</code>), <code>SITETYPE</code> containing the site type of the Natura2000 area (one of <code>A</code>, <code>B</code> or <code>C</code>), <code>min_coord_uncertainty</code> with the lowest coordinate uncertainty in meters, <code>is_alien</code> containing the alien status (<code>TRUE</code> or <code>FALSE</code>) and <code>remarks</code> containing, if present, the infraspecific alien taxa whose occurrences contribute to the calculated <code>aoo</code> (only for species).</li> <li>protected_areas_species_info.csv: taxonomic information of taxa in <code>protected_areas_species_occurrence.csv</code> as retrieved from <a href="https://www.gbif.org/dataset/d7dddbf4-2cf0-4f39-9b2a-bb099caae36c">GBIF Backbone Taxonomy</a>. Columns: <code>taxonKey</code>, <code>speciesKey</code>, <code>scientificName</code>, <code>kingdom</code>, <code>phylum</code>, <code>order</code>, <code>class</code>, <code>genus</code>, <code>family</code>, <code>species</code>, <code>rank</code> and <code>includes</code>. The latter contains the infraspecific taxa and synonyms whose occurrences contribute to the number of occurrences at species level.</li> <li>protected_areas_metadata.csv: protected area information for areas included in <code>protected_areas_species_occurrence.csv</code>. Columns: <code>SITECODE</code> as in <code>protected_areas_species_occurrence.csv</code> (<code>BE*******</code>), <code>SITENAME</code> containing the name of the protected area, <code>SITETYPE</code> as in <code>protected_areas_species_occurrence.csv</code>, <code>flanders</code>, <code>wallonia</code> and <code>brussels</code> containing whether the area is situated respectively in Flanders, Wallonia or Brussels-Capital Region (<code>TRUE</code> or <code>FALSE</code>). Field codes are in line with <a href="https://www.eea.europa.eu/data-and-maps/data/natura-11/natura-2000-tabular-data-12-tables">EEA element definitions</a> for Natura 2000 sites.</li> </ul> Potential use of the dataset Currently, there is no comprehensive reporting system for invasive alien species in Natura 2000 sites. This dataset provides a baseline as to which species occur in which protected area. We envisage this dataset can be an interesting starting point for various types of analyses on alien species in protected areas in Belgium, but that it can also be used in complement to other data on alien species in protected areas to study more general patterns. Some examples of research questions: <ul> <li>Which protected areas are most invaded by alien species</li> <li>Which alien species are most distributed in protected areas and which traits do they have</li> <li>How does the proportion of alien species in protected areas change in time</li> <li>How does the occurrence/occupancy of alien species in protected areas match lists of regulated species (e.g. Union List, EPPO lists)</li> <li>To what extent can the network of protected areas contribute to providing safe refuge to native species from the impacts of invasive alien species</li> <li>How widespread are the impacts of certain alien species on protected areas</li> </ul> <h2>Acknowledgements</h2> This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS).

opencc-by-4.0Jun 2020View details →

Experiments for "Automata Theoretic Approach to Verification of MPLS Networks under Link Failures"

Prerequisites: <ol> <li>A 2020 linux distribution on an x86_64 platform</li> <li>python3 installation with os, sys, json and statistics packages installed for computation of timing statistics</li> <li>pdflatex for visualization of reachability matrices</li> </ol> Contents: <ul> <li>The bin folder contains the pre-compiled binaries for our tool and the backend verifier for pushdown-systems, moped.</li> <li>The sources folder contains a snapshot of source-code used for producing the tool binary, namely <ul> <li>AalWiNes, also found at https://github.com/DEIS-Tools/AalWiNes in release v0.92.J (337f1e4c44f8bce2060897b88e706f7ceca38319)</li> <li>PDAAAL, used for pushdown-system manipulation, also found at https://github.com/DEIS-Tools/PDAAAL in release v0.2.2.J (ed1e7fbb4cacd6ffb240a514cfe8472f83d91f60)</li> </ul> </li> <li>The nested folder contains scripts and models for reproducing the results of Table IV</li> <li>The nn-net folder contains scripts and models for constructing reachability-matrices (Table V to Table XII) and operator specific queries</li> <li>Within the nn-net folder, you also find a mapping from alphabetical naming of NORDUnet routers in the paper to numericals (nn-net/index-alpha-map.txt)</li> </ul> P-Rex vs HSA (Table IV) We here re-use the HSA-timings computed in <a href="https://doi.org/10.1145/3281411.3281432">https://doi.org/10.1145/3281411.3281432</a> The timings for our tool can be obtained by the following commands <pre><code class="language-bash">cd nested ./compute_nested.sh | grep "#"</code></pre> which should compute a sequence of lines in the terminal equal to <pre><code class="language-bash">### Running N0 with aalwines ### Memory: 27064 Kb Time 0.02 seconds ### Running N1 with aalwines ### Memory: 37404 Kb Time 0.03 seconds ### Running N2 with aalwines ### Memory: 47704 Kb Time 0.03 seconds ### Running N3 with aalwines ### Memory: 57784 Kb Time 0.04 seconds ### Running N4 with aalwines ### Memory: 68368 Kb Time 0.04 seconds ### Running N5 with aalwines ### Memory: 78468 Kb Time 0.05 seconds ### Running N6 with aalwines ### Memory: 88980 Kb Time 0.06 seconds </code></pre> Operator Queries The experiments on the queries of the operators can be computed via the commands <pre><code class="language-bash">cd nn-net ./compute_operator.sh </code></pre> which should complete less than 10 minutes. The traces and timings are outputted directly to the terminal. Reachability Matrices (Table V to Table XII) This package folder comes pre-populated with the raw computation results. Notice that the compute_grid.sh command will invalidate these results. To compute the full set of reachability tables, you can use the following commands <pre><code class="language-bash">cd nn-net ./make_queries.sh ./compute_grid.sh</code></pre> Notice, however, that this computation in total takes more than two days to complete on a single core. To reduce the size of the experiment, clear the contents of the query sub-folder an modify the make_queries.sh file by commenting out unwanted queries and parameter-combinations. To obtain a matrix, the command <pre><code class="language-bash">./make_grid.sh aalwines IP IP 0 > ip_ip.tex pdflatex ip_ip.tex</code></pre> which generated the IP IP matrix with 0 failures. For the second argument, the following values are supported IP, MPLS while the third argument can also attain the value of ANY. The fourth argument gives the number of link failures. Timing information for the reachability-matrices can be obtained by <pre><code class="language-bash">python3 ./timing_stats.py aalwines 3 IP_IP_UNDER_0</code></pre> where the third argument determines the reduction-type used, and the fourth argument gives the specific query and engine mode used. In the given example we attain the statistics for reduction-mode 3 for the IP-IP matrix using under-approximation on zero link-failures. The pre-reduction and post-reduction elements of the output give the size of the constructed pushdown system before (and after) reduction respectively.

opencc-by-4.0Jul 2020View details →

Harry Potter's social network

Literary works can be analysed in the framework of network theories, as proposed in 2011 by the Stanford Literary Laboratory in some of its experiments. In fact, the plot of a play or a novel can be displayed as a network of interacting characters, where the timeline of the plot is projected on a planar graph. This approach can help in highlighting some features of the literary work. Here in the image, it is shown  the Harry Potter’s social network, as we can obtain from “Harry Potter and the Philosopher’s Stone” (or Sorcerer’s Stone), the J.K. Rowling's debut novel, published on 26 June 1997. A small part of the network was already proposed in [1]. In the same article, we detailed in the appendix the  edges between vertices, that is the links among characters. [1] Amelia Carolina Sparavigna, On Social Networks in Plays and Novels, International Journal of Sciences 10(2013):20-25 DOI: 10.18483/ijSci.312

Data of Cerrado´s Tree Crown Networks

Information about the architecture of the woody crown obtained through representations in the form of a network (graphs). The essential components of these networks are nodes and connectors. Decomposition, topology, and properties calculated for analyzing the strategies of crown airspace acquisition in any environment. The networks represented in a two-dimensional space follow the general laws of network theory, but with specific meanings for the crown architecture. Thus, a dataset generated and included information about five individuals from fifteen tree species growing under the natural conditions of the Cerrado vegetation. We presented the types and the total number of nodes. Initial node (IN) was the node that starts the network, regular node (RN) was the vast majority of nodes with three connectors. Emission node (EN) showed four connectors, and the final node (FN) was the last in leafy axes. There are data about the distances between the initial and final nodes (IN-IF), and initial and emission nodes (IN-IE). Decomposition and topological combinations permitted to disclose the properties (navigability, vulnerability, symmetry, and complexity).  The data presented can be used by researchers from all over the world in works that investigate the behavior of networks in biological systems, in addition to the specific applications of studies of functional ecology and plant ecophysiology. We obtained the data directly from a skeletonized representation of the woody crown in a two-dimensional space in the form of a drawing. Subsequently, the nodes counted, and their proportions (decomposition), the distances between the different types of nodes (topology), and the values of network properties (the combination of decomposition and topology) obtained.

opencc-by-4.0Sep 2020View details →

Generalized linear model with elastic net regularization and convolutional neural network for evaluating Aphanomyces root rot severity in lentil

Red-Green-Blue (RGB) imaging was used to evaluate Aphanomyces root rot in 547 lentil accessions and lines. The root images were pre-processed by removing image background. This dataset (6,460 root images) was used to build two machine learning models — generalized linear model with elastic net regularization and convolutional neural network— to classify root images into three classes. Details about the methodology and results are described in Marzougui et al. (2020, Plant Phenomics). The excel file includes Aphanomyces root rot disease visual scores (Root_Rating), unique identifier for each lentil accession/line (Lentil_ID), unique identifier for each experiment (Experiment), and unique identifier for each image (Lab_ID).

opencc-by-4.0Sep 2020View details →

Glassware images and code samples for training and identification of glassware by neural networks

These images were used to perform an image identification exercise with first-year students in the author's Introduction to Scientific Computing course. The zip file also includes sample Mathematica notebooks that were used to perform the training and data analysis of the neural network's performance. The corresponding publication in the Journal of Computational Science Education can be found here <a href="https://doi.org/10.22369/issn.2153-4136/12/1/2">https://doi.org/10.22369/issn.2153-4136/12/1/2</a>

opencc-by-4.0Sep 2020View details →

Evidence of a coordinated network amplifying inauthentic narratives in the 2020 election

On 15 September 2020, the Washington Post published an article by Isaac Stanley-Becker titled “<a href="https://www.washingtonpost.com/politics/turning-point-teens-disinformation-trump/2020/09/15/c84091ae-f20a-11ea-b796-2dd09962649c_story.html">Pro-Trump youth group enlists teens in secretive campaign likened to a ‘troll farm,’ prompting rebuke by Facebook and Twitter</a>.” The article reported on a preliminary analysis we conducted at the request of The Post. Here we would like to share the dataset used in our analysis with the research community. Our Observatory on Social Media at Indiana University has been studying <a href="https://theconversation.com/misinformation-on-social-media-can-technology-save-us-69264">social media manipulation</a> and <a href="https://theconversation.com/misinformation-and-biases-infect-social-media-both-intentionally-and-accidentally-97148">online misinformation</a> for over ten years. We uncovered the first known instances of <a href="http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2850">astroturf campaigns</a>, <a href="https://cacm.acm.org/magazines/2016/7/204021-the-rise-of-social-bots/fulltext">social bots</a>, and <a href="http://doi.org/10.1126/science.aao2998">fake news</a> websites during the 2010 US midterm election, long before these phenomena became widely known in 2016. We develop public, state-of-the art network and data science methods and <a href="https://osome.iu.edu/tools/">tools</a>, such as <a href="https://botometer.osome.iu.edu/">Botometer</a>, <a href="https://hoaxy.iuni.iu.edu/">Hoaxy</a>, and <a href="https://osome.iu.edu/tools/botslayer/">BotSlayer</a>, to help researchers, journalists, and civil society organizations study coordinated inauthentic campaigns. So when Stanley-Becker contacted us about accounts posting identical political content on Twitter, we were happy to apply our <a href="https://arxiv.org/abs/2001.05658">analytical framework</a> to map out what was going on.

opencc-by-4.0Oct 2020View details →

Analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Networks

This multi-spectral satellite image data set is associated with our recent work on analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Neural Networks. The images were extracted using an automated Python script from Google Maps Static API, based on sample locations in four East African capital cities namely Kampala, Nairobi, Dar es Salaam, and Kigali. Other data sets associated with this work, that is, ESRI shapefiles for administrative level 1 and OpenStreetMap data for the named cities may be downloaded directly from the respective URLs provided in the manuscript.