Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behaviour

This work presents an analysis based on training AI models directly on devices to make personalized predictions tailored to individual usage patterns, ensuring that each user benefits from a personalized approach to battery management. By integrating these AI-based insights, mobile devices can proactively manage power consumption, improving battery performance and user satisfaction. This personalized, intelligent approach to battery management represents a significant advance in optimizing device efficiency and addresses the growing demand for longer-lasting mobile technology.

opencc-by-4.0Jan 2021View details →

OH-IIUNAM network precipitation data

NetCDF files for all 51 working stations of the OH-IIUNAM (Observatorio Hidrológico del Instituto de Ingeniería de la Universidad Nacional Autónoma de México), a network of 39 laser disdrometers and 12 weighting rain gauges deployed across Mexico City. The OH-IIUNAM network relies on the OTT Parsivel2 disdrometer and Pluvio2 L weighing gauge instruments, which use a laser diode to produce a horizontal sheet of light to detect the number and size of hydrometeors and, a weight-based sensor to measure liquid and solid precipitation, respectively.

opencc-by-4.0Jul 2021View details →

Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios.

DOCUMENTATION - Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for various single- and multi-capillary occlusion scenarios. Correspondence: fschmid@ethz.ch (Franca Schmid, ORCID: <a href="https://orcid.org/0000-0002-0689-9366">0000-0002-0689-9366</a>) 1. Related references: The data set is published in context with the manuscript:  [1] The severity of microstrokes depends on local vascular topology and baseline perfusion.  F Schmid, G Conti, P Jenny and B Weber. eLife. 2021. Doi: 10.7554/eLife.60208 The bi-phasic blood flow simulations have been performed in realistic microvascular networks (MVNs) from the mouse somatosensory cortex first published in: [2] The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow. P Blinder, PS Tsai, JP Kaufhold, PM Knutsen, H Suhl and D Kleinfeld. Nature Neuroscience. 2013. Doi: 10.1038/nn.3426 The bi-phasic blood flow model for realistic MVNs has first been published in: [3] Depth-dependent flow and pressure characteristics in cortical microvascular networks. F Schmid, PS Tsai, D Kleinfeld, P Jenny and B Weber. PLoS Computational Biology. 2017. Doi: 10.1371/journal.pcbi.1005392 For further information on how to perform bi-phasic blood flow simulation, please contact the corresponding authors of [1] or [3]. 2. Requirements (software): All simulations and analyses have been performed in Python 2.7. To execute the analysis script the following python libraries need to be installed: cPickle, python-igraph, pandas, seaborn, scipy. The individual analyses script can then be executed by in Python (e.g. “python plot_Figure3.py”). 3. Content: All folders are stored as compressed archives (*.tar.bz2). On unix-based system the folders can be unpacked by: "tar –jxf  ARCHIVE_NAME" 3a. Time-averaged simulation results (python dictionaries stored as python 2.7 pickle files): SimulationResults_Baseline.tar.bz2: Folders: MVN1, MVN2 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl (generated from prepare_Figure4.py), data_spatial_distribution_AVfactor.pkl (generated from plot_Figure4.py) SimulationResults_SingleCapillaryOcclusions.tar.bz2: Folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out, 2-in-2-out_high, 2-in-2-out_AL1, 2-in-2-out_AL2, 2-in-2-out_AL3, 2-in-2-out_AL4, 2-in-2-out_AL5, 2-in-2-out_closeToDA, 2-in-2-out_farFromDA Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl (only folders: 1-in-1-out, 1-in-2-out, 2-in-1-out, 2-in-2-out) SimulationResults_MultiCapillaryOcclusions.tar.bz2: Folders: vesselsOccluded_1, vesselsOccluded_3, vesselsOccluded_5, vesselsOccluded_7, vesselsOccluded_9 Content: verticesDict_baseline.pkl, edgesDict_baseline.pkl, pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl 3b. Analysis scripts (python 2.7 scripts in folder Analyses_Scripts): For details on the figure content see [1]. The verticesDict* and the edgesDict* are converted into graph structure (python-igraph) for all analyses. The functionality of python-igraph is used heavily throughout the various analyses. helperFunctions.py: various functions used by the other analysis scripts plot_Figure1_and_Figure1-supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_1/*, Supplementary_Figures/Figure_1-supplement_1_a-d/* plot_Figure2_and_Figure2_supplement_1_a-d.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2-out/verticesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/edgesDict.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1-out/verticesDict.pkl Output: Figures/Figure_2/*, Supplementary_Figures/Figure_2-supplement_1_a-d/* plot_Figure3.py: Input: SimulationResults_Baseline/MVN1/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN1/verticesDict_baseline.pkl, SimulationResults_MultiCapillaryOcclusion/*/edgesDict.pkl, SimulationResults_MultiCapillaryOcclusion/*/verticesDict.pkl  Output: Figures/Figure_3/* prepare_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl, SimulationResults_Baseline/MVN*/verticesDict_baseline.pkl, Output: SimulationResults_Baseline/MVN*/pathsDict_allPaths_from_DA_to_AV_mainBranch.pkl plot_Figure4.py (long execution time!): Input: SimulationResults_Baseline/MVN*/* Output: SimulationResults_Baseline/MVN*/edgesDict_baseline.pkl (attribute Lfactor_median added), SimulationResults_Baseline/MVN*/data_spatial_distribution_AVfactor.pkl, Figures/Figure_4/* plot_Figure5.py: Input: SimulationResults_Baseline/MVN*/*  Output: Figures/Figure_5/* plot_Figure6.py: Input: SimulationResults_Baseline/MVN1/*, SimulationResults_SingleCapillaryOcclusion/2-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/2-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-2- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl, SimulationResults_SingleCapillaryOcclusion/1-in-1- out/pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased.pkl Output: Figures/Figure_6/* 4. Attributes stored in python dictionaries: 4a. Baseline: verticesDict: contains all relevant information and data stored at vertices. <ul> <li>index: index of vertex</li> <li>pressure: time averaged pressure at vertex [mmHg]</li> <li>inflowE: list of edges delivering blood to the vertex (inflows of the vertex)</li> <li>outflowE: list of edges removing blood from the vertex (outflows of the vertex)</li> <li>coords: coordinates of the vertex [µm]</li> <li>pBC: pressure boundary conditions [mmHg], None for internal vertices</li> <li>corticalDepth: depth from cortical surface [µm]</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> </ul> edgesDict: contains all relevant information and data stored at edges. <ul> <li>diameter: effective vessel diameter [µm]. See [3] for details.</li> <li>htd: time averaged discharge hematocrit [-]. </li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>mainAV: identifier for ascending venule (AV) main brain. 1: is AV main brain, 0: no AV main branch</li> <li>mainDA: identifier for descending arteriole (DA) main brain. 1: is DA main brain, 0: no DA main branch</li> <li>flow: time averaged flow rate [µm3 ms-1]</li> <li>length: tortuous vessel length [µm] See [1] and [3] for details.</li> <li>tissueVolume: topological tissue volume supplied by vessel [µm3]. See [1] for details.</li> <li>nkind: identifier for the vessel type. 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending vein, 4: capillary</li> <li>edgesFulfillingSelection: identifier if vessels fulfils selection criteria to qualify for analysis. 1: vessel included for analysis, 0: vessel not included for analysis. Details on the selection criteria are provided in [1].</li> <li>htt: time averaged tube hematocrit [-]</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate.</li> <li>sign: sign describing the flow direction in the vessel. +: flow direction from source (vertex with lower index) to target (vertex with higher index), -: flow direction from target to source vertex. Based on time averaged pressure values.</li> <li>points: list of tortuous vessel coordinates of the edge [µm]. Starting at the source vertex. Ending at the target vertex.</li> <li>Lfactor_median: AV-factor of the vessel. None if no AV-factor can be assigned. See [1] for details. Attribute added by plot_Figure4.py</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch: contains all flow path from DA main brain to AV main branch. For details see [1]. <ul> <li>startPoint: list of vertex indices of the end point of the DA</li> <li>endPoint: list of vertex indices of the end point of the AV</li> <li>allPaths: list of lists of vertex indices describing all paths between a the associated startPoint and endPoint.</li> </ul> data_spatial_distribution_AVfactor: contains information on the spatial distribution of venule-sided capillaries (AV-factor  > 0.5). For details see [1]. <ul> <li>edges_L_mean_50um: list of all edges for which the average AV-factor in an analysis sphere of 50 µm has been computed.</li> <li>resulting_L_mean_50um: average AV-factor for an analysis sphere for 50 µm (see Figure4/AV_factor_delta_analysisSphere50_MVN*.pkl)</li> <li>shortest_distance_to_closest_vessel: list of shortest distances to any vessel for all discretization points along all venule sided capillaries.</li> <li>shortest_distance_to_Lfactor_lt_05: list of shortest distances to an arteriole-sided capillary (AV-factor < 0.5) for all discretization points along all venule sided capillaries.</li> </ul> 4b. Occlusion scenarios (both single- and multi-capillary occlusions): verticesDict: <ul> <li>index: index of vertex</li> <li>coords: coordinates of the vertex [µm]</li> <li>pressure_strokeIndex_n: time averaged pressure at vertex [mmHg] for the simulation where edge n has been occluded. For details see [1].</li> </ul> edgesDict: <ul> <li>htd_strokeIndex_n: time averaged discharge hematocrit [-] for the simulation where edge n has been occluded. For details see [1]. </li> <li>flow: time averaged flow rate [µm3 ms-1] for the simulation where edge n has been occluded. For details see [1].</li> <li>RBCflux: time averaged RBC flux [RBC/s] computed from the discharge hematocrit and the flow rate for the simulation where edge n has been occluded. For details see [1].</li> <li>htt: time averaged tube hematocrit [-] for the simulation where edge n has been occluded. For details see [1].</li> <li>connectivity: tuple of vertex indices which are connected by the edge.</li> <li>diameter_strokeIndex_n: effective vessel diameter [µm] for the simulation where edge n has been occluded (only given for multi-capillary occlusions).</li> </ul> pathsDict_allPaths_from_DA_to_AV_mainBranch_vertexBased: contains all flow path from DA main brain to AV main branch (unique vertex sequences). For details see helperFunctions.py --> function convert_pathsDict_to_unique_vertexSequence. <ul> <li>startPoint_strokeIndex_n: list of vertex indices of the end point of the DA for the simulation where edge n has been occluded.</li> <li>endpoint_strokeIndex_n: list of vertex indices of the end point of the AV for the simulation where edge n has been occluded.</li> <li>allPaths_strokeIndex_n: list of lists of vertex indices describing all paths between a the associated startPoint and endpoint for the simulation where edge n has been occluded.</li> </ul>

opencc-by-4.0Jul 2021View details →

Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts

Research Artefact: What network simulator questions do users ask? a large-scale study of stack overflow posts This is a research artefact for the paper: What network simulator questions do users ask? a large-scale study of stack overflow posts. This artefact is a repository consisting of the collected dataset including 2,322 network-simulator-related Stack Overflow questions. This artefact aims to enable researchers to replicate our dataset of the paper and reuse the dataset for further research.

Data from: Integrating pheromonal and spatial information in the amygdalo-hippocampal network. Villafranca-Faus et al. 2021

The local field potential (LFP) of the dorsal hippocampus (CA1) and cortical amygdala (PMCo) of mice, under head-fix recording and inmersed on a virtual environtmernt. Paper Abstract:  Vomeronasal information is critical in mice for territorial behavior. Consequently, learning the territorial spatial structure should incorporate the vomeronasal signals indicating individual identity into the hippocampal cognitive map. In this work we show in mice that navigating a virtual environment induces synchronic activity, with causality in both directionalities, between the vomeronasal amygdala and the dorsal CA1 of the hippocampus in the theta frequency range. The detection of urine stimuli induces synaptic plasticity in the vomeronasal pathway and the dorsal hippocampus, even in animals with experimentally induced anosmia. In the dorsal hippocampus, this plasticity is associated with the overexpression of pAKT and pGSK3β. An amygdalo-entorhino-hippocampal circuit likely underlies this effect of pheromonal information on hippocampal learning. This circuit likely constitutes the neural substrate of territorial behavior in mice, and it allows the integration of social and spatial information.

WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks

Dataset release for our IEEE Symposium on Security and Privacy 2022 paper entitled "WtaGraph: Web Tracking and Advertising Detection using Graph Neural Networks" These files are pretty self-explanatory: there are node/edge features of a specified (full graph or random 5K graph). To use these files, make sure to check out GitHub <a href="https://github.com/jun521ju/IEEE_SP_2022_WtaGraph">here</a>. Basically, you need put each of these files in a correct folder as detailed on GitHub.

Human contact network analytics and COVID-19 hospital incidence in France

This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper: Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029 See methods in the article for detailed descriptions and the data curation process.   1) cov_mob_tst_national.csv contains national-level data   The columns comprise: incid_hosp: hospital admission incidence                          incid_rea: ICU admission incidence incid_dc: hospital death incidence  incid_rad: incidence of those returned home within_departement_colocation_X%: X%-quantile of colocation probabilities with départements between_departement_colocation_X%: X%-quantile of colocation probabilities between départements fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département null_links_X%: X%-quantile of null links across départements clustering_X%: X%-quantile of clustering coefficients across départements ricci_X%: X%-quantile of curvature across départements ricci_min_X%: X%-quantile of minimum curvature across départements ricci_mean_X%: X%-quantile of average curvature across départements ricci_max_X%: X%-quantile of maximum curvature across départements strength_X%: X%-quantile of network strengths across départements betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests retail_and_recreation_percent_change_from_baseline: Google Mobility Reports grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports parks_percent_change_from_baseline:  Google Mobility Reports         transit_stations_percent_change_from_baseline: Google Mobility Reports workplaces_percent_change_from_baseline: Google Mobility Reports residential_percent_change_from_baseline: Google Mobility Reports mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements     2) cov_mob_dep.csv contains département-level data The columns comprise: dep: département code incid_hosp: hospital admission incidence                          incid_rea: ICU admission incidence incid_dc: hospital death incidence  incid_rad: incidence of those returned home week: week (matched to colocation data recording usually on Tuesdays) dep_name: name of the département null_links: number of null links betweenness_centrality: betweenness centrality clustering: clustering coefficient strength: network strength ricci_mean: minimum curvature among all edges incident to a département ricci_min: mean curvature across all edges incident to a département              ricci_X%: X%-quantile curvature among all edges incident to a département fb_population: number of facebook users                        facebook_colocation_within_dep: colocation probability within département fb_population_coverage: ratio of fb_population over census population in département facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département min_temperature: minimum daily temperature averaged over the week max_temperature: maximum daily temperature averaged over the week mean_temperature: mean daily temperature averaged over the week incid_hosp_Y: incidence of hospital admission from Ynd most colocated département incid_rea_Y: incidence of ICU admission from Ynd most colocated département incid_dc_Y: incidence of hospital deaths from Ynd most colocated département incid_rad_Y: incidence of returned home from Ynd most colocated département

author map and author network- FMVZ-UADY (1970-jun2021)

Data for VOSviewer regarding author map and author network- publications indexed in scopus from FMVZ-UADY (1970-jun2021)

Diversity in citations to a single study: Supplementary data set for citation context network analysis

Introduction This document describes the data set used for all analyses in 'Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited' accepted for publication in Quantitative Science Studies [1]. Data Collection The data collection procedure has been fully described [1]. Concisely, the data set contains bibliometric data collected from Web of Science Core Collection via the University of Edinburgh’s Library subscription concerning all papers that cited a cohort study, Paul et al. [2], in the period <1985. This includes a full list of citing papers, and the citations between these papers. Additionally, it includes textual passages (citation contexts) from 343 citing papers, which were manually recovered from the full-text documents accessible via the University of Edinburgh’s Library subscription. These data have been cleaned, converted into network readable datasets, and are coded into particular classifications reflecting content, which are described fully in the supplied code book and within the manuscript [1].  Data description All relevant data can be found in the attached file 'Supplementary_material_Leng_QSS_2021.xlsx', which contains the following five workbooks: <ul> <li>“Overview” includes a list of the content of the workbooks.</li> <li>“Code Book” contains the coding rules and definitions used for the classification of findings and paper titles.</li> <li>“Node attribute list” includes a workbook containing all node attributes for the citation network, which includes Paul et al. [2] and its citing papers as of 1984. Highlighted in yellow at the bottom of this workbook is two papers that were discarded due to duplication - remove these if analysing this dataset in a network analysis. The columns refer to:</li> </ul> <ol> <li>Id, the node identifier</li> <li>Label, the formal citation of the paper to which data within this row corresponds. Citation is in the following format: last name of first author, year of publication, journal of publication, volume number, start page, and DOI (if available).  </li> <li>Title, the paper title for the paper in question.</li> <li>Publication_year, the year of publication.</li> <li>Document_type, the document type (e.g. review, article)</li> <li>WoS_ID, the paper’s unique Web of Science accession number.</li> <li>Citation_context, a column specifying whether citation context data is available from that paper</li> <li>Explanans, the title explanans terms for that paper;</li> <li>Explanandum, the explanandum terms for that paper.</li> <li>Combined_Title_Classification, the combined terms used for fig 2 of the published manuscript.</li> <li>Serum_cholesterol_(SC), a column identifying papers that cited the serum cholesterol findings.</li> <li>Blood_Pressure_(BP), a column identifying papers that cited the blood pressure findings.</li> <li>Coffee_(C), a column identifying papers that cited the coffee findings.</li> <li>Diet_(D), a column identifying papers that cited the dietary findings.</li> <li>Smoking_(S), a column identifying papers that cited the smoking findings.</li> <li>Alcohol_(A), a column identifying papers that cited the alcohol findings.</li> <li>Physical_Activity_(PA), a column identifying papers that cited the physical activity findings.</li> <li>Body_Fatness (BF), a column identifying papers that cited the body fatness findings.</li> <li>Indegree, the number of within network citations to that paper, calculated for the network shown in Fig 4 of the manuscript.</li> <li>Outdegree, the number of within network references of that paper as calculated for the network in Fig 4.</li> <li>Main_component, a column specifying whether a node is contained in the largest weakly connect component as shown in Fig 4 of the manuscript.</li> <li>Cluster, provides the cluster membership number as discussed within the manuscript (Fig 5).</li> </ol> <ul> <li>“Edge list” includes a workbook including the edges for the network. The columns refer to:</li> </ul> <ol> <li>Source, contains the node identifier of the citing paper.</li> <li>Target, contains the node identifier of the cited paper.</li> </ol> <ul> <li>“Citation context classification” includes a workbook containing the WoS accession number for the paper analysed, and any finding category discussed in that paper established via context analysis (see the code book for definitions). The columns refer to:</li> </ul> <ol> <li>Id, the node identifier</li> <li>Finding_Class, the findings discussed from Paul et al. within the body of the citing paper.  </li> </ol> <ul> <li> “Citation context data” includes a workbook containing the WoS accession number for papers in which citation context data was available, the citation context passages, the reference number or format of Paul et al. within the citing paper, and the finding categories discussed in those contexts (see code book for definitions). The columns refer to:</li> </ul> <ol> <li>Id, the node identifier</li> <li>Citation_context, the passage copied from the full text of the citing paper containing discussion of the findings of Paul et al.</li> <li>Reference_in_citing_article, the reference number or format of Paul et al. within the citing paper.</li> <li>Finding_class, the findings discussed from Paul et al. within the body of the citing paper. </li> </ol> Software recommended for analysis For the analyses performed within the manuscript, Gephi version 0.9.2 was used [3], and both the edge and node lists are in a format that is easily read into this software. The Sci2 tool was used to parse data initially [4]. Notes <ol> <li>Leng, R. I. (Forthcoming). Diversity in citations to a single study: A citation context network analysis of how evidence from a prospective cohort study was cited. Quantitative Science Studies.</li> <li>Paul, O., Lepper, M. H., Phelan, W. H., Dupertuis, G. W., Macmillan, A., McKean, H., et al. (1963). A longitudinal study of coronary heart disease. Circulation, 28, 20-31. <a href="https://doi.org/10.1161/01.cir.28.1.20">https://doi.org/10.1161/01.cir.28.1.20</a>.</li> <li>Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media.</li> <li>Sci2 Team. (2009). Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. Stable URL: <a href="https://sci2.cns.iu.edu">https://sci2.cns.iu.edu</a></li> </ol>

opencc-by-4.0Dec 2018View details →

Fig. 2a-f in Wild bees (Anthophila) of Porto Santo (Madeira Archipelago) and their habitats: species diversity, distribution patterns and bee-plant network *

Fig. 2a-f: a) Andrena dourada, female; b) Andrena portosanctana, female collecting pollen on Cakile maritima; c) Lasioglossum wollastoni, female in front of nesting site; d) Osmia latreillei iberoafricana, male visiting Cakile maritima; e) Amegilla quadrifasciata maderae, female collecting pollen on Echium portosanctensis, f) Bombus terrestris lusitanicus, worker, collecting pollen on Echium portosanctensis. Photos: A. Kratochwil (a, b, e), A. Schwabe (c, d, f).

opencc-by-4.0Dec 2018View details →

Fig. 1 in Wild bees (Anthophila) of Porto Santo (Madeira Archipelago) and their habitats: species diversity, distribution patterns and bee-plant network *

Fig. 1: Aspects from some of our sampling sites and their surroundings in March after an extreme dry winter and a wet winter: Left: March 2012 (November 2011–March 2012, no precipitation); right: March 2017 (October 2016–March 2017, 301 mm precipitation); a, b: sand beach with Vila Baleira in the centre; c, d: Pico Juliana and mainly fallow land; e, f: southern-exposed extensively grazed dry grassland; view from Capela da Graça (in the background right: Pico do Facho with Pinus plantations). Photos: A. Schwabe.

PhAQ: Intuitive Physics Question Answering forMulti-Modal Neural Network training

Dataset for the paper: PhAQ: Intuitive Physics Question Answering forMulti-Modal Neural Network training The two proposed splits are given in zip files.

Fig. 11 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia

Fig. 11. Haplotype network of Ngirhaphium sivasothii. Thailand: Krabi, Phangnga, Satun; Singapore: Sarimbun, Pulau Tekong, Pulau Ubin, Labrador, Semakau Island, and Sungei Buloh.

Fig. 10 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia

Fig. 10. Haplotype network of Ngirhaphium murphyi. Singapore: Pulau Ubin, Mandai Sungei Buloh; Thailand: Satun and Krabi.

Fig. 12 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia

Fig. 12. Compilation of male terminalia of Ngirhaphium in lateral view. A, Ngirhaphium sivasothii left side with ventral surstylus removed; cerci dorsally; B, Ngirhaphium murphyi left side with ventral surstylus removed; cerci and dorsal surstyli dorsally; C, Ngirhaphium caeruleum left side with ventral surstylus removed; cerci and dorsal surstyli dorsally; D, Ngirhaphium meieri, new species left side with ventral surstylus removed; E, Ngirhaphium chutamasae left side with ventral surstylus removed; F, Ngirhaphium thaicum, new species right side. Scale = 0.1 mm.

Fig. 8 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia

Fig. 8. Ngirhaphium meieri, new species, holotype male terminalia: A, Lateral view of genital capsule with left ventral surstylus removed; B, Cerci dorsally; C. Left ventral surstylus; D, Ventral view of genital capsule. Abbreviations: ae = aedeagus; c = cercus; ds = dorsal surstylus; hy = hypandrium; sp = sperm pump; vs = ventral surstylus. Scale = 0.1 mm.

Fig. 5 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia

Fig. 5. Ngirhaphium thaicum, new species male terminalia (27_009) A, epandrium left side; B, cerci dorsal view; C, left surstylus inside view. Scale = 0.1 mm.

Fig. 2 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia

Fig. 2. Ngirhaphium caeruleum-complex. View of inside of the dorsal surstylus of the left side and the fused dorsal and ventral surstyli of the right side. Scale = 0.1mm. A, green caeruleum from Tutong, Brunei (ZRCBDP0066395) at 1.6 % from the Semakau population; B, green caeruleum from Pulau Tekong, Singapore (ZRCBDP0001462) at 0.6% from the Semakau population; C, green caeruleum from Pulau Ubin, Singapore (ZRC_BDP_0084430) at 0.6% from the Semakau population; D, blue caeruleum from the type locality on Semakau Island, Singapore (ZRCBDP0118762); E, green thaicum, new species from Surat Thani, Thailand (24-018) at 4.2% from the Semakau population; F, green thaicum, new species from Cambodia (JP3C_Ngi-cambodiensis_Misc002) within the variability of the southern Thailand populations.

Fig. 1 in NGS-barcodes, haplotype networks combined to external morphology help to identify new species in the mangrove genus Ngirhaphium Evenhuis & Grootaert, 2002 (Diptera: Dolichopodidae: Rhaphiinae) in Southeast Asia

Fig. 1. Haplotype network of the Ngirhaphium caeruleum-complex. Brunei: Berambang, Tutong, Labu; Singapore: Pulau Ubin, Pulau Tekong, Semakau Island; Thailand: Chumphon, Surat Thani, and Cambodia.