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DATA SET: Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units
<p>This repository contains the data sets of the article:</p> <p>Mesquida, J., Caballer, A., Cortese, L. <em>et al.</em> Peripheral microcirculatory alterations are associated with the severity of acute respiratory distress syndrome in COVID-19 patients admitted to intermediate respiratory and intensive care units. <em>Crit Care</em> <strong>25, </strong>381 (2021). https://doi.org/10.1186/s13054-021-03803-2</p>
An experimental data set on the thermal and fluid dynamic performance of double skin facades (DSFs) subjected to various controlled boundary conditions through the use of a climate simulator facility
<p>Double skin facades (DSFs) are building envelope systems defined by complex phenomena and non-linear-processes that make characterizing their performance a non-trivial task. In an effort to enable the scientific community to access experimental data for further analysis or model validation purposes, we release together with the open-access paper entitled “<strong><em>Laboratory testbed and methods for flexible characterization of the thermal and fluid dynamic behavior of double skin facades” (</em></strong><a href="https://doi.org/10.1016/j.buildenv.2021.108700"><strong><em>https://doi.org/10.1016/j.buildenv.2021.108700</em></strong></a><strong><em>)</em></strong>, a set of experimental data collected during tests carried out with the use of the newly developed testbed. The data contains the results of a series of tests where various configurations of a full-scale DSF mock-up that have been subjected to different boundary conditions replicated in a climate simulator. The database contains a guide in the form of the file ‘Guide.pdf’, which explains how to read data, presents a schematic drawing of sensor layout, and provides more information on sensors’ positions. Further information on the original aims of the experiments, methods, and other data can be found in the article mentioned above, which becomes an essential tool to understand how to read and interpret the experimental data fully. The following collection of experimental data are provided:</p> <ul> <li>32 steady-state measurements where the following factors were changed: ventilation mode (indoor and outdoor air curtain), solar irradiance (0, 400, 600, and 800 Wm<sup>-2</sup>), outdoor chamber temperature (10, 20, 30, and 40 ℃), cavity depth (20, 30, 40 and 60 cm) and venetian blinds position (no blinds, closed blinds, θ=45 º, and open blinds) [file names: ‘Taguchi_4Lx4F_L16_I-I.csv’ and ‘Taguchi 4Lx4F_L16_O-O.csv’],</li> <li>Dynamic profile measurements corresponding to a typical hot summer day [Dynamic_profile_measurements.csv] and</li> <li>Calibration data [Callibration.csv].</li> </ul> <p>Any inquires on the experimental data<em> can be sent </em>to: aleksandar.jankovic@ntnu.no</p>
An experimental data set for the analysis of the thermophysical behavior of a single-story naturally ventilated double-skin façade (DSF) under summer boundary conditions
<p>Double-skin facades (DSFs) are adaptive building envelope elements that offer the possibility to dynamically interact with the heat and mass flow between indoor and outdoor environments. Though designed to provide better performance compared to more conventional envelope solutions, these façade systems may, in some cases, underperform and lead to an increase in energy use or in thermal discomfort if not properly designed and operated. One of the known problems is the risk of overheating, in hot periods, in the ventilated cavity. In order to analyze this effect, we have systematically investigated the performance of a single-story, naturally ventilated DSF. The DSF is operated in the so-called outdoor air curtain mode and has venetian blinds installed in the 20 mm deep ventilated cavity. Tests were carried out under a steady-state regime corresponding to relevant summertime conditions. In an effort to enable the scientific community to access experimental data to analyze this problem further or for model validation purposes, we released together with the open-access paper entitled "<strong>Characterization of a naturally ventilated double-skin façade through the design of experiments (DOE) methodology in a controlled environment</strong>," the entire set of experimental data collected during the tests. The data set contains the results of a series of experimental runs where different configurations of the DSF, as detailed below, have been subjected to various boundary conditions through a climate simulator facility equipped with a solar simulator device. The database is supported by a guide ("Guide.pdf"), where further explanations about how to read data and schematic drawings of the sensor layout are provided. Additional information about the original aims of the experiments, the detailed methods, and other data processing procedures can be found in the article mentioned above. The collection of experimental tests in this data set covers:</p> <ul> <li>49 steady-state measurements where the following factors were changed using different experimental designs: solar irradiance (0, 350, and 700 Wm<sup>-2</sup>), outdoor chamber temperature (15, 25, and 35 ℃), opening size (7, 21, and 42 dm<sup>2</sup>), and venetian blinds angle (closed blinds θ=0 º, θ=45 º, and open blinds θ=90 º) [file name: "Complete_data.csv"],</li> </ul> <p>Any inquiries about the experimental data can be sent to: <a href="mailto:aleksandar.jankovic@ntnu.no">aleksandar.jankovic@ntnu.no</a></p> <p>The activities presented in this paper were carried out within the research project "REsponsive, INtegrated, VENTilated - REINVENT – windows," supported by the Research Council of Norway through the research grant 262198, and the partners SINTEF, Hydro Extruded Solutions, Politecnico di Torino and Aalto University.</p>
Data sets for Span-level SNR Regression in EONs
<ul> <li><strong>DS1: </strong>the symbol rate is fixed and equals 64 Gbaud, and the channel loading factor is selected from [25 − 100];</li> <li><strong>DS2:</strong> the symbol rate and channel occupancy status is randomly selected (uniformly distributed) from {32, 64, 96 (GBaud) and {0, 1}, respectively;</li> <li> <strong>DS3</strong>: both symbol rate and the channel loading factor are fixed and equal to 64 GBaud and 25%, respectively.</li> </ul>
Citation network data sets for 'Oxytocin – a social peptide? Deconstructing the evidence'
<p><strong>Introduction</strong></p> <p>This note describes the data sets used for all analyses contained in the manuscript 'Oxytocin - a social peptide?’<a href="#_ftn1">[1]</a> </p> <p><strong>Data Collection</strong></p> <p>The datasets described here were originally retrieved from Web of Science (WoS) Core Collection via the University of Edinburgh’s library subscription <a href="#_ftn2">[2]</a>. The aim of the original study for which these data were gathered was to survey peer-reviewed primary studies on oxytocin and social behaviour. To capture relevant papers, we used the following query:</p> <p><em>TI = (“oxytocin” OR “pitocin” OR “syntocinon”) AND TS = (“social*” OR “pro$social” OR “anti$social”)</em></p> <p>The final search was performed on the 13 September 2021. This returned a total of 2,747 records, of which 2,049 were classified by WoS as ‘articles’. Given our interest in primary studies <em>only</em> – articles reporting original data – we excluded all other document types. We further excluded all articles sub-classified as ‘book chapters’ or as ‘proceeding papers’ in order to limit our analysis to primary studies published in peer-reviewed academic journals. This reduced the set to 1,977 articles. All of these were published in the English language, and no further language refinements were unnecessary.</p> <p>All available metadata on these 1,977 articles was exported as plain text ‘flat’ format files in four batches, which we later merged together via Notepad++. Upon manually examination, we discovered examples of papers classified as ‘articles’ by WoS that were, in fact, reviews. To further filter our results, we searched all available PMIDs in PubMed (1,903 had associated PMIDs - ~96% of set). We then filtered results to identify all records classified as ‘review’, ‘systematic review’, or ‘meta-analysis’, identifying 75 records <a href="#_ftn3">[3]</a> (thus, ~4% of records classified by WoS were classified as reviews in PubMed). After examining a sample and agreeing with the PubMed classification, these were removed these from our dataset - leaving a total of 1,902 articles.</p> <p>From these data, we constructed two datasets via parsing out relevant reference data via the Sci2 Tool <a href="#_ftn4">[4]</a>. First, we constructed a ‘node-attribute-list’ by first linking unique reference strings (‘Cite Me As’ column in WoS data files) to unique identifiers, we then parsed into this dataset information on the identify of a paper, including the title of the article, all authors, journal publication, year of publication, total citations as recorded from WoS, and WoS accession number. Second, we constructed an ‘edge-list’ that records the citations from a <em>citing paper</em> in the ‘Source’ column and identifies the <em>cited paper</em> in the ‘Target’ column, using the unique identifies as described previously to link these data to the node-attribute-list.</p> <p>We then constructed a network in which papers are nodes, and citation links between nodes are directed edges between nodes. We used Gephi Version 0.9.2 <a href="#_ftn5">[5]</a> to manually clean these data by merging duplicate references that are caused by different reference formats or by referencing errors. To do this, we needed to retain both all retrieved records (1,902) as well as including <em>all</em> of their references to papers whether these were included in our original search or not. In total, this produced a network of 46,633 nodes (unique reference strings) and 112,520 edges (citation links). Thus, the average reference list size of these articles is ~59 references. The mean indegree (within network citations) is 2.4 (median is 1) for the entire network reflecting a great diversity in referencing choices among our 1,902 articles.</p> <p>After merging duplicates, we then restricted the network to include <em>only</em> articles fully retrieved (1,902), and retrained <em>only</em> those that were connected together by citations links in a large interconnected network (i.e. the largest component). In total, 1,892 (99.5%) of our initial set were connected together via citation links, meaning a total of ten papers were removed from the following analysis – and these were neither connected to the largest component, nor did they form connections with one another (i.e. these were ‘isolates’).</p> <p>This left us with a network of 1,892 nodes connected together by 26,019 edges. <strong><em>It is this network that is described by the ‘node-attribute-list’ and ‘edge-list’ provided here</em></strong>. This network has a mean in-degree of 13.76 (median in-degree of 4). By restricting our analysis in this way, we lose 44,741 unique references (96%) and 86,501 citations (77%) from the full network, but retain a set of articles tightly knitted together, all of which have been fully retrieved due to possessing certain terms related to oxytocin AND social behaviour in their title, abstract, or associated keywords.</p> <p>Before moving on, we calculated indegree for all nodes in this network – this counts the number of citations to a given paper from other papers within this network – and have included this in the <em>node-attribute-list</em>. We further clustered this network via modularity maximisation via the Leiden algorithm <a href="#_ftn6">[6]</a>. We set the algorithm to resolution 1, and allowed the algorithm to run over 100 iterations and 100 restarts. This gave <em>Q</em>=0.43 and identified seven clusters, which we describe in detail within the body of the paper. We have included cluster membership as an attribute in the node-attribute-list.</p> <p>For additional analysis, we also analysed the full reference list data to examine the most commonly cited references between 2016 and 2021 - the results of this are described in OTSOC_Cited_2016-2021.csv. This takes the reference lists of all retrieved papers within the network and examines their full reference lists (including references to other papers not contained within the network). These data were cleaned by matching DOIs and manual cleansing. </p> <p><strong>Data description</strong></p> <p>We include here two network datasets: (i) ‘OTSOC-node-attribute-list.csv’ consists of the attributes of 1,892 primary articles retrieved from WoS that include terms indicating a focus on oxytocin and social behaviour; (ii) ‘OTSOC-edge-list.csv’ records the citations between these papers. Together, these can be imported into a range of different software for network analysis; however, we have formatted these for ease of upload into Gephi 0.9.2. Finally, we include (iii) 'OTSOC_Cited_2016-2021' that lists all papers cited by >10 papers in the OTSOC network following any analysis of the bibliographies of retrieved papers. Below, we detail their contents:</p> <p><strong>1. ‘OTSOC-node-attribute-list.csv’</strong> is a comma-separate values file that contains all node attributes for the citation network (n=1,892) analysed in the paper. The columns refer to:</p> <p><em>Id</em>, the unique identifier</p> <p><em>Label</em>, the reference string of the paper to which the attributes in this row correspond. This is taken from the ‘Cite Me As’ column from the original WoS download. The reference string is in the following format: last name of first author, publication year, journal, volume, start page, and DOI (if available). </p> <p><em>Wos_id</em>, unique Web of Science (WoS) accession number. These can be used to query WoS to find further data on all papers via the ‘UT= ’ field tag.</p> <p><em>Title</em>, paper title.</p> <p><em>Authors</em>, all named authors.</p> <p><em>Journal, </em>journal of publication.</p> <p><em>Pub_year</em>, year of publication.</p> <p><em>Wos_citations</em>, total number of citations recorded by WoS Core Collection to a given paper as of 13 September 2021</p> <p><em>Indegree</em>, the number of within network citations to a given paper, calculated for the network shown in Figure 1 of the manuscript.</p> <p><em>Cluster</em>, provides the cluster membership number as discussed within the manuscript (Figure 1). This was established via modularity maximisation via the Leiden algorithm (Res 1; Q=0.43|7 clusters)</p> <p><strong>2. ‘OTSOC-edge -list.csv’</strong> is a comma-separated values file that contains all citation links between the 1,892 articles (n=26,019). The columns refer to:</p> <p><em>Source</em>, the unique identifier of the citing paper.</p> <p><em>Target, </em>the unique identifier of the cited paper.</p> <p><em>Type, </em>edges are ‘Directed’, and this column tells Gephi to regard all edges as such.</p> <p><em>Syr_date, </em>this contains the date of publication of the citing paper.</p> <p><em>Tyr_date, </em>this contains the date of publication of the cited paper.</p> <p><strong>3. 'OTSOC_Cited_2016-2021.csv'</strong> is a comma-separated values file that contain citations to all cited references that were cited by at least 10 of the retrieved papers within the OTSOC network published from 2016 onwards. The columns refer to: </p> <p><em>Reference, </em>the cited reference string extracted from the bibliographies of retrieved papers.</p> <p><em>Publication year, </em>the publication year of the cited reference.</p> <p><em>DOI</em>, the DOI of the cited reference. </p> <p><em>indegree_2016, </em>the total number of citations to a cited reference from papers published in 2016 and contained within the OTSOC network. </p> <p><em>indegree_2017, </em>the total number of citations to a cited reference from papers published in 2017 and contained within the OTSOC network. </p> <p><em>indegree_2018, </em>the total number of citations to a cited reference from papers published in 2018 and contained within the OTSOC network. </p> <p><em>indegree_2019, </em>the total number of citations to a cited reference from papers published in 2019 and contained within the OTSOC network. </p> <p><em>indegree_2020, </em>the total number of citations to a cited reference from papers published in 2020 and contained within the OTSOC network. </p> <p><em>indegree_2021, </em>the total number of citations to a cited reference from papers published in 2021 and contained within the OTSOC network. </p> <p><em>total indegree 2016-21</em>, the total number of citation to a cited reference from papers published between 2016-2021 and contained within the OTSOC network. </p> <p><strong>Software recommended for analysis</strong></p> <p>Gephi version 0.9.2 was used for the visualisations within the manuscript, and both files can be read and into Gephi without modification.</p> <p><strong>Notes</strong></p> <p><a href="#_ftnref1">[1]</a> Leng, G., Leng, R. I., Ludwig, M. (Submitted). Oxytocin – a social peptide? Deconstructing the evidence.</p> <p><a href="#_ftnref2">[2]</a> Edinburgh University’s subscription to Web of Science covers the following databases: (i) Science Citation Index Expanded, 1900-present; (ii) Social Sciences Citation Index, 1900-present; (iii) Arts & Humanities Citation Index, 1975-present; (iv) Conference Proceedings Citation Index- Science, 1990-present; (v) Conference Proceedings Citation Index- Social Science & Humanities, 1990-present; (vi) Book Citation Index– Science, 2005-present; (vii) Book Citation Index– Social Sciences & Humanities, 2005-present; (viii) Emerging Sources Citation Index, 2015-present.</p> <p><a href="#_ftnref3">[3]</a> For those interested, the following PMIDs were identified as ‘articles’ by WoS, but as ‘reviews’ by PubMed: ‘34502097’ ‘33400920’ ‘32060678’ ‘31925983’ ‘31734142’ ‘30496762’ ‘30253045’ ‘29660735’ ‘29518698’ ‘29065361’ ‘29048602’ ‘28867943’ ‘28586471’ ‘28301323’ ‘27974283’ ‘27626613’ ‘27603523’ ‘27603327’ ‘27513442’ ‘27273834’ ‘27071789’ ‘26940141’ ‘26932552’ ‘26895254’ ‘26869847’ ‘26788924’ ‘26581735’ ‘26548910’ ‘26317636’ ‘26121678’ ‘26094200’ ‘25997760’ ‘25631363’ ‘25526824’ ‘25446893’ ‘25153535’ ‘25092245’ ‘25086828’ ‘24946432’ ‘24637261’ ‘24588761’ ‘24508579’ ‘24486356’ ‘24462936’ ‘24239932’ ‘24239931’ ‘24231551’ ‘24216134’ ‘23955310’ ‘23856187’ ‘23686025’ ‘23589638’ ‘23575742’ ‘23469841’ ‘23055480’ ‘22981649’ ‘22406388’ ‘22373652’ ‘22141469’ ‘21960250’ ‘21881219’ ‘21802859’ ‘21714746’ ‘21618004’ ‘21150165’ ‘20435805’ ‘20173685’ ‘19840865’ ‘19546570’ ‘19309413’ ‘15288368’ ‘12359512’ ‘9401603’ ‘9213136’ ‘7630585’</p> <p><a href="#_ftnref4">[4]</a> 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></p> <p><a href="#_ftnref5">[5]</a> 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. Gephi is available via <a href="https://gephi.org/">https://gephi.org/</a></p> <p><a href="#_ftnref6">[6]</a> Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: guaranteeing well-connected communities. Scientific reports, 9(1), 5233. <a href="https://doi.org/10.1038/s41598-019-41695-z">https://doi.org/10.1038/s41598-019-41695-z</a></p>
A Global Data Set of Present-Day Oceanic Crustal Age and Seafloor Spreading Parameters
<p>Datasets of present-day oceanic crustal age and seafloor spreading parameters from Seton et al. (2020).</p> <p>This dataset contains:</p> <ul> <li>Animations: animations of the present-day age grid and seafloor spreading parameters in both low and high resolution</li> <li>Feature Data: GPlates compatible files (*.gpml and *.rot) consistent with and used to create this dataset. Preferred magnetic anomaly picks are also included.</li> <li>Grids: Gridded datasets (netCDF-4 and netCDF-3) of present-day age, rate, asymmetry, direction, obliquity, confidence, and age misfit (in v1.1 only) in 6 minute resolution. Age grids are also provided in 1 and 2 minute resolution as netCDFs, and as 6 minute xyz files.</li> <li>Images: Images of the present-day age grid and seafloor spreading parameters</li> <li>Workflows: the latest workflow to create the present-day age grid can be found on GitHub: https://github.com/EarthByte/presentday-agegridding </li> </ul> <p>These files can also be downloaded from the EarthByte website <a href="https://earthbyte.org/webdav/ftp/earthbyte/agegrid/2020/">here</a>, and the global plate motion model can be found online <a href="https://www.earthbyte.org/webdav/ftp/Data_Collections/Muller_etal_ 2019_Tectonics">here</a>.</p> <p><strong>Please cite the dataset as:</strong><br> Seton, M., Müller, R. D., Zahirovic, S., Williams, S., Wright, N. M., Cannon, J., et al. (2020). A global data set of present‐day oceanic crustal age and seafloor spreading parameters. <em>Geochemistry, Geophysics, Geosystems</em>, 21, e2020GC009214. https://doi.org/10.1029/2020GC009214</p>
EXTREMA: Ballistic capture sets at Mars over an Earth–Mars synodic period from January 1, 2030, to February 20, 2032
<p>EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture, the focus of this work. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019.</p> <p>In Pillar 3 it is investigated how a spacecraft can attain ballistic capture in autonomy. Ballistic capture is an event that occurs in extremely-rare occasions, and requires acquiring a proper state (position, velocity) far away from the target planet [1]. Massive numerical simulations are required to find the specific conditions that support capture [2]. On average, 1 out of 10,000 conditions explored by the algorithm grants capture [3]. The union of these points defines the capture set, which in turn is used to find the capture corridors: these are streams of orbits that can be targeted far away from the planet and that guarantee ballistic capture.</p> <p>The data set contains the initial conditions of weakly-stable, unstable, crash, moon-crash, and capture sets at Mars with initial epochs uniformly distributed from 01 JAN 2030 12:00:00.000 (UTC) to 20 FEB 2032 10:32:39.144 (UTC), covering a complete Earth–Mars synodic period of approximately 780 days. The grid of initial conditions is built to maximize the capture ratio for Mars (see Figure 10 in [3]). Initial conditions are propagated in high-fidelity. The equations of motion of the restricted n-body problem are considered. The gravitational attractions of the Sun, Mercury, Venus, Earth (B*), Mars (central body), Jupiter (B), Saturn (B), Uranus (B), and Neptune (B) are taken into account. Additionally, solar radiation pressure, Mars’ non-spherical gravity, and relativistic corrections [4] (Schwarzschild solution, geodesic precession, and Lense-Thirring precession) are also included in the model.</p> <p>For additional information about the EXTREMA project visit the page <a href="http://extrema.polimi.it">extrema.polimi.it</a>.</p> <p><strong>References</strong><br> [1] F. Topputo and E. Belbruno,'Earth–Mars transfers with ballistic capture', Celestial Mechanics and Dynamical Astronomy, Vol. 121, No. 4, 2015, pp. 329–346. DOI: <a href="http://doi.org/10.1007/s10569-015-9605-8">10.1007/s10569-015-9605-8</a>.<br> [2] F. Topputo and E. Belbruno, 'Computation of weak stability boundaries: Sun–Jupiter system', Celestial Mechanics and Dynamical Astronomy, Vol. 105, No. 1-3, 2009, pp. 3–17. DOI: <a href="http://doi.org/10.1007/s10569-009-9222-5">10.1007/s10569-009-9222-5</a><br> [3] Z.-F. Luo and F. Topputo, 'Analysis of ballistic capture in Sun–planet models', Advances in Space Research, Vol. 56, No. 6, 2015, pp. 1030–1041. DOI: <a href="http://doi.org/10.1016/j.asr.2015.05.042">10.1016/j.asr.2015.05.042</a><br> [4] C. Huang, J. C. Ries, B. D. Tapley, and M. M.Watkins, 'Relativistic effects for near-earth satellite orbit determination', Celestial Mechanics and Dynamical Astronomy, Vol. 48, No. 2, 1990, pp. 167–185. DOI: <a href="http://doi.org/10.1007/BF00049512">10.1007/BF00049512</a></p> <p>* Here B stands for barycenter.</p>
New Zealand Hikurangi Margin temperature and pressure loggers - data sets.
<p>Description of data</p> <p>In 2019, 14 RBR data loggers were purchased as a supplement to an existing NSF grant, and were tested, calibrated, and deployed on the Hikurangi Subduction Zone margin as part of the Evan Solomon (UW) deployment of flow meters at this site. The purpose of the Solomon pore fluid analysis program was to determine the impact of Slow Slip Tectonic Events on near-surface fluid flow for this active margin. The purpose of the supplemental RBR loggers, attached directly to fluid flow meters, was to add the key components of pressure and temperature to this data set and specifically to identify any sediment slope failures and turbidity flows that occur during the two year deployment period.</p> <p>By May 6, 2021, all of the Solomon flow meters have been recovered with the attached UW supplemental RBR loggers. The loggers were be subjected to a final ice bath calibration test prior to data-download in New Zealand and all T/P data will be returned to UW via internet. The physical data loggers were then be returned to UW via air freight.</p> <p>The data in these Zenodo data archive files include all of the pre-cruise and post-cruise calibrations fro the RBR T/P loggers, the raw and calibrated data, and the locations of the stations. Also included are some infrastructure information including a multi-channel seismic profile showing that locations on the Hikurangi margin with known fault zones, and a published catalogue of earthquakes that occurred during the logger deployment period.</p> <p> </p>
Hyperspectral X-ray CT datasets for a set of multiply-stained mouse limb specimens
<p><strong>General Data description:</strong></p> <p>The following are hyperspectral (energy-resolved) X-ray CT datasets for a set of mouse limb specimens, each stained with multiple contrast agents. All scans were acquired with an energy-sensitive HEXITEC detector in the Henry Moseley X-ray Imaging Facility at The University of Manchester.</p> <p>The following data contains all the files necessary for reconstruction of each dataset. The biological specimens were produced as they each contain multiple contrast agents, with distinct spectral markers. When measured by an energy-sensitive detector, each contrast agent may be identified and segmented individually following spectral analysis. A mouse hindlimb was double-stained with elemental iodine and BaSO<sub>4</sub>. A mouse forelimb was triple-stained with I<sub>2</sub>KI, BaSO<sub>4 </sub>and PTA.</p> <p><strong>File descriptions:</strong></p> <p>Contained are two HDF5 (.h5) data files, as well as two (.txt) metadata files and a MATLAB (.mat) file.</p> <p>Hindlimb_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the double-stained hindlimb.</p> <p>Forelimb_scan_parameters.txt provides the full sample and detector geometry of the scan acquisition for the triple-stained forelimb.</p> <p>DS_Mouse_hindlimb_sinogram.h5 contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the double-stained hindlimb specimen. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied.</p> <p>TS_Mouse_forelimb_sinogram.h5 contains the full 4D sinogram constructed following flatfield normalisation of the raw projection data for the triple-stained forelimb specimen. The 4D array contains the total number of energy channels acquired during scanning, followed by vertical and horizontal pixel number, and finally total projections angles acquired. In addition, a ring artefact reduction filter was applied.</p> <p>Energy_axis.mat provides a direct conversion between the energy channels, and the energies (in keV) that they correspond to, following a calibration procedure prior to scanning.</p>
Architectural Languages for the Microservices Architecture: A systematic mapping study [Data set]
<p>This repository contains all artifacts related to the study: Architectural Languages for the Microservices Architecture: A systematic mapping study.</p>
Performance Criteria and Example Parameter Sets Comparing Different Variants of the Ensemble Kalman Filter as Applied to Volcanology
<p>This dataset contains the results of various Ensemble Kalman Filter (EnKF) inversions in which synthetic GNSS and InSAR observations from an inflating magma system are assimilated into numerical models of rock deformation around a pressurized ellipsoidal magma reservoir. Each inversion uses a different variant of the EnKF, with changes to workflow meta-parameters such as the number of ensemble members or the particular update algorithm used. In particular, each filter variant is evaluated by comparing the final output model to the original synthetic model. The specific performance criteria used include (1) the root mean square error (RMSE) between the model predictions and the assimilated observations, as well as normalized misfit terms measuring the filter's ability to resolve (2) reservoir wall tensile stress, (3) easily-observable unique parameters such as reservoir position and aspect ratio, and (4) difficult-to-derive non-unique parameters such as the specific size and internal pressure of the reservoir. The assimilated data include two different scenarios, one in which inflation is caused by pressurization and another in which it is driven by a lateral reservoir expansion. Both datasets are tested with each EnKF variant. Finally, we include example matrices from within an EnKF update step to demonstrate inter-parameter correlations that develop during the assimilation and how they can be mitigated through randomization.</p>
Humans display a reduced set of consistent behavioral phenotypes in dyadic games
<p>Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental research has adopted a game theoretical perspective, generating valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals’ behavior when facing different situations and to define a comprehensive classification of the strategies underlying the observed behaviors. We present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals’ actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (envious, optimist, pessimist, and trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts toward the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, which could be applied to simulating societies, policy-making scenario building, and even a variety of business applications.</p> <p> </p> <p>The data from the "dr Brain" experiment is organized in two separated files: drbrain_users.csv<br> and drbrain_decisions.csv.</p> <p><br> 1.) drbrain_users.csv contains information about the participants of the experiment (or users).<br> There is one row per user, with the following information about each one of them:</p> <p>User_ID: unique ID number to identify the user.<br> Age: user's age<br> Gender: user's gender<br> Experiment_number: Number of the experiment the user participated in. For organizational reasons, our research actually was made 45 experiments (or replicas) run over a period of 2 days, each one run with differnt users. A user was only allowed to participate in one experiment. Each experiment included between 10-25 users typically, and they played around 13-18 game rounds, typically. Each round and each couple of users played in different games (that is, different values of S, Sucker's payoff, and T, Temptation to defect, while the values of P=5 , Punishment, and R=10, Reward, were always fixed).<br> Earnings: number of points the user obtained in total, over all rounds.</p> <p><br> 2.) drbrain_decisions.csv contains the information of the all game rounds for all experiments and all users.<br> User_ID: unique ID number to identify the user. <br> Experiment_number: Number of the experiment the user participated in.<br> Round_number: Number of the round within a given experiment.<br> S: Value for the "Sucker's payoff" in the game of that round.<br> T: Value for the "Temptation to defect" in the game of that round. <br> Game: Name of the game corresponding to those values of S and T for that round<br> Action: Action chosen by the user (C: cooperate, D: defect)<br> Opponent_ID: ID number of the user's opponent in that round. <br> Opponent_Action: Action (C or D) chosen by the user's opponent in that round.</p> <p>--------</p> <p>For more details, see our research article:</p> <p>Humans display a reduced set of consistent behavioral phenotypes in dyadic games.<br> Julia Poncela-Casasnovas, Mario Gutiérrez-Roig, Carlos Gracia-Lázaro, Julian Vicens, Jesús Gómez-Gardeñes, Josep Perelló, Yamir Moreno, Jordi Duch and Angel Sánchez.<br> Science Advances Vol. 2, no. 8, 2016.<br> DOI: 10.1126/sciadv.1600451<br> http://advances.sciencemag.org/content/2/8/e1600451</p>
Simulation of Fire Propagation in Cable Tray Installations - Data Set
<p>This repository contains simulation data used for a conference paper at ISTSS 2018, with the title "<a href="https://www.researchgate.net/publication/323999819_Simulation_of_Fire_Propagation_in_Cable_Tray_Installations_for_Particle_Accelerator_Facility_Tunnels?ev=auth_pub">Simulation of Fire Propagation in Cable Tray Installations for Particle Accelerator Facility Tunnels</a>". Furthermore, the plots are provided, including the Python 3 scripts to create the plots, used in this paper.</p> <p>With the Fire Dynamics Simulator FDS, in the versions 6.3.2 and 6.5.3, simulations of cable fire tests have been performed. Experimental data from micro-combustion calorimetry and Cone Calorimeter tests were used to calibrate a material parameter set, aming to predict the fire spread in a cable tray installation. The simulations are based on experimental data from the CHRISTIFIRE Phase 1 campaign.</p> <p>The authors want to thank Kevin B. McGrattan for providing access to the CHRISTIFIRE data.</p> <p> </p> <p><strong>Some remarks on the usage:</strong></p> <p>Unfortunately, for some unclear reason, Zenodo does right now not support the creation of folders within the repository. In an effort to maintain the structure of the data, ZIP archives have been created. Note that specifically the MT-3 simulations are quite large and take about 3.5 GB of space after extraction.</p> <p>It is only necessary to reproduce the file structure, if the user wants to utilise the provided Python scripts "as is". It is, of course, also possible to adjust the file pathes in the scripts to the users desire.</p> <p>To recreate the original file structure, one needs to copy all files of this repository into a single directory. The ZIP archives are sub-directories within that basic directory. The names of the archives contain the information of how the sub-directory structure looks like. Triple underscores '___' are placeholders indicating the file path, thus need basically changed to '/'. For example, the ZIP archive ''Cone___CoarseCone___ArrCHRISTIFIRE.zip' translates to the path 'Cone\CoarseCone\ArrCHRISTIFIRE\'.</p> <p> </p>
Run-to-failure data set of ball bearings subjected to time-varying load and speed conditions
<p>This data set consist of experimental data collected during 17 run-to-failure experiments on ball bearings subjected to time-varying load and speed conditions. No defect was initiated in the bearings before the experiments. A detailed description file is enclosed.</p> <p>Version 2024-04-02: All experiments B01 through B17 are uploaded. Furthermore, Figure 1 of the description file has been updated.</p> <p>Please also cite our paper, when using this data set: Javanmardi, A., Aimiyekagbon, O. K., Bender, A. ., Kimotho, J. K., Sextro, W., & Hüllermeier, E. (2024). Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying Conditions. <em>PHM Society European Conference</em>, <em>8</em>(1), 9. https://doi.org/10.36001/phme.2024.v8i1.4101</p>
Data Set from GREAT Case Study 1
<p><span>This open data set contains the raw CSV files that were generated in the first GREAT case study, carried out in collaboration with UNDP and using the infrastructure developed by PlanetPlay. A </span><span>merged file is also provided that may be more convenient for some users who wish to carry out their own analysis.</span></p>
A large data-set of CASP protein refinement simulations for machine-learning
<p>The uploaded trajectory data originates from our own laboratory's refinement method in CASP11 and CASP12 for which the reference crystal structure is available in the PDB. In total the trajectory data consists of 904 trajectories with 3419 ns cumulative simulation time and 1,709,704 snapshots with a delta t =2 ps from 42 different protein systems.</p> <p><strong>File Overview</strong></p> <ul> <li><strong>trajectory_data_pdbs.tar.gz :</strong> contains the PDB files of the different trajectories as well as the starting model and reference crystal structure for each target</li> <li><strong>casp_normalized_all_data_final.csv.gz : </strong>contains the trajectory features calculated for each snapshot from the trajectory PDBs</li> <li><strong>cv_folds.csv : </strong>contains the 7 fold cross-validation assignment used to assess the performance of the model<br> </li> </ul> <p> </p> <p> </p> <p> </p>
GEODAR data of snow avalanches at Vallée de la Sionne: Seasons 2010/11, 2011/12, 2012/13 & 2014/15 [Data set]
<p>This data repository contains radar data from 77 snow avalanches recorded using the GEODAR (GEOphysical flow dynamics using pulsed Doppler radAR) system at the Swiss full-scale avalanche testsite Vallée de la Sionne. GEODAR is a purpose built, advanced phased-array FMCW system.</p> <p>The data contain range-time plots of intensities gained from moving target identification (MTI) processing (-MTI.h5), an PDF preview image (-MTI.pdf), the trajectory of the front in range and time (-TRAJ-001.h5), the corresponding Thalweg as steepest descent from release area (-Thal-001.h5) and a processing info file in Matlab format (-info.mat).</p> <p>This document covers details about the different versions of the radar setup and raw data processing steps as well as a description of the repository content (see file geodar_repository.pdf).</p> <p> </p>
Set-shifting in Egernia striolata
<p>Data collected during a study looking at the set-shifting ability of Egernia striolata, an Australian lizard species. The code used to analyse the raw data is provided.</p>
ALICE multi camera image set
<p>Images of pinned insects from multiple camera views with associated calibration grid images. This image set is used for label identification and segmentation in the ALICE project. https://doi.org/10.31219/osf.io/s2p73</p>
ALICE machine learning image set
<p>Pinned insect images and corresponding label outlines in JSON format. This image set is used for machine learning of label identification and segmentation for the ALICE project. https://doi.org/10.31219/osf.io/s2p73</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.