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Lagrangian particles in turbulence: An experimental data set
<p>This data set contains the coordinates of Lagrangian trajectories in a quasi-homogeneous isotropic turbulent flow. The trajectories were measured in a water tank experiment using the 3D-PTV method through the MyPTV open-source software (https://github.com/ronshnapp/MyPTV). The total duration of this data set corresponds to 20 seconds of recording at a rate of 500 Hz, and it holds roughly 575,000 trajectories.</p> <p> <br> The data is stored in two text files in a tab-separated format, where each file coresponds to 10 seconds of recording. Each row in the files outlines a single measurement point from the experiment. The various columns correspond to the following information:<br> 1 - trajectory_id <br> 2 - x [mm] <br> 3 - y [mm]<br> 4 - z [mm]<br> 5 - vx [mm/frame]<br> 6 - vy [mm/frame]<br> 7 - vz [mm/frame]<br> 8 - ax [mm/frame^2]<br> 9 - ay [mm/frame^2]<br> 10 - az [mm/frame^2]<br> 11 - time [frame]<br> where trajectory_id uniquely marks samples that correspond to the same physical trajectory; x, y, and z are the position components in the three orthogonal space directions; vx, vy, and vz correspond to the velocity components; ax, ay, and az that correspond to the acceleration components.</p> <p><br> The flow in the experiment was forced using a system of 8 propellers, powered by DC motors that were positioned at the corners of the cylindrical, octagonally shaped, water tank. The propellers were changing their direction of rotation at random time intervals with an average interval duration of 0.1 seconds. The root mean square of the turbulent flow fluctuations is about 100 millimeters per second. There is a time-averaged secondary circulation with a magnitude of roughly 66% of the root mean squared fluctuation strength. The Taylor microscale Reynolds number is estimated as about 188. </p>
Stimolazione Magnetica Transcranica nel setting sperimentale
<p>Video della presentazione "Stimolazione Magnetica Transcranica nel setting sperimentale" tenuta dallla Prof.ssa Raffaella Ricci durante il corso ECM "<strong>STIMOLAZIONE MAGNETICA TRANSCRANICA - TMS E TECNICHE AVANZATE DI NEUROSTIMOLAZIONE</strong>" nella giornata 26 maggio 2022.</p>
An experimental data set for analysis of the thermophysical behavior of a single-story mechanically ventilated double-skin façade (DSF) in fixed boundary conditions corresponding to winter/mid-season and summer cases
<p>Double-skin facades (DSFs) are dynamic and flexible building envelopes that employ a ventilated cavity to either prevent or reduce the solar-induced cooling load or exploit solar energy for passive solar heating. The mechanical ventilation of the cavity offers higher flexibility and control than natural ventilation, as the latter largely depends on stochastic and unpredictable external conditions. Furthermore, when mechanical ventilation rates are combined with the operation of a shading device, the possibilities for controlling the accumulated heat in the cavity of the DSF increase further. Therefore, this experimental campaign systematically investigates how these two important features interact in controlling the cavity's thermal load and airflow conditions. The measurement collected during the experiments constitutes a dataset that contains the results of a series of experimental runs where the different configurations of DSF, in terms of mechanical ventilation rate and venetian blinds, have been subjected to two representative boundary conditions through a climate simulator facility equipped with a solar simulator device. The full-scale DSF mock-up, which includes venetian blinds installed in a 200 mm ventilated cavity, is operated in this experiment in two modes: outdoor air curtain (OAC) and supply air (SA) mode. Tests were carried out under a steady-state regime with different boundary conditions. For the analysis of the utilization of the excess heat accumulated in the cavity and prevention of DSF overheating, boundary conditions corresponding to g-value calculations were selected. For the analysis of air preheating in the DSF cavity, the boundary conditions corresponding to late winter/mid-season weather (cold outdoor air and low-to-moderate solar irradiance) were chosen. The entire set of experimental data collected during the tests is made publicly available to enable the scientific community to access experimental data to further analyze this problem or for model validation purposes. The data set supplements the open-access paper entitled "<strong>Control of heat transfer in single-story mechanically ventilated facades</strong>" (<a href="https://doi.org/10.1016/j.enbuild.2022.112304">https://doi.org/10.1016/j.enbuild.2022.112304</a>), where additional information about the aims of the experiments, the detailed methods, and other data processing procedures can be found. 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. The collection of experimental tests is divided into two files, according to two considered cases:</p> <ul> <li><strong>DSF operating in outdoor air curtain mode </strong>(24 steady-state measurements). The following factors were changed: mechanical ventilation rate (0, 10, 15, 20, 30, 40, 50, and 100 % of maximum fan power) and venetian blind configuration (closed θ=0 º, semi-open θ=45 º, and raised blinds). The outdoor and indoor temperatures, 30 ℃ and 25 ℃, and solar irradiance of 500 Wm<sup>-2</sup> were replicated. [file name: "Summer.csv"],</li> <li><strong>DSF operating in supply air mode</strong> (27 steady-state measurements). The following factors were changed: mechanical ventilation rate (0, 10, 15, 20, 30, 40, 50, 75, and 100 % of maximum fan power) and venetian blind configuration (closed θ=0 º, semi-open θ=45 º, and raised blinds). The outdoor and indoor temperatures, 10 ℃ and 25 ℃, and solar irradiance of 300 Wm<sup>-2</sup> were replicated. [file name: " Winter_MidSeason.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>
Low winter temperatures and divergent freezing resistance set the cold range limit of widespread alpine graminoids
<p><span>Aim:</span><span> "Where and why does a species exist" is a fundamental question in ecology. However, the actual range limits of alpine plant species are largely unexplored and unexplained. We aim at identifying the low temperature range limits of the two most abundant alpine graminoid species on acidic soils that intermingle in mosaics of high-elevation habitats across the European Alps.</span></p> <p><span>Location:</span><span> Alpine grasslands in the Swiss Alps.</span></p> <p><span>Taxon:</span><span> Carex curvula (Cyperaceae) and Nardus stricta (Poaceae), named by the genus name hereafter.</span></p> <p><span>Results:</span><span> Carex </span><span>and Nardus clearly segregated across different microsites. Season length, growing degree hours and soil chemistry (pH, C/N-ratio, phosphorus) did not demarcate the two species' ranges, while their distribution was strongly affected by soil minimum temperature in winter. Carex occurred at sites with and without protecting snow cover and resisted low soil temperatures (-13 °C). Nardus was absent at microsites with snow cover duration less than 5 months and soil minimum temperatures below -5 °C. During the growing season, leaves of Carex had a higher freezing resistance with LT50 of -16.1 °C than those of Nardus with LT50 of -13.3 °C (LT50: lethal temperature for 50% of the tissue). Tetrazolium staining in shoots also revealed a higher freezing resistance in Carex compared to Nardus, and shoot apices tolerated lowest temperatures: Carex -30 °C, Nardus -24 °C. Though, a vital shoot apex alone did not ensure regrowth after winter. Regrowth after severe frost events requires intact vessels and roots, all less freezing tolerant than apical meristems and young leaves.</span></p> <p><span>Main conclusions:</span><span> The cold range limits of these widespread alpine graminoid species are evidently set by thermal extremes in winter. Microtopography, thus snow distribution pattern, in concert with the species' freezing resistance explains the cold edge of the fundamental niche of these two species.</span></p>
Dataset of the deleted DOIs extracted from the difference set between Crossref DOIs as of March 2017 and January 2021
<p><strong>Abstract</strong></p> <p>Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we identified the deleted DOIs by from the difference set between Crossref DOIs as of March 2017 and January 2021. We note that the file "00_Non-Crossref_DOIs.jsonl.gz" is not deleted DOIs but other files are deleted DOIs. Please refer the conference paper shown in the references for details. Sample of the record is the following.</p> <ul> <li>doi -- DOI name (String), e.g., "10.xxxx/xxxx"</li> <li>whichRA -- Registration agency name or error message for the DOI name according to the “<a href="https://www.doi.org/factsheets/DOIProxy.html#whichra">whichRA?</a>.” (String). "Airiti," "Crossref," "DOI does not exist," "DataCite," "KISTI," "Public," or "mEDRA."</li> <li>redirects -- Redirected URIs for the DOI name obtained by curl command (Array of String), e.g., ["https://doi.org/10.1001/archinte.166.4.387","http://archinte.jamanetwork.com/article.aspx?doi=10.1001/archinte.166.4.387"]</li> <li>redirect_to_other_doi -- The other DOI when the DOI link redirects to. (Array of String), e.g., "[10.1001/archinte.166.4.387]"</li> <li>timestamp -- Date the data was retrieved (Datetime), "2022-01-30T02:10:45Z"</li> <li>label -- The group where the DOI belongs to. Alias DOIs," "DOIs with Deleted Description in Metadata," "DOIs without Redirects," "Defunct DOIs," "Non-Crossref DOIs," "Non-existing DOIs," or "Other DOIs."</li> </ul> <p>As for the file "04_DOIs_with_Deleted_Description_on_Metadata.jsonl.gz," additional records are available as follows.</p> <ul> <li>alias_doi -- Alias DOI name, the same as the value of "doi." (String), e.g., "10.1007/bf00400428."</li> <li>primary_doi -- Primary DOI name for the alias DOI name, the same as the first value of "redirect_to_other_doi". (String), e.g., "10.1007/bf00400429"</li> <li>container_title_of_alias_doi -- the container title for the alias DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>title_of_alias_doi -- the title for the alias DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>container_title_of_primary_doi -- the container title for the primary DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> <li>title_of_alias_doi -- the title for the primary DOI according to the Crossref REST API. e.g., "CrossRef Listing of Deleted DOIs."</li> </ul> <p><strong>References</strong></p> <ul> <li>Kikkawa, J., Takaku, M. & Yoshikane, F. "Analysis of the deletions of DOIs: What factors undermine their persistence and to what extent?", Proceedings of the 26th International Conference on Theory and Practice of Digital Libraries (<a href="http://tpdl2022.dei.unipd.it/"><em>TPDL 2022</em></a>), (to appear), 2022.</li> </ul> <p><strong>FUNDING</strong></p> <ul> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K21303">JP21K21303</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-22K18147">JP22K18147</a>, <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12543">JP20K12543</a>, and <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K12592">JP21K12592</a></li> </ul>
Ballistic capture sets at Mars using Differential Algebra with initial epoch December 9, 2023
<p>The data set is made of two items containing initial sub-domains of inconsistent, weakly-stable, unstable, crash, moon-crash, and capture sets at Mars [1]. They are compressed files that contain a classification of the sub-domains of the search-space defined and used to compute the consistency and quality criteria of the differential algebra mapping, as well as graphical representations of the search space. All initial sub-domains have initial epoch 09 DEC 2023 00:45:18.363 (UTC). The search space is built to maximize the capture ratio for Mars (see Figure 10 in [2]). Initial sub-domains are propagated in high-fidelity. The equations of motion of the restricted n-body problem including solar radiation pressure are considered. The first item contains a low-resolution mapping. The search space is divided 32 times along the radius of the periapsis and the argument of the periapsis. The maximum depth of the Automatic Domain Splitting algorithm is 9 splits [3]. The first item contains a high-resolution mapping. The search space is divided 128 times along the radius of the periapsis and the argument of the periapsis. The maximum depth of the Automatic Domain Splitting algorithm is 10 splits [3].</p> <p>The data set is associated to the following journal article:</p> <ul> <li> Article: Stable sets mapping with Taylor differential algebra with application to ballistic capture orbits around Mars</li> <li> Journal: Celestial Mechanics and Dynamical Astronomy</li> <li> Authors: Thomas Caleb (corresponding author: thomas.caleb@student.isae-supaero.fr), Gianmario Merisio, Pierluigi di Lizia, and Francesco Topputo</li> <li> DOI: <a href="https://www.doi.org/10.1007/s10569-022-10090-8">10.1007/s10569-022-10090-8</a></li> </ul> <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="https://doi.org/10.1007/s10569-015-9605-8">10.1007/s10569-015-9605-8</a><br> [2] 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="https://doi.org/10.1016/j.asr.2015.05.042">10.1016/j.asr.2015.05.042</a><br> [3] A. Wittig, P. Di Lizia, et al., Propagation of large uncertainty sets in orbital dynamics by automatic domain splitting', Celestial Mechanics and Dynamical Astronomy, Vol. 122, No. 3, 2015, pp. 239-261. DOI: <a href="https://doi.org/10.1007/s10569-015-9618-3">10.1007/s10569-015-9618-3</a></p>
Landmark set for zygomatic bones
<p>Landmark configuration for left zygomatic bones (humans). The file includes 7 fixed landmarks and 23 semilandmarks. The data has been subject to Procrustes superimposition.</p>
data set of scopus about technology and halal meat supply chain publications
<p>This is the dataset of papers related to the topics of technology and halal meat supply chain collected from Scopus. The duration of year was 2008-2022</p>
RobotReviewer evaluation data (new test set)
<p>Includes 3,324 openly available PDFs (<em>rct_pdfs.zip</em>) with risk-of-bias annotations (<em>robotreviewer_eval_data.json</em>) from Cochrane systematic reviews. This data has not been used in the development of RobotReviewer and in this way represents a new, unseen test set. For each PDF/pubmed ID, Cochrane topics are also provided (<em>robotreviewer_topics.json</em>).</p>
Data Set : Seismic Wave Propagation Simulations in Indo Gangetic Basin using Spectral Element Method
<p>Indo Gangetic (IG) basin is one of the largest alluvial basins in the world. The surrounding Himalayan topography and the geometry of the basin make the IG basin unique. The analysis of seismic response of the basin is important as the region is seismically active with more than 40% of Indian population residing in it. This online database consists of the input files for performing the spectral finite element simulation for IG basin by incorporating the 3D variation of material properties and basin geometry. The input files consists of mesher, solver and CMTSOLUTION files for SPECFEM3D Cartesian (Version-3) simulation.</p>
ClinSpEn-CC (Clinical Cases) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Cases sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with three columns: document number, line number and English line. The direction of this sub-track is EN>ES. The clinical cases themselves include COVID-19 case reports as well as diverse content extracted from PubMed.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
ClinSpEn-OC (Ontology Concepts) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Ontology Concepts sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with two columns: concept number and English concept. The direction of this sub-track is EN>ES. Ontologies and structured vocabularies represent a key resource for semantic interoperability, entity linking, biomedical knowledge bases and precision medicine, and thus there is a pressing need to generate multilingual biomedical ontologies for a range of clinical applications</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
ClinSpEn-CT (Clinical Terms) Test + Background Set
<p>This repository contains the test and background data for the ClinSpEn-Clinical Terms sub-track. ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content.</p> <p>The data is made up of a TSV file with two columns: term number and Spanish term. The direction of this sub-track is ES>EN. The used terms were directly extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions.</p> <p> </p> <p>Related Links:</p> <p><strong>- Data website with more information: </strong><a href="https://temu.bsc.es/clinspen/">https://temu.bsc.es/clinspen/</a></p> <p><strong>- WMT website (includes schedule, registration, ...): </strong><a href="https://www.statmt.org/wmt22/">https://www.statmt.org/wmt22/</a></p> <p><strong>- CodaLab: </strong><a href="https://codalab.lisn.upsaclay.fr/competitions/6696">https://codalab.lisn.upsaclay.fr/competitions/6696</a></p> <p> </p> <p>ClinSpEn SAMPLE SETS:</p> <p><strong>- ClinSpEn-CC Sample Set (Clinical Cases):</strong> <a href="https://doi.org/10.5281/zenodo.6497350">https://doi.org/10.5281/zenodo.6497350</a></p> <p><strong>- ClinSpEn-CT Sample Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6497372">https://doi.org/10.5281/zenodo.6497372</a></p> <p><strong>- ClinSpEn-OC Sample Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6497388">https://doi.org/10.5281/zenodo.6497388</a></p> <p>ClinSpEn TEST SETS:</p> <p><strong>- ClinSpEn-CC Test Set (Clinical Cases): </strong><a href="https://doi.org/10.5281/zenodo.6948634">https://doi.org/10.5281/zenodo.6948634</a></p> <p><strong>- ClinSpEn-CT Test Set (Clinical Terms): </strong><a href="https://doi.org/10.5281/zenodo.6948669">https://doi.org/10.5281/zenodo.6948669</a></p> <p><strong>- ClinSpEn-OC Test Set (Ontology Concepts): </strong><a href="https://doi.org/10.5281/zenodo.6948679">https://doi.org/10.5281/zenodo.6948679</a></p> <p> </p>
PowerDuck: A GOOSE Data Set of Cyberattacks in Substations
<p>A recorded GOOSE data set as described in </p> <p>Sven Zemanek, Immanuel Hacker, Konrad Wolsing, Eric Wagner, Martin Henze, and Martin Serror. 2022. PowerDuck: A GOOSE Data Set of Cyberattacks in Substations. In Cyber Security Experimentation and Test Workshop (CSET ’22), August 8, 2022, Virtual, CA, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3546096.3546102</p> <p>The data set contains network traces of GOOSE communication recorded in a physical substation testbed. Further, it includes recordings of various scenarios with and without the presence of attacks. All network packets originating from the attacker are clearly labeled as such to facilitate their identification using the Industrial Protocol Abstraction Layer (IPAL) format. We thus envision PowerDuck improving and complementing existing data sets of substations, which are often generated synthetically, and thus aim to enhance the security of power grids.</p>
Data set with length measurments of niches in Chachabamba
<p>For the metrological analysis, this type of data was obtained from a 3D point cloud and all measurements for Chachabamba architectural remains were collected in two data sets.The first group belongs to the central part of the sanctuary, called sector A, and consists of 39 measurements. The second group of measurements was collected from the water fountains located in the four corners of sector A, where 31 measurements were collected.</p>
The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature [data set]
<p>This collection contains the dataset and the code which were used to find the thermal parameters’ lateral variations of the Volgo–Uralian subcraton through the Bayesian Markov Chain Monte Carlo (MCMC) statistical approach. The code originally was given in the analogous study of Antarctica's geothermal structure by Lösing et al. (2020) and it can be found in https://github.com/MareenLoesing/GHF-Antarctica-Bayesian. The main changes to the code of Lösing et al. (2020) are listed in the section 2 of the readme file.</p> <p>For an official use of the Bayesian inversion code please also cite: Lösing, M., Ebbing, J. & Szwillus, W. (2020) Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion. Front. Earth Sci., 8, 105. doi:10.3389/feart.2020.00105</p> <p>The lateral variations of the thermal parameters for the single-layer and multi-layer crust are saved in “GHF_Volgo-Uralia_Single-layer.csv” and “GHF_Volgo-Uralia_Multi-layer.csv” respectively.</p>
Net Enclosures Disrupt Codling Moth Dispersal Not Establishment - Data Set
<p>To maintain control of codling moth (<em>Cydia pomonella</em> (L.)), apple growers have pursued the use of exclusion netting. The structures implemented range from row covers which are supported by the tree canopy and tied off to the trunk, to full block enclosures which are supported by trellis systems and allow access for workers and equipment. It is uncertain if these nets provide a physical or behavioral barrier to codling moth and if they can prevent establishment in new blocks. To determine the effects of netting we conducted field trials with wild and sterile moths using small (3 trees) and large (48 trees) cages to evaluate the permeability of the netting and the establishment of wild moths.These are the data from those experiments which are used for the publication in Agricultural and Forest Entomology entitled, "Net enclosures disrupt codling moth dispersal not establishment".</p>
Data set for "The ion-ion recombination coefficient α: comparison of temperature- and pressure-dependent parameterisations for the troposphere and stratosphere"
<p>The uploaded data are related to the publication "The ion–ion recombination coefficient <span class="math-tex">\(\alpha\)</span>: comparison of temperature- and pressure-dependent parameterisations for the troposphere and stratosphere" in Atmospheric Chemistry and Physics (ACP). The data are the same as shown in the figures of the publication. The naming of the uploaded files indicates the figure (e.g., "Fig_2" indicates Figure 2 of the publication). </p>
Brain Tumor MR Image Data Set For Machine Vision Approach for Brain Tumor Classification using Multi Features Dataset
<p>The uploaded dataset contains the brain tumor MRI dataset. The dataset has been collected form the Bahawal Victoria Hospital, Bahawalpur, Pakistan. This dataset is an authorized MRI brain tumor dataset. Is has been authorized from the expert Radiologists of the Bahawal Victoria Hospital <a href="https://www.qamc.edu.pk/administration/2">BVH</a>. The dataset consists of three brain tumor types, namely adenomas, meningioma and glioma. it is only for academic, educational and experimental purpose. no other usage will be owned or any liability will be accepted by the authors.</p>
Data set: Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects
<p>This is the data set accompanying the paper "Quantifying the Accuracy of Collaborative IoT and Robot Sensing in Indoor Settings of Rigid Objects" by Sune L. Sørensen and Mikkel Baun Kjærgaard. Please refer to the paper for a description of the hardware used to record the data and how it is recorded.</p> <p>It consists of the following files:</p> <p><em>IoT camera images</em>: RBG images, named img_aa_bbb_0.jpg, where aa is the setup ID, bb is the camera ID (101, 102, 103 or 104).</p> <p><em>Robot RGB images</em>: RBG images, named aa0.png where aa is the setup ID.</p> <p><em>Robot point clouds</em>: pcd-files, named aa0.pcd where aa is the setup ID.</p> <p>The transformation from the IoT coordinate system to the robot coordinate system is:</p> <p>robotTiot = np.array([[0.914428, 0.134934, -0.378832, 3.76475],</p> <p>[0.393661, -0.49845, 0.772371, 0.791051],</p> <p>[-0.0846896, -0.855336, -0.509056, 2.37154],</p> <p>[0.0, 0.0, 0.0, 1.0]])</p> <p>Example, tranforming a pose in IoT coordinates to robot coordinates: p_rob = robotTiot * p_iot</p>
ScienceDex guides
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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.