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2,394 results for “Containers”
Interactions of the EphA2 Kinase Domain with a PIP2 containing membrane
<p>Last frames of atomistic simulations revealing the interactions of the transmembrane, juxtamembrane (JM), and kinase domains with the membrane. The structures highlight how the kinase domain is oriented relative to the membrane and how the JM region can modulate this interaction. These structures highlight the role of phosphatidylinositol phosphates (PIPs) in mediating the interaction of the kinase domain with the membrane and, conversely, how positively charged patches at the kinase surface and in the JM region induce the formation of nanoclusters of PIP molecules in the membrane.</p> <p>Analysis of the orientation of the kinase domain when bound to the PIP<sub>2</sub>-containing membrane suggests that there are two main modes of interaction. The predominant binding mode (inter1.pdb) involves the N-terminal lobe of the kinase domain. In this interaction mode, the activation loop of the kinase is accessible to phosphorylation. In the secondary mode (inter2.pdb), the interaction with the bilayer involves both the N- and C-terminal lobes of the kinase and thus the activation loop less accessible. </p>
Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework
<p>[This repository contains the source data for the manuscript "<strong>Real-time optical and electronic sensing with a β-amino enone linked, triazine-containing 2D covalent organic framework</strong>" https://nature-research-under-consideration.nature.com/users/37265-nature-communications/posts/47951-a-real-time-optical-and-electronic-chemical-sensor-based-on-a-amino-enone-linked-triazine-containing-2d-covalent-organic-framework]</p> <p>Fully-aromatic, two-dimensional covalent organic frameworks (2D COFs) are hailed as candidates for electronic and optical devices, yet to-date few applications emerged that make genuine use of their rational, predictive design principles and permanent pore structure. Here, we present a 2D COF made up of chemoresistant β-amino enone bridges and Lewis-basic triazine moieties that exhibits a dramatic real-time response in the visible spectrum and an increase in bulk conductivity by two orders of magnitude to a chemical trigger - corrosive HCl vapours. The optical and electronic response is fully reversible using a chemical switch (NH<sub>3</sub> vapours) or physical triggers (temperature or vacuum). These findings demonstrate a useful application of fully-aromatic 2D COFs as real-time responsive chemosensors and switches.</p>
Thermal decomposition data of uranium containing microspheres produced via internal gelation and ammonium diuranate powder
<p>A combination of simultaneous thermal analysis, evolved gas analysis and non-ambient XRD techniques was used to characterise and investigate the thermal decomposition behaviour in the NH<sub>3</sub> − UO<sub>3</sub> − H<sub>2</sub>O class of materials.</p> <p>One compound was prepared according to a typical ammonium diuranate precipitation reaction, and could be identified as 3UO<sub>3</sub>·NH<sub>3</sub>·5H<sub>2</sub>O. Microspheres prepared by the sol-gel method via internal gelation were associated to the composition 3UO<sub>3</sub>·2NH<sub>3</sub>·4H<sub>2</sub>O under the specified conditions.</p> <p>The products were analysed using the techniques listed below, the resulting data are part of this dataset.</p> <ul> <li>TGA, combined with EGA-MS (<em>T<sub>max</sub></em> = 1300 °C, heating rate = 2 °C/min)</li> <li>TG-DSC, combined with EGA-MS (<em>T<sub>max</sub></em> = 1300 °C, heating rate = 10 °C/min)</li> <li>ambient XRD (dried products after synthesis)</li> <li><em>in-situ</em> high temperature XRD (including initial and final scans, taken at 35 °C) <ul> <li><em>T<sub>max</sub></em> for 3UO<sub>3</sub>·NH<sub>3</sub>·5H<sub>2</sub>O = 1300 °C; <em>T<sub>max</sub></em> for 3UO<sub>3</sub>·2NH<sub>3</sub>·4H<sub>2</sub>O = 650 °C</li> <li>Samples measured directly on a Pt/Rh heating strip (Pt/Rh phase visible in patterns, blank scan included)</li> </ul> </li> </ul>
Dataset for Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Kłodawski Michał, Jachimowski Roland, & Chamier-Gliszczyński Norbert, 2024. „Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs”. Energies 17: 1–24. https://doi.org/10.3390/en17050985 - published online: 2024-02-20, which discusses the application of simulation in solving the problem of the overhead crane energy consumption using different container loading strategies in Urban Logistics Hubs.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset.</li> <li>Data_Crane.xlsx: Contains the input data used in the model for estimating crane energy consumption.</li> <li>Results_01.csv: Contains output data - Simulation results of energy consumption, and total average energy recovery for each scenario.</li> <li>Results_02.csv: Contains output data - Simulation results - mean values from the results of all scenario replications.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Deliverable 1.1.1.1 BEL-Float project | Dataset containing the results of numerical simulations (motions, forces) of the operational performance analysis - Input files
<p>This dataset contains the parent input used to generate the simulation files of the DeepCwind OC4 semi-submersible combined with the 5MW NREL turbine for various wind and wave conditions. The basis of the OpenFAST input files are taken from <a href="https://github.com/OpenFAST/r-test/tree/main/glue-codes/openfast/5MW_OC4Semi_WSt_WavesWN">OpenFAST r-test GitHub repository (5MW_OC4Semi_WSt_WavesWN)</a> and adapted to simulate various wind and wave conditions. The turbulent wind field as the input to the InflowWind module is generated using <a href="https://www.nrel.gov/wind/nwtc/turbsim.html">TurbSim</a>. The simulations are performed on a modified version of OpenFAST v3.5.3 to which adaptation to the code is made to extract additional Morison drag output up to 16 cylindrical members. This adapted code is <a href="https://github.com/abkpribadi/openfast/tree/Morison_additional_output">uploaded on GitHub as a branch from a forked OpenFAST repository</a>. In total there are 1152 simulation results consists of 768 irregular waves and 384 regular waves cases. The complete dataset is divided into 9 sub-datasets, see "Related work" section. A report describing this dataset will be made available on BEL-Float project website by November 2024: https://www.owi-lab.be/bel-float.</p>
Major facilitator superfamily domain-containing protein 10 (MFSD10) A Target Enabling Package (TEP)
<p>MFSD10 (also known as TETRAN in humans) has been proposed to function as an organic anion efflux pump and as a transporter for some NSAIDs. We have produced milligram quantities of purified recombinant protein and solved its structure in an outward-facing state at 2.6 Å resolution by X-ray crystallography. The structure - the first example for a human atypical SLC - provides the initial clues to understanding the broad specificity of its putative substrate-binding site.</p>
Bibliographic dataset based on Scientometrics, containing provenance information compliant with the OpenCitations Data Model and non disambigued authors
<p>The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals and bibliographic resources always appear unambiguously, without duplicates. On the contrary, the authors have not been disambigued. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.</p>
Datasets containing the results from the analysis on SDGS and eHealth inside the Citizen Science Community on Twitter
<p>This datasets contain the results from our analyses of the Citizen Science Community on Twitter. These analyses have been done to better understand the discussion about SDGs, eLearning and eHealth.</p> <p><strong>T</strong>he purpose of sharing these datasets is to provide the basis to reproduce the results reported in the associated deliverable. These files are not raw data, since due to privacy concerns we can not share personal information from Twitter.</p> <p><strong>dominant_topics_anonym.xlsx</strong>: Excel datasheet. This dataset contians the distribution of the most discussed topics inside the SDGs discussion.</p> <p><strong>Edges_Hashtag_connected.csv</strong>: CSV file. This dataset contains the edges to build the network of connected hashtags. This edges can be used to build a network and explore the connections or to statiscally analyse the results.</p> <p><strong>hashtags.csv</strong>: CSV file. This dataset contains the results of the most used hashtags in the analysis about eLearning. <br> </p> <p><strong>hashtags_treemap_health.xlsx</strong>: Excel datasheet. This dataset contains the results of the most frequent hashtags in the eHealth analysis.</p> <p><strong>ldavis_prepared_ieee17.html</strong>: HTML file. This file contains the Intertopic distance map and most salient terms from the topic modelling analysis done in the SDGs conversation study.</p> <p><strong>Most_retweeted_accounts.xlsx</strong>: Excel datasheet. This dataset contains the top 20 users that receive more retweets in the conversation around eHealth. The column called Indegree refers to the topological value calculated from the network of retweets. This indegree is equivalent to the number of retweets received. On the other hand, Outdegree is the opposite, so number of retweets given to others.</p> <p><strong>Most_retweeting_account.xlsx</strong>: Excel datasheet. This dataset presents the opposite part of the previous one, the accounts that retweet the most from the eHealth analysis. The columns contain the same indicators: Indegree and Outdegree.</p> <p><strong>sdgs_count_publish.csv</strong>: CSV file. This dataset contains the number of tweets assigned to the different SDGs from the analysis done on the conversation about these Goals.</p> <p><strong>sdgs_tweets_sdgsaccess.xlsx</strong>: Excel datasheet. Same file as the previous one in other format to ease the handling in Excel.</p> <p><strong>top_hash_health.xlsx</strong>: Excel datasheet. The most used hashtags inside the conversation about eHealth.</p> <p><strong>topics_tweets_sdgsaccess.xlsx</strong>: Excel datasheet. Tweets by topic extracted using Machine Learning in the SDGs analysis.</p> <p> </p> <p>This repository will receive updates in the future in order to present all the data available and publishable from the different analysis that were described.</p>
Stimuli Responsive and Antimicrobial Cellulose-Chitosan Hydrogels Containing Polydiacetylene Nanosheets
<p>Hydrogels were prepared by esterification of chitosan (Cs) with monochloroacetic acid to produce CMCs which was then crosslinked to HEC using citric acid as the crosslinking agent. To impart a stimuli responsiveness property to the hydrogels, polydiacetylene-zinc oxide (PDA-ZnO) nanosheets were synthesized in-situ during the crosslinking reaction followed by photopolymerization of the resultant composite. First, 10,12-pentacosadiynoic acid (PCDA) head groups were stabilized with ZnO nanoparticles in the presence of CMCs-HEC hydrogels in petroleum ether. This was followed by irradiating the composite with Uv radiation to photopolymerize the PCDA to PDA within the hydrogel matrix so as to impart thermal and pH responsiveness to the hydrogel</p>
An Empirical Study of Container Image Configurations and Their Impact on Start Times (Container Image Data)
<p>Dataset with the container image metadata used for our IEEE/ACM CCGRID 2023 paper "An Empirical Study of Container Image Configurations and Their Impact on Start Times".</p> <p>Abstract of the paper: A core selling point of application containers is their fast start times compared to other virtualization approaches like virtual machines. Predictable and fast container start times are crucial for improving and guaranteeing the performance of containerized cloud, serverless, and edge applications. While previous work has investigated container starts, there remains a lack of understanding of how start times may vary across container configurations. We address this shortcoming by presenting and analyzing a dataset of approximately 200,000 open-source Docker Hub images featuring different image configurations (e.g., image size and exposed ports). Leveraging this dataset, we investigate the start times of containers in two environments and identify the most influential features. Our experiments show that container start times can vary between hundreds of milliseconds and tens of seconds in the same environment. Moreover, we conclude that no single dominant configuration feature determines a container's start time and that hardware and software parameters must be considered together for an accurate assessment.</p> <p>Dataset description: Our images dataset contains 200,986 entries with 21 features associated to each container image. In the following, we describe the meaning of each feature. Further information is available in <a href="https://github.com/opencontainers/image-spec">OCI Image Specification</a> and the <a href="https://docs.docker.com/engine/reference/run/">Docker Run Documentation</a>. Besides the 20 features grouped in the five categories below, each dataset entry has a image_id, which is used to uniquely identify the dataset entry.</p> <p>Features</p> <p>Metadata features (prefix: meta)</p> <ul> <li><strong>meta_repo_digest</strong> : The repo digest is a SHA-256 hash which is used to uniquely identify and pull the image from Docker Hub</li> <li><strong>meta_architecture</strong> : The CPU architecture which the binaries in the image are built to run on</li> <li><strong>meta_os</strong> : The name of the operating system which the image is built to run on</li> <li><strong>meta_docker_version</strong> : The Docker version used to built this image</li> </ul> <p>I/O stream features (prefix: io)</p> <ul> <li><strong>io_attach_stdin</strong> : boolean setting to determine whether the console should be attached to the process stdin stream</li> <li><strong>io_attach_stdout</strong> : boolean setting to determine whether the console should be attached to the process stdout stream</li> <li><strong>io_attach_stderr</strong> : boolean setting to determine whether the console should be attached to the process stderr stream</li> <li><strong>io_tty</strong> : boolean setting to determine whether the console should pretend to be a TTY when attached</li> <li><strong>io_open_std_in</strong> : boolean setting to determine whether the process stdin stream should be kept open even if console not attached</li> <li><strong>io_std_in_once</strong> : boolean setting to determine whether the process retrieved input from the stdin stream at least once</li> </ul> <p>Start command features (prefix: cmd)</p> <ul> <li><strong>cmd_args</strong> : Length of list of arguments to use as the command to execute when the container starts</li> <li><strong>cmd_envvars</strong> : Environment variables set per default when the container starts</li> <li><strong>cmd_additional_args</strong> : Length of list for additional arguments to the containers entrypoint</li> </ul> <p>File system features (prefix: fs)</p> <ul> <li><strong>fs_volumes</strong> : Number of volumes to create/use by default</li> <li><strong>fs_size</strong> : Size of this image in bytes</li> <li><strong>fs_virtual_size</strong> : Virtual size of this image in bytes (equals size)</li> <li><strong>fs_graph_driver_name</strong> : Name of the image's graph driver</li> <li><strong>fs_root_fs_type</strong> : Name of the file system type used in the image</li> <li><strong>fs_layers</strong> : Number of root file system layers</li> </ul> <p>Networking features (prefix: net)</p> <ul> <li><strong>net_ports</strong> : Number of ports to expose per default</li> </ul> <p> </p> <p>Dataset acquisition: The dataset has been acquired from Docker Hub using a web crawler. We used substring matches with the <a href="https://hub.docker.com/explore">Docker Hub Explore function</a>. As search strings, we used all letter combination with sizes 1 to 3, meaning that our first search string was 'a' and our last was 'zzz'. We included both results from the 'recently updated' and the 'most popular' selection. We came up with an initial list of 286,294 image names. We then tested we could pull and start these images once. These tests have been conducted from April to June 2022. We sorted out all images that were either not pullable or startable and retrieved all total of 200,986 valid images. In the following, we describe the error types that we encountered and that let to the removal of the causing image from the dataset:</p> <ul> <li>The image manifest was unknown when we tried to download it meaning that is has been renamed or deleted from the time when our web crawler was running</li> <li>The entrypoint command required a dependency that was missing in the image and therefore the container could not be started</li> <li>The image did not specify an entrypoint command and could therefore not be started</li> <li>The image declared an invalid root file system type</li> <li>The image had a malformed root file system</li> <li>The image configuration was incomplete and therefore not all required data could be obtained</li> </ul> <p>See also our CodeOcean capsule with the processing scripts for our paper: https://doi.org/10.24433/CO.4595026.v2</p>
Dataset for manuscript: Rock anisotropy promotes hydraulic fracture containment at depth
<p>This is the experimental data used in manuscript: "Rock anisotropy promotes hydraulic fracture containment at depth".</p> <p>This data set has 5 folders: </p> <p>K1, T2, M3 and M4 contain the raw data collected by active acoustic monitoring system and various pumps and transducers. <br> The readings of the Top Industrie syringe pump (for fluid injection), pressure transducers at downstream, and GDS pumps that provide confining stresses along all three directions via flatjacks are provided by two excel spreadsheets - Low_frequency_measurements.csv and Pump_reading.csv.<br> For active acoustic data, there are four different files named by the starting time for each experiment. The .json file contains the basic information of each experiment. The .txt file contains the acquisition time for each active acoustic sequence. The .bin file contains the collected waveforms of all acoustic sequences.</p> <p>Processed_data includes the synchronized pressure data (Pressure_data.xlsx) and detailed acoustic emission results (AE_K1.csv, AE_T2.csv, AE_M3.csv, AE_M4.csv).<br> </p>
Extensive crowdsourced dataset of in-situ evaluated binaural soundscapes of private dwellings containing subjective sound-related and situational ratings along with person factors to study time-varying influences on sound perception — research data
<p><strong>Abstract:</strong></p> <p>The soundscape approach highlights the role of situational factors in sound evaluations; however, only a few studies have applied a multi‐domain approach including sound‐related, person‐related, and time‐varying situational variables. Therefore, we conducted a study based on the Experience Sampling Method to measure the relative contribution of a broad range of potentially relevant acoustic and non‐auditory variables in predicting indoor soundscape evaluations. Here we present the comprehensive dataset for which 105 participants reported temporally (rather) stable trait variables such as noise sensitivity, trait affect, and quality of life. They rated 6.594 situations regarding the soundscape standard dimensions, perceived loudness, and the saliency of its sound components and evaluated situational variables such as state affect, perceived control, activity, and location. To complement these subject‐centered data, we additionally crowdsourced object‐centered data by having participants make binaural measurements of each indoor soundscape at their homes using a low‐(self‐)noise recorder. These recordings were used to compute (psycho‐)acoustical indices such as the energetically averaged loudness level, the A‐weighted energetically averaged equivalent continuous sound pressure level, and the A‐weighted five‐percent exceedance level. This complex hierarchical data can be used to investigate time‐varying non‐auditory influences on sound perception and to develop soundscape indicators based on the binaural recordings to predict soundscape evaluations.</p> <p><strong>Content:</strong></p> <ul> <li><a href="https://zenodo.org/record/7858848/files/01%20StudyDescription.pdf">01 StudyDescription.pdf </a> <ul> <li>Description of the field study.</li> <li>Information about the methods and materials used.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/02%20Dataset.csv">02 Dataset.csv</a> <ul> <li>The dataset, consisting of 93 variables describing 6594 observations taken by 105 participants.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/03%20VariableDescriptions_EnglishPersonQuestionnaire.pdf">03 VariableDescriptions_EnglishPersonQuestionnaire.pdf</a> <ul> <li>Descriptions of all variables, their measurement scale, scale ranges and levels.</li> <li>Questions and task descriptions of the Experience Sampling Method questionnaire in German language with an English translation.</li> <li>English translations of questions asked in the person questionnaire.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/04%20ESM-Questionnaire.pdf">04 ESM-Questionnaire.pdf</a> <ul> <li>Screenshots of the original Experience Sampling Method questionnaire with English translations.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/05%20PersonQuestionnaire_OriginalGermanVersion.pdf">05 PersonQuestionnaire_OriginalGermanVersion.pdf</a> <ul> <li>Original version of the person questionnaire in German language.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/06%20HelpTexts.pdf">06 HelpTexts.pdf</a> <ul> <li>Descriptions of the study task.</li> <li>Explanations of the scales used in the questionnaire.</li> <li>Explanations of the sound categories and the soundscape composition.</li> <li>Explanation of the operation of the recording device.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_README.md">AcousticFeatures_README.md</a> <a href="https://zenodo.org/api/files/3d784540-c0f4-412f-8742-df1db6f5401d/TimeSeries_and_Spectrograms_README.md?versionId=9291496c-d2c6-4151-96f1-a2ad99e1a540"> </a> <ul> <li>Descriptions of the structure of the AcousticFeatures_xxx.csv and .zip files.</li> <li>Analyis settings used in Artemis Suite to generate the acoustic features.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_SingleValues.csv">AcousticFeatures_SingleValues.csv</a> <ul> <li>All acoustic features, aggregated to single values per feature, recording, and channel.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectra.csv">AcousticFeatures_Spectra.csv</a> <ul> <li>Time-averaged 1/3 octave spectra of each channel of each recording, A-weichted and un-weighted.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_Spectrograms.zip">AcousticFeatures_Spectrograms.zip</a> <ul> <li>13188 .csv files with un-weighted spetrograms of each channel of each recording.</li> </ul> </li> <li><a href="https://zenodo.org/record/7858848/files/AcousticFeatures_TimeSeries.zip">AcousticFeatures_TimeSeries.zip</a> <ul> <li>A .csv file containing LAeq and LZeq time series of each channel of each recording.</li> </ul> </li> </ul> <p><strong>Publications refering to this dataset:</strong></p> <p>Versümer, Siegbert; Steffens, Jochen; Weinzierl, Stefan (currently under review): "The role of loudness predictions, personal and situational factors in day-to-day loudness assessments of indoor soundscapes."</p> <p><strong>Funding:</strong></p> <p>This study was sponsored by the German Federal Ministry of Education and Research. “FHprofUnt” funding code: 13FH729IX6. </p> <p><strong>License: </strong></p> <p>CC 4.0 BY, <a href="https://creativecommons.org/licenses/by/4.0/legalcode">https://creativecommons.org/licenses/by/4.0/legalcode</a></p> <p><strong>Version history:</strong></p> <p>Details can be found in the <a href="https://zenodo.org/api/files/a15d6a91-1a35-4b5e-a7ec-da8a9bcbee2b/Changelog.md">Changelog.md</a> file.</p> <ul> <li> V.01.0. March 7, 2023: Initial publication. <a href="https://doi.org/10.5281/zenodo.7193938">https://doi.org/10.5281/zenodo.7193938</a></li> <li> V.01.1. April 25, 2023. <a href="https://doi.org/10.5281/zenodo.7858848">https://doi.org/10.5281/zenodo.7858848</a></li> </ul>
A dataset of metadata for UK academic institutional repositories, including a census of research software contained.
<p>A dataset of metadata for UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table> <p> </p>
CHOC: The CORSMAL Hand-Occluded Containers dataset
<p>CORSMAL Hand-Occluded Containers (CHOC) is an image-based dataset for category-level 6D object pose and size estimation, affordance segmentation, object detection, object and arm segmentation, and hand+object reconstruction. The dataset has 138,240 pseudo-realistic composite RGB-D images of hand-held containers on top of 30 real backgrounds (mixed-reality set) and 3,951 RGB-D images selected from the <a href="https://corsmal.eecs.qmul.ac.uk/containers_manip.html">CORSMAL Container Manipulation (CCM)</a> dataset (real set). CHOC-AFF is the subset that focuses on the problem of visual affordance segmentation. CHOC-AFF consists of the RGB images, the object and arm segmentation masks, and the affordance segmentation masks.</p> <p>The images of the mixed-reality set are automatically rendered using <a href="https://www.blender.org/">Blender</a>, and are split into 129,600 images of handheld containers and 8,640 images of objects without hand. Only one synthetic container is rendered for each image. Images are evenly split among 48 unique synthetic objects from three categories, namely 16 <em>boxes</em>, 16 drinking containers without stem (<em>nonstems</em>) and 16 drinking containers with stems (<em>stems</em>), selected from <a href="https://shapenet.org/">ShapeNetSem</a>. For each object, 6 realistic grasps were manually annotated using <a href="https://graspit-simulator.github.io/">GraspIt!</a>: bottom grasp, natural grasp, and top grasp for the left and right hand. The mixed-reality set provides RGB images, depth images, segmentation masks (hand and object), normalised object coordinates images (only object), object meshes, annotated 6D object poses (orientation and translation in 3D with respect to the camera view), and grasp meshes with their <a href="https://mano.is.tue.mpg.de/">MANO</a> parameters. Each image has a resolution of 640x480 pixels. Background images were acquired using an <a href="https://en.wikipedia.org/wiki/Intel_RealSense">Intel RealSense D435i</a> depth camera, and include 15 indoor and 15 outdoor scenes. All information necessary to re-render the dataset is provided, namely backgrounds, camera intrinsic parameters, lighting, object models, and hand + forearm meshes, and poses; users can complement the existing data with additional annotations. Note: The mixed-reality set was built on top of previous works for the generation of synthetic and mixed-reality datasets, such as <a href="https://hassony2.github.io/obman.html">OBMan</a> and <a href="https://geometry.stanford.edu/projects/NOCS_CVPR2019/">NOCS-CAMERA</a>.</p> <p>The images of the real set are selected from 180 representative sequences of the CCM dataset. Each image contains a person holding one of the 15 containers during a manipulation occurring in the video prior to a handover (e.g., picking up an empty container, shaking an empty or filled food box, or pouring a content into a cup or drinking glass). For each object instance, sequences were chosen under four randomly sampled conditions, including background and lighting conditions, scenarios (person sitting, with the object on the table; person sitting and already holding the object; person standing while holding the container and then walking towards the table), and filling amount and type. The same sequence is selected from the three fixed camera views (two side and one frontal view) of the CCM setup (60 sequences for each view). Fifteen sequences exhibit the case of the empty container for all fifteen objects, whereas the other sequences have the person filling the container with either pasta, rice or water at 50% or 90% of the full container capacity. The real set has RGB images, depth images and 6D pose annotations. For each sequence, the 6D poses of the containers are manually annotated every 10 frames if the container is visible in at least two views, resulting in a total of 3,951 annotations. Annotations of the 6D poses for the intermediate frames are also provided by using interpolation.<br> </p> <p><strong>Contacts</strong><br> For enquiries, questions, or comments, please contact <a href="mailto:alessio.xompero@gmail.com?subject=CHOC">Alessio Xompero</a>. For enquiries, questions, or comments about CHOC-AFF, please contact <a href="mailto:tommaso.apicella@edu.unige.it?subject=CHOC-AFF%20%2F%20ACAnet">Tommaso Apicella</a>.<br> </p> <p><strong>References</strong><br> If you work on Visual Affordance Segmentation and you use the subset CHOC-AFF, please see the related work on <a href="https://apicis.github.io/projects/acanet.html">ACANet </a>and also cite:<br> <em>Affordance segmentation of hand-occluded containers from exocentric images</em><br> T. Apicella, A. Xompero, E. Ragusa, R. Berta, A. Cavallaro, P. Gastaldo<br> IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023<br> </p> <p><strong>Additional resources</strong><br> <a href="https://corsmal.eecs.qmul.ac.uk/pose.html">Webpage</a> of 6D pose estimation using CHOC<br> <a href="https://github.com/CORSMAL/CHOC-dataset-toolkit">Toolkit</a> to parse and inspect the dataset, or generate new data<br> </p> <p><strong>Release notes</strong><br> 2023/09/10<br> - Added object affordance segmentation masks<br> <br> 2023/02/08<br> - Fixed NOCS maps due to a missing rotation during the generation<br> - Fixed annotations to include the missing rotation<br> <br> 2023/01/09<br> - Fixed RGB_070001_80000 (wrong files previously)<br> <br> 2022/12/14<br> - Added a mapping dictionary from grasp-IDs to their corresponding MANO-parameters-IDs to grasp.zip<br> - Added object meshes with the NOCS textures/material in object_models.zip<br> - Fixed folder name in annotations.zip<br> - Updated README file to include these changes and fix a typo in the code block to unzip files</p>
Data describing the life cycle and material flows of neodymium contained in products
<p>Assumptions used to calculate flows of neodymium (Nd) in Europe. For various products (consumer products and industrial goods), the dataset describes the following properties:</p> <ul> <li>lifespan,</li> <li>product weight,</li> <li>neodymium content,</li> <li>end-of-life (EoL) fate,</li> <li>component weight,</li> <li>market share of Nd-containing components</li> </ul> <p>The classification of products is based on UNU Keys.</p>
Figure 2 in Bornean caterpillar (Lepidoptera) constructs cocoon from Vatica rassak (Dipterocarpaceae) resin containing multiple deterrent compounds
Figure 2. Pieces of resin taken from the cocoon and imaged (A) using photomontage; and (B– D) environmental electron microscopy. Images (B–D) show the elaborate shearing patterns within the resin. The centre of image (D) shows what may be a score mark in the surface of the resin made by the caterpillar.
Self-contained 4-BSS's dataset of spectrum management in WLANs
<p>This folder contains the self-contained dataset of 4 BSS's analyzed in the thesis by <em>Sergio Barrachina-Muñoz, "Responsive Spectrum Management for Wireless Local Area Networks: from Heuristic-based Policies to Model-Free Reinforcement Learning", 2020</em>.</p> <p>-------------------------------------------------------------<br> <strong>*** General info ***</strong></p> <p>The dataset has been generated simulating all the spectrum management configurations (including primary channel and maximum bandwidth) in a 4-BSS's deploymment. Simulations have been performed with the Komondor wireless network simulator (<a href="https://github.com/wn-upf/Komondor">https://github.com/wn-upf/Komondor</a>).</p> <p><strong>*** Dataset structure ***</strong></p> <p>The dataset is composed of 1 file, dataset.csv, containing all the combinations of spectrum management configurations.</p> <p><strong>*** File format ***</strong></p> <p>The dataset.csv file is composed of 53 columns and 1679616 rows. <br> - Each column is a parameter or performance metric of the global spectrum management configuration, i.e., the configuration of all the BSS's.<br> - Each row is a realization of the global configuration.<br> - Column sim_code refers to the simulation code.</p> <p>The colums for each BSS are (only showing for BSS A):<br> - bss_A_code: code of the BSS<br> - action_ix_A: action (or BSS configuration) index<br> - status_ix_A: status index (combination of action and traffic load)<br> - primary_A: primary channel of the BSS<br> - max_bw_ix_A: index of the maximum allowed bandwidth of the BSS<br> - load_ix_A: traffic load index of the BSS<br> - load_A: traffic load [pkt/s] of the BSS<br> - thr_A: throughput [Mbps] of the BSS<br> - d_A: packet delay [ms] of the BSS<br> - rts_lost_A: number of RTS lost by BSS A<br> - rts_sent_A: number of RTS sent by BSS A<br> - frames_lost_A: number of frames lost by BSS A<br> - frames_sent_A: number of frames sent by BSS A</p>
Dataset containing laser speckle-contrast images
<p>Dataset contains laser speckle-contrast images of human skin under various physiological tests (controlled respiration test, breath holding test, venous occlusion test).</p>
Container Registry Benchmark experiments measurements and trace workload samples
<p>Measurements for experiments using Container Registry Benchmark, CReB. 4 experiments: Long running, small experiment stress mode, small experiment delay mode, and large workload experiment.</p> <p> </p> <p>Structure:</p> <ol> <li><strong>full-measurements-long-running-pull.csv : </strong>measurements for long running pull experiment</li> <li><strong>full-measurements-long-running-push.csv: </strong>measurements for long running push experiment</li> <li><strong>result-bug-analysis.zip: </strong>results from bug analysis of trace replayer</li> <li><strong>results-1hr-experiment.zip: </strong>measurements for the large experiment (4 registries)</li> <li><strong>results-small-delay.zip: </strong>measurements for the delay mode, small experiment with real workload</li> <li><strong>results-small-stress.zip: </strong>measurements for the stress mode, small experiment with real workload</li> <li><strong>traces.zip: </strong>traces used for pen-and-paper experiment, 1 hour sample, and the trace used for small experiment (selected are first 405 requests)</li> </ol>
Example phenopacket containing one disease and two associations
<p>This is an example of a nascent exchange format "Phenopacket". For more information, see github.com/phenopackets</p>
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