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Individual .ttl files for plate VGF157 samples

Individual .ttl files for plate VGF157 samples processed using the ENPKG framework

Individual .ttl files for plate VGF159 samples

Individual .ttl files for plate VGF159 samples processed using the ENPKG framework

Individual .ttl files for plate VGF155 samples

Individual .ttl files for plate VGF155 samples processed using the ENPKG framework

Individual .ttl files for plate VGF138 samples

Individual .ttl files for plate VGF138 samples processed using the ENPKG framework

Individual .ttl files for plate VGF141 samples

Individual .ttl files for plate VGF141 samples processed using the ENPKG framework

Individual .ttl files for plate VGF145 samples

Individual .ttl files for plate VGF145 samples processed using the ENPKG framework

Individual .ttl files for plate VGF140 samples

Individual .ttl files for plate VGF140 samples processed using the ENPKG framework

Individual .ttl files for plate VGF142 samples

Individual .ttl files for plate VGF142 samples processed using the ENPKG framework

Individual .ttl files for plate VGF146 samples

Individual .ttl files for plate VGF146 samples processed using the ENPKG framework

Individual .ttl files for plate VGF147 samples

Individual .ttl files for plate VGF147 samples processed using the ENPKG framework

Individual .ttl files for plate VGF143 samples

Individual .ttl files for plate VGF143 samples processed using the ENPKG framework

Individual .ttl files for plate VGF144 samples

Individual .ttl files for plate VGF144 samples processed using the ENPKG framework

opencc-by-4.0Nov 2023View details →

Simulation data and surrogate model for the DTU 10MW reference wind turbine including down-regulation, power boosting and individual blade control

This contribution provides the simulated data and surrogate models for the DTU 10 MW reference wind turbine in an onshore configuration simulated with FAST v8.16.00. The dimensions include mean wind speed, turbulence intensity, and power level, as well as the application of an individual blade control (IBC) loop. Down-regulation up to 50% is considered using two controller trajectories. The constTSR trajectory considers only pitching for down-regulation, maintaining a constant tip speed ratio, and the lin70 trajectory considers both pitch and rotational speed reduction to achieve down-regulation. Power boosting is performed up to 130% power level by following the optimal Cp trajectory until the requested power level is reached. The regression is done with two methods: a spline-based interpolation and a  Gaussian Process Regression (GPR). The raw data, smoothened data, and the trained GPR models are provided along with scripts for generating the surrogate model's predictions with both methods. A short description of the simulation parameters and variables considered is given in the supplementary pdf file. The dataset is part of the doctoral thesis 'Wind Turbine Operational Optimization Considering Revenue and Fatigue Objectives' by Vasilis Pettas at the University of Stuttgart (<a href="http://dx.doi.org/10.18419/opus-13959">http://dx.doi.org/10.18419/opus-13959</a>) and the journal publication 'Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities' <a href="https://doi.org/10.3390/en17061284">(https://doi.org/10.3390/en17061284</a>). Detailed analysis of the controller design and validation of the surrogate models can be found in these publications.

opencc-zeroDec 2023View details →

Mitogenome alignment of 159 unique haplotypes representing 455 individual killer whales

Genome sequences can reveal the extent of inbreeding in small populations. Here we present the first genomic characterization of type D killer whales, a distinctive eco/morphotype with a circumpolar, subantarctic distribution. Effective population size is the lowest estimated from any killer whale genome and indicates a severe population bottleneck. Consequently, type D genomes show among the highest level of inbreeding reported for any mammalian species (FROH 0.65). Detected recombination events of different haplotypes are up to an order of magnitude rarer than in other killer whale genomes studied to date. Comparison of genomic data from a museum specimen of a type D killer whale that stranded in New Zealand in 1955, with three modern genomes from the Cape Horn area, reveals high covariance and identity-by-state of alleles, suggesting these genomic characteristics and demographic history are shared among different social groups within this morphotype. Limitations to the insights gained in this study stem from the non-independence of the three closely related modern genomes, the short coalescence time of most variation within the genomes, and the nonequilibrium population history which violates the assumptions of many model-based methods. Long-range linkage disequilibrium and extensive runs of homozygosity found in type D genomes provide the potential basis for coupling of genetic barriers to gene flow with other killer whale populations, and the distinctive morphology.

opencc-by-4.0Dec 2023View details →

Multimodal datataset of EEG, ECG and audio recordings of individual musicians playing emotional music

This dataset contains EEG, ECG and audio recordings of 11 individual musicians playing emotional music on their instrument. The dataset consists of two parts: <ol> <li> Experiment: Musicians’ self-reported ratings, audio recordings, and physiological recordings where the 11 expert musicians were asked to play at least 4~2-minute unfamiliar (non-popularly known) musical pieces. Participants were asked to play at least once one of the following  emotions:  happiness, sadness, relaxation, and anger. For the  physiological recordings EEG, ECG, and GSR signals were recorded. Each musican’s data is denoted by MS_ followed by the order in which they were recorded. </li> <li> Self-report questionnaire: A self-assessment questionnaire and their answers where 11 expert musicians were asked to rate musical pieces recorded based on: <ol> <li> Objective valence & arousal they felt the piece had; </li> <li> Felt valence & arousal during playing. </li> </ol> </li> </ol>   For a more detailed explanation of the dataset, its recording procedure, and its contents, see  L. Turchet, B. O'Sullivan, R. Ortner & C. Gugher (2024). Emotion Recognition of Playing Musicians from EEG, ECG, and Acoustic Signals. IEEE Transactions on Human-Machine Systems.   <h2>File Listing</h2> The following files are available (each explained in more detail below): <div> <table> <tbody> <tr> <td> Name </td> <td> Format </td> <td> Contents </td> </tr> <tr> <td> EEG_ECG_data_for_each_musician </td> <td> mat </td> <td> This folder contains the raw EEG, ECG, & GSR data for all 11 musicians for each piece they played as well as a resting state recording, which was recorded while a neutral audio stimulus was played. </td> </tr> <tr> <td> audio_data_for_each_musician </td> <td> wav, JSON, csv </td> <td> This folder contains 3 subfolders: 1) wav_audio_files_original: This is the raw audio data recorded for each musician and includes all 56 pieces included in the paper reported above, please see below regarding rejected trials. 2) wav_audio_files (original split_into_3_parts): Here, the 56 pieces are appropriately split into 3 separate parts. 3) analysis_audio_files: This folder contains the acoustic features extracted, 1714 acoustic features were extracted from each split trial. Each result is stored in .JSON, however, the collated results can be seen in all_results.csv. </td> </tr> <tr> <td> self_report_questionnaire </td> <td> pdf, xls </td> <td> Two files exist in this folder: 1. The self-reported questionnaire given to the musicians of the questionnaire during the experiment. 2.  </td> </tr> </tbody> </table> </div> <h2>File Details</h2>   <h3>EEG_ECG_data_for_each_musician</h3> These are the original raw data recordings. EEG data were recorded using a g.GAMMAcap2 by g.tec Medical Engineering, a 64-channel cap with g.SCARABEO active electrodes, with two g.GAMMAsys reference active ear clip electrodes. Two g.GAMMAbox electrode connector boxes were used to connect the active electrodes to two g.USBamp biosignal amplifiers with a sampling frequency of 256 Hz. The following 31 EEG channels were used: Fp1, Fp2, AFz, AF3, AF4, AF7, AF8, Fz, F3, F4, F7, F8, Cz, C3, C4, CP3, CP4, CP5, CP6, P1, P2, P3, P4, P5, P6, P7, P8, PO7, PO8, O1, and O2. AFz was used as a ground electrode and Cz was used as a re-reference electrode. The right-side ear clip electrode was used as a reference electrode.  ECG data were recorded using a single g.GAMMAclip active electrode clip connected directly to the g.GAMMAbox, sharing the same ground electrode with the EEG cap and placed on position V4 of the subjects. GSR data was recorded using the g.GSRsensor² box which contains two small dry electrodes placed underneath the participant’s toes (due to the amount of hand movement required for playing an instrument). The g.GSRsensor² was connected directly to a g.GAMMAbox, using jumper cables connected to different ports in the g.USBAMPs to share the same reference and ground electrodes as the EEG cap.    The locations of the channels and their corresponding number in the raw data is as follows: <div> <table> <tbody> <tr> <td> Channel Number </td> <td> Channel Name </td> </tr> <tr> <td> 1 </td> <td> Time series </td> </tr> <tr> <td> 2 </td> <td> AF3 </td> </tr> <tr> <td> 3 </td> <td> AF4 </td> </tr> <tr> <td> 4 </td> <td> AF7 </td> </tr> <tr> <td> 5 </td> <td> AF8 </td> </tr> <tr> <td> 6 </td> <td> CP3 </td> </tr> <tr> <td> 7 </td> <td> CP4 </td> </tr> <tr> <td> 8 </td> <td> CP5 </td> </tr> <tr> <td> 9 </td> <td> CP6 </td> </tr> <tr> <td> 10 </td> <td> P1 </td> </tr> <tr> <td> 11 </td> <td> P2 </td> </tr> <tr> <td> 12 </td> <td> P5 </td> </tr> <tr> <td> 13 </td> <td> P6 </td> </tr> <tr> <td> 14 </td> <td> P7 </td> </tr> <tr> <td> 15 </td> <td> P8 </td> </tr> <tr> <td> 16 </td> <td> O1 </td> </tr> <tr> <td> 17 </td> <td> O2 </td> </tr> <tr> <td> 18 </td> <td> Cz </td> </tr> <tr> <td> 19 </td> <td> Fp1 </td> </tr> <tr> <td> 20 </td> <td> Fp2 </td> </tr> <tr> <td> 21 </td> <td> F3 </td> </tr> <tr> <td> 22 </td> <td> F4 </td> </tr> <tr> <td> 23 </td> <td> F7 </td> </tr> <tr> <td> 24 </td> <td> F8 </td> </tr> <tr> <td> 25 </td> <td> C3 </td> </tr> <tr> <td> 26 </td> <td> C4 </td> </tr> <tr> <td> 27 </td> <td> P3 </td> </tr> <tr> <td> 28 </td> <td> P4 </td> </tr> <tr> <td> 29 </td> <td> PO7 </td> </tr> <tr> <td> 30 </td> <td> PO8 </td> </tr> <tr> <td> 31 </td> <td> AFz </td> </tr> <tr> <td> 32 </td> <td> GSR </td> </tr> <tr> <td> 33 </td> <td> ECG </td> </tr> </tbody> </table> </div> <h3>audio_data_for_each_musician/wav_audio_files_original</h3> This folder contains all of the raw audio files recorded during the experiment. All pieces were recorded using the software Audacity and exported as WAV files encoded with a bit depth of 32-bits and a sampling rate of 44.1 kHz. 56 of the included trials are present in this folder.   <h3>audio_data_for_each_musician/wav_audio_files (original split_into_3_parts)</h3> This folder contains the above mentioned 56 raw audio pieces separated into 3 appropriately sized recordings.   audio_data_for_each_musician/analysis_audio_files This folder contains the 1714 acoustic features from each of the 3 separated trials from 56 accepted pieces, these are denoted by MS_ followed by the order in which the musicians were recorded and the order in which the trails were split, the individual features are in JSON format. Within this folder, we have collated all results in a csv file (all_results.csv) where the columns show the intended emotion, trial name and number, followed by the names of the acoustic features.    self_report_questionnaire This pdf is the questionnaire which each musician was given following each trial relating to the emotions communicated and felt during each trial. Most* questions in the questionnaire were multiple-choice and speak pretty much for themselves. The answers for which were collated and are described below.   self_report_questionnaire_anwsers This .xls file contains the results of all the musicians self-reported ratings from the above described questionnaire.  <div> <table> <tbody> <tr> <td> Column name </td> <td> Description </td> </tr> <tr> <td> Subject </td> <td> The subject code of the musician, denoted by MS_ </td> </tr> <tr> <td> recording_filename </td> <td> The name of the trial denoted by MS_01_ followed by the trial number. </td> </tr> <tr> <td> intended_emotion </td> <td> The intended emotion the musician was instructed to communicate. The emotions are as follows: <ol> <li> Angry </li> <li> Sad </li> <li> Relaxed </li> <li> Happy </li> </ol> They are intended to be reported by using the valence-arousal space. </td> </tr> <tr> <td> valence_communicated </td> <td> The valence rating (integer between 1 and 5), that participants were asked to objectively rate how they thought the music played would be perceived.  </td> </tr> <tr> <td> arousal_communicated </td> <td> As above, but relating to arousal rather than valence. </td> </tr> <tr> <td> valence_felt </td> <td> The valence rating (integer between 1 and 5), that participants were asked to rate how they felt while playing the piece. </td> </tr> </tbody> </table> </div>   <div> <table> <tbody> <tr> <td> arousal_felt </td> <td> As above, but relating to arousal rather than valence. </td> </tr> <tr> <td> instrument_played </td> <td> The instrument played for each trial. </td> </tr> </tbody> </table> </div>

opencc-zeroDec 2023View details →

Data from: Lunar synchrony, geography, and individual clocks shape autumn migration timing in an avian migrant

Timing programs in animal migrants have been selected to synchronize movements that coincide with predictable resources on the breeding and nonbreeding grounds. Migrants face potential temporal conflicts if their migration schedules benefit from synchrony to conflicting rhythms associated with annual biogeographical (circannual) cues, lunar (circalunar) cues, or individually-repeatable internal clocks. We repeat-tracked individuals of an avian lunaphilic species, Eastern Whip-poor-will (Antrostomus vociferus), for 2–3 successive autumn migrations to determine the influence of the lunar cycle, breeding location, and individual repeatability on migration timing. Almost all birds avoided departing for migration during a full moon, likely to take advantage of the bright moonlight to facilitate visual foraging and enhance pre-migration fattening. However, groups from two latitudinally-distant sampling areas adjusted their autumn departure timing differently relative to the timing of the September full moon, presumably due to differences in seasonal prey availability. Individual repeatability increased throughout autumn migration, suggesting that the factors responsible for shaping migration timing may differ for different migration stages. Our results, that lunar synchrony, local climate, and individual internal clocks appeared to account for much of the variation in migration timing in whip-poor-wills, underscore the value of measuring potentially interacting factors that shape migratory behavior at species, group, and individual levels. It remains unclear if, or how, maintaining individually-repeatable annual migration schedules provides an adaptive benefit for whip-poor-wills or other lunaphilic migrants. Further clarifying the reasons for phenotypic variation in whip-poor-will migration timing will improve predictions of their abilities to adjust migratory movements under changing environmental conditions.

opencc-byApr 2021View details →

Qasr Hallabat Cistern Individual 1 View 1

This L pelvis fragment was recovered from a cistern within Qasr Hallabat, Jordan and dates to the 8th - 10th centuries AD. 3D modeling and photography of this element was conducted under a permanent loan agreement between the Department of Antiquities of Jordan and East Carolina University. Source: Objaverse 1.0 / Sketchfab

opencc-zeroJan 2024View details →

Individual and temporal variation in movement patterns of wild alpine reindeer and implications for disease management

Animal behaviour is important for prevalence and outbreaks of infectious diseases, for instance by affecting individual interactions. Increasing the knowledge of individual movement patterns can provide better insight into disease prevalence and spread, helping to target efforts to minimise disease outbreaks. Chronic wasting disease (CWD) is a fatal prion disease affecting cervids. CWD is transmitted by animal-to-animal contact and through the environment, thus individual variation in space use and social associations may influence disease transmission patterns and infection risk. CWD was detected in Norwegian alpine reindeer (Rangifer tarandus) in 2016, and eradication of the infected population was implemented. A 3:1 infection rate between males and females suggests sex-specific behavioural drivers. We utilised an extensive individual-based dataset of 149 GPS-marked wild reindeer to investigate individual variation in movement patterns in terms of inter- and intra-annual home range size and site fidelity, and variation in home range overlap and distances between individuals. We aimed to identify patterns which could indicate higher potential disease risk. Females had larger annual and seasonal home ranges than males, except during calving and rut. Greater home range overlaps and shorter between-individual distances were found between same-sex individuals than different-sex individuals, except during the rut. Accordingly, the rut season stands out with greater male home ranges, greater home range overlap and shorter distances between males and between males and females, which could indicate that this season is critical for disease transmission. Measures to prevent disease spread should lower contact rates, e.g., by reducing the abundance of adult males before they mix with other groups during the rut. This can be achieved for instance by allowing earlier hunt on adult males when they are distributed in small male groups, to reduce the transmission risk and keep disturbance of other individuals low.

opencc-zeroJan 2024View details →

Proteome of peripheral mononuclear cells (PBMCs) from asymptomatic malaria and uninfected individuals and the ensuing malaria episodes

Cumulative malaria parasite exposure in endemic regions often results in the acquisition of partial immunity and asymptomatic infections. There is limited information on how host-parasite interactions mediate maintenance of chronic symptomless infections that sustain malaria transmission. Here, we have determined the gene expression profiles of the parasite population and the corresponding host peripheral blood mononuclear cells (PBMCs) from 21 children (<15 years). We compared children who were defined as uninfected, asymptomatic and those with febrile malaria. Children with asymptomatic infections had a parasite transcriptional profile characterized by a bias toward trophozoite stage (~12 hours-post invasion) parasites and low parasite levels, while earlier ring stage parasites were characteristic of febrile malaria. The host response of asymptomatic children was characterized by downregulated transcription of genes associated with inflammatory responses, compared to children with febrile malaria. Interestingly, the host responses during febrile infections that followed an asymptomatic infection featured stronger inflammatory responses, whereas the febrile host responses from previously uninfected children featured increased humoral immune responses. The priming effect of prior asymptomatic infection may explain the blunted acquisition of antibody responses seen to malaria antigens following natural exposure or vaccination in malaria endemic areas.