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282 results for “imagination”
Centroid Moment Tensor solutions for the earthquake dataset of the project IMAGINE_IT
<p>The project IMAGINE_IT (PI Dr. Dimitri Komatitsch) received 40 million CPU-hours on the Tier-0 GENCI/TGCC CURIE supercomputer as a winner of the 9th PRACE consortium call (2014). </p> <p>The awarded computational resources allowed us to construct a new 3D tomographic model for the Italian lithosphere, <em>Im25</em>,<em> </em>by combining spectral-element three-dimensional wavefield simulations and an adjoint-state method.</p> <p>To obtain the final model <em>Im25, </em>we performed 25 adjoint tomography iterations. Moreover, two additional source inversion iterations have been performed in order to improve the earthquake source parameter estimates and reduce the misfit between observed and synthetic seismograms: one inversion using the 3D wavespeed model considered as starting model of the tomographic procedure, and one inversion for the improved wavespeed model at iteration 12 (<em>Im12</em>). </p> <p>The presented table contains the Centroid Moment Tensor parameters of the163 earthquakes considered in the IMAGINE_IT project for: the initial (Time Domain Moment Tensor; http://terremoti.ingv.it/) source solution based on a 1D wavespeed model (iter=0), the source inversion solution with the starting 3D wavespeed model (iter=1), and the source inversion solution with model <em>Im12</em> (iter=2). <strong> </strong></p>
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
Emotional imagination of negative situations: functional neuroimaging in anorexia and bulimia
<p>Clinical dataset (SPSS 20 file) 64 subjects.</p> <p>fMRI con images 64 subjects described in the clinical dataset.</p> <p> </p>
SNSF Spark project "Sonic Imagination" – Excerpts of recorded evaluation interviews
<p>We aimed to investigate the hypothesis that virtual sound sources in audio augmented environments are particularly suitable for triggering and directing imaginations within human inner perception. Our hypothesis was that the binaural listening to imaginary entities at the place of recording (so to speak 'in-situ') enhances the imagination in a special way, since sound as a non-visual medium favours the creation of images in human inner perception. We narrowed down our research question especially in regards to our practical experimentations to possible applications in areas that rely on the guided creation of individual or intersubjective imaginations, such as urban planning and scenario development, certain forms of trauma therapy, as well as audiovisual art and fiction.</p> <p>Further information: </p> <p><a href="https://www.ludwigzeller.net/projects/sonic-imagination/">https://www.ludwigzeller.net/projects/sonic-imagination/</a></p> <p><a href="https://p3.snf.ch/project-195868">https://p3.snf.ch/project-195868</a></p> <p> </p>
Multi-surveyor capture-mark-recapture as a powerful tool for butterfly population monitoring in the pre-imaginal stage
<p>For many elusive insect species, which are difficult to cover by standard monitoring schemes, innovative survey methods are needed to gain robust data on abundance and population trends. The recording of pre-imaginal butterfly life stages provides great potential for ecological studies and conservation monitoring. However, using counts of pre-imaginal stages for quantitative research requires detection probability to be determined.</p> <p>We tested different removal and capture-mark-recapture (CMR) approaches to determine the detection probability for overwintering larvae of the endangered nymphalid butterfly <em>Limenitis reducta</em>. Classical removal and CMR studies require movement of the organisms under study but in our approach, we replaced movement of the study organisms by random movement of multiple different surveyors. The study was conducted in three plots within a spruce clear-cut in the 'Alb-Donau' region, Germany.</p> <p>Our dataset provides detection data of nine/ten different surveyors per study plot. The surveyors differed in their experience ('experts' vs. 'novices'). In the R-scripts we present the analysis of the data using i) the removal method, ii) CMR approaches with varying personnel expenditure. In addition, we test the validity of our method by comparing observed and simulated detection frequencies.</p> <p>The results of our study indicate that multi-surveyor removal/CMR techniques are highly suitable for estimating abundance of overwintering L. reducta larvae and that the proposed methodology has several strengths: long survey period, estimates of the absolute population size accompanied by uncertainty measures, estimates of overwinter mortality. The methods from our study can be adapted and used for several different butterfly species, other insect taxa with specific immobile life-stages, and some sessile organisms, e.g. elusive plants, fungi, or corals.</p>
РИС. 11. ИЗображениЯ крючков глохидиев при раЗной настройке Яркости и контрастности (Anemina arcaeformis, р. Сита, Хабаровский кр.). А. Оптимальные Яркость и контрастность. В. ИЗбыточнаЯ контрастность. С. ИЗбыточнаЯ Яркость. МасШтаб 20 мкм. Микроскоп Zeiss EVO 40, напыление углеродом. FIG. 11. Imagines of glochidia hooks with different brightness and contrast value (Anemina arcaeformis, Sita River, Khabarovsk Krai). A. Optimal brightness and contrast. B. Excessive contrast. C. Excessive brightness. Scale bars 20 μm. Zeiss EVO 40 microscope, sputter coating with carbon. in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 11. ИЗображениЯ крючков глохидиев при раЗной настройке Яркости и контрастности (Anemina arcaeformis, р. Сита, Хабаровский кр.). А. Оптимальные Яркость и контрастность. В. ИЗбыточнаЯ контрастность. С. ИЗбыточнаЯ Яркость. МасШтаб 20 мкм. Микроскоп Zeiss EVO 40, напыление углеродом. FIG. 11. Imagines of glochidia hooks with different brightness and contrast value (Anemina arcaeformis, Sita River, Khabarovsk Krai). A. Optimal brightness and contrast. B. Excessive contrast. C. Excessive brightness. Scale bars 20 μm. Zeiss EVO 40 microscope, sputter coating with carbon.
Extracts from Cariola, 2014, Imagin Cogn Pers
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iMAGINE UC4 - Segmented oil spills
<p>Segmented oil spills in shapefile format</p> <p>Temporal coverage: 12/05/2019 - 18/10/2022</p> <p>Spatial coverage: global</p> <p> </p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4b. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>d et al., 2011). Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue.</p>
Figure 3 in The northernmost European record of Parasetodes respersellus Trichoptera: Leptoceridae) from the Courish Spit (Russia) with notes on its distribution and imaginal morphology
Figure 3. Parasetodes respersellus, male genitalia, A: dorsal view; B: ventral view; C: phallus, lateral view.
Figure 6 in New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae)
Figure 6. Imaginal phenology of Arctia tundrana based on the long-term collection data of 1904-2019 (Bolotov et al. 2015: Table S2; this study: Table 1). Specimens with uncertain collecting dates were excluded from the data set.
Figure 2 in New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae)
Figure 2. Female specimens of Arctia tundrana. (A) Near Bugrino village, Kolguev Island [locality no. 1; see Fig. 4], 26.vii.2009 [coll. RMBH]. (B) Near Seyakha village, Yamal Peninsula [locality no. 49; see Fig. 4 and 5D], 31.vii.2014 [coll. RMBH]. (C) Agapa River, Taymyr [locality no. 17; see Fig. 4], 12.vii.1976 [coll. ZMMU]. Scale bar = 10 mm. (Photos: Elisaveta A. Spitsyna and Vitaly M. Spitsyn).
Figure 1 in New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae)
Figure 1. Male specimens of Arctia tundrana. (A) Shokalsky Island [locality no. 48; see Figs. 4 and 5C], 06.viii.2019 [coll. RMBH]. (B) Tareya village, Taymyr [locality no. 12; see Fig. 4], vii.1967 [coll. ZMMU]. (C) Agapa River, Taymyr [locality no. 17; see Fig. 4], 23.vii.1973 [coll. ZMMU]. (D) Kresty village, Taymyr [locality no. 14; see Fig. 4], 12.vii.1976 [coll. ZMMU]. (E) Uelen settlement, Chukotka [locality no. 51; see Fig. 4], 19.vii.1979 [coll. ZMMU]. (F- H) Naukan settlement, Chukotka [locality no. 51; see Fig. 4], 14.vii.1979 [coll. ZMMU]. Scale bar = 10 mm. (Photos: Elisaveta A. Spitsyna and Vitaly M. Spitsyn).
Figure 5 in New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae)
Figure 5. Habitats of Arctia tundrana in Northeastern Europe (A-B) and Western Siberia (C-D). (A) Bog with lichens, mosses, and cottongrasses near Bugrino village, Kolguev Island [locality no. 1]. (B) Mountain dry willow-grass tundra near Amderma village, Yugor Peninsula [locality no. 2]. (C) Wet tundra, Shokalsky Island [locality no. 48]. (D) Nival grass-herb meadow on a southern slope of a hill near Seyakha village, Yamal [locality no. 49]. The numbers of localities correspond to those in the map (Fig. 4). (Photos: Boris Y. Filippov [A-B], Evgeny S. Babushkin [C], and Natalia A. Zubrii [D]).
Figure 4 in New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae)
Figure 4. Updated map of Arctia tundrana (Palearctic species) and A. subnebulosa (Nearctic species) occurrences (earlier version of this map is presented in Bolotov et al. 2015: Fig. 1A). The red circles indicate new occurrences of A. tundrana (Table 1). The yellow circles indicate A. tundrana occurrences published by Bolotov et al. (2015). The violet circles indicate occurrences of A. subnebulosa, a Nearctic vicariate of A. tundrana (after Bolotov et al. 2015). The numbers of localities correspond to those in Table 1 (this study) and Table S2 (Bolotov et al. 2015: Online Resource 1).
Figure 3 in New occurrences, morphology, and imaginal phenology of the rarest Arctic tiger moth Arctia tundrana (Erebidae: Arctiinae)
Figure 3. Male paratype of Arctia tundrana [coll. ZMMU]. (A) Dorsal view of the specimen with corresponding labels. The geographic label [in Russian] reads as follows: "Chukotka, Koluchinskaya Bay , Belyak's Sandspit, 17.vii.1988, P. Tomkovitch leg." [locality no. 24; see Fig. 4] (B) Genitalia and aedeagus. (Photos: Vitaly M. Spitsyn).
DroughtScope dataset for AI4EO IMAGIN-e Challenge
<p>Climate change poses new challenges to the food industry, which is required to optimise production in terms of quantity and quality of products against reduced water and land availability due to increased periods of drought and urbanisation. The availability of accurate and real-time information about ET is expected to improve decision-making concerning the optimisation of agricultural practice by properly allocating the resources needed. In this regard, the idea of using real-time ET estimates has a significant societal value, as it contributes to <strong>sustainable agriculture</strong>, <strong>food security</strong>, <strong>water conservation</strong>, and <strong>climate change adaptation</strong>. The envisioned business model includes data-driven agricultural consulting services, a subscription-based platform and partnerships with agritech companies.<br> Evapotranspiration (ET) data helps irrigation planning and is a powerful tool for managing land and water resources. Water scarcity makes knowledge of crop water consumption essential for water budgeting of connected ecosystems like agriculture, industry and cities. In a context in which severe droughts induce farmers to change crop typology to face reduced water availability, even in areas historically characterised by water abundance, offline processing of satellite data can result in delayed or sub-optimal decision-making. Timely information about the status of the cultivations can dramatically reduce water consumption, leading to cost savings, increased quality and quantity of food production and profitability. In this context, exploiting pre-trained models is an enabling factor for onboard processing, which is not feasible for direct retrieval of ET from measured brightness temperature via computationally expensive radiative transfer models.<br> This project aims to develop a decision support system helping public and private agrifood decision-makers optimise water resource management. Our solution provides <strong>early warnings</strong> about <strong>crop water stress</strong> based on the estimate of the evaporative stress index (<strong>ESI</strong>), which is one of the most important indicators of stress conditions identified in the literature.<br> <strong>Main innovations</strong></p> <ul> <li>Original deep learning architecture to infer information typically contained in the thermal domain from the near-infrared one</li> <li>Real-time onboard estimates thanks to lightweight processing</li> <li>Multi-task architecture for ESI estimation and LULC mapping</li> </ul> <p><strong>Benefits for users/stakeholders</strong></p> <ul> <li>Optimization of water resources management to tackle increasing water scarcity</li> <li>Possibility to implement local actions based on daily warning maps</li> <li>Optimization of food production</li> <li>Reduction of costs and resources employed for field monitoring</li> </ul> <p><strong>Benefits for the scientific community</strong></p> <ul> <li>Availability of new data with better resolution of the ones today available provided by the ECOSTRESS mission</li> <li>Daily LULC maps to tackle with high-dynamic phenomena (river mobility, sedimentation, deforestation)</li> </ul> <p><strong>Data</strong><br> This research exploits L1C data from the <strong>IMAGIN-e hyperspectral</strong> (HS) sensor, which features 50 spectral bands within the range [450,950] nm with a spatial resolution of 45 metres. The primary data sources are integrated with auxiliary data useful for classifying agricultural lands and estimating Evapotranspiration (ET).<br> To this end, we use the <strong>ESA WorldCover 2021</strong> dataset (<a href="https://worldcover2021.esa.int/">https://worldcover2021.esa.int/</a>) as the ground truth for Crop Classification; the original dataset at 10m spatial resolution is resampled at 45 meters with a max-voting strategy. Finally, a binary map is obtained in the code, considering only the Cropland values as 1 and 0 otherwise.<br> For ET training, we have considered <strong>ECOSTRESS</strong> data as a reference. ECOSTRESS provides ET data based on the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) method, with a spatial resolution of approximately 70 metres, which will be rescaled to 45 metres for consistency with input HS data.<br> The patches (14) included in the dataset are a stack of all these data. So we have 54 channels in this order:</p> <ul> <li>1:50: hyperspectral bands</li> <li>51: ESA WorldCover 2021 at 45m</li> <li>52: Evaporative Stress Index (ESI)</li> <li>53: Quality Control Band</li> <li>54: Evapotranspiration</li> </ul> <p>In the data folder are also available original ESI and LULC data.</p>
Multi-surveyor capture-mark-recapture as a powerful tool for butterfly population monitoring in the pre-imaginal stage
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Re-imagining fMRI for awake behaving infants
<p>Thousands of functional magnetic resonance imaging (fMRI) studies have provided important insight into the human brain. However, only a handful of these studies tested infants while they were awake, because of the significant and unique methodological challenges involved. We report our efforts to address these challenges, with the goal of creating methods for awake infant fMRI that can reveal the inner workings of the developing, preverbal mind. We use these methods to collect and analyze two fMRI datasets obtained from infants during cognitive tasks, released publicly with this paper. In these datasets, we explore and evaluate data quantity and quality, task-evoked activity, and preprocessing decisions. We disseminate these methods by sharing two software packages that integrate infant-friendly cognitive tasks and eye-gaze monitoring with fMRI acquisition and analysis. These resources make fMRI a feasible and accessible technique for cognitive neuroscience in awake and behaving human infants.</p>
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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.