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4,325 results for “real”
Analysis of the root diameter distribution from time series images of real and simulated Cassava root systems
<p>The data was collected, simulated and analyzed in the framework of the CassavaStore project (a collaboration between IBG-2, Forschungszentrum Jülich, Germany and different institution from Thailand; for details see <a href="https://www.international-bioeconomy.org/cassavastore_eng">https://www.international-bioeconomy.org/cassavastore_eng</a>). Aim of this project is to get a better understanding of storage root development in cassava (<em>Manihot esculenta</em> Crantz) in order to optimize cassava growth with respect to variety breeding and growth management. The storage root is one of the main providers of starch in Thailand and therefore of high economic importance. Monitoring the formation of storage roots over time via quantification of the root diameter distribution of excavated root systems was one of the key aspects addressed in this project. To measure the diameters a software was developed that identifies roots in RGB images and analyzes the diameters along each identified root automatically. The published data contains 1) analyzed images from cassava roots that were acquired in a video box; 2) simulated virtual root model images with known root diameter distributions that were used to validate the analysis approach; 3) a description of the data and the folder structure.</p>
Dataset used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study."
<p>This dataset repository includes eight raster layers (Reference System EPSG:3035 - ETRS89-extended / LAEA Europe), used in the study "Residential buildings real estate values linked to summer surface thermal anomaly patterns and urban features: the Florence (Italy) case study", and obtained by the adaptation of analyses carried out by previous studies (Morabito et al., 2021; Guerri et al., 2021; 2022).</p> <p>Further information regarding the source, study period, and horizontal resolution is available in the attached text file. </p> <p> </p> <p><strong><em>References</em></strong></p> <p>Guerri, G., Crisci, A., Congedo, L., Munafò, M., Morabito, M., <strong>2022</strong>. A functional seasonal thermal hot-spot classification: Focus on industrial sites. Science of The Total Environment 806, 151383.<a href="http://https://doi.org/10.1016/j.scitotenv.2021.151383"> https://doi.org/10.1016/j.scitotenv.2021.151383</a>.</p> <p>Guerri, G., Crisci, A., Messeri, A., Congedo, L., Munafò, M., Morabito, M., <strong>2021</strong>. Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers. Remote Sensing 13, 538. <a href="https://doi.org/10.3390/rs13030538">https://doi.org/10.3390/rs13030538</a>.</p> <p>Morabito, M., Crisci, A., Guerri, G., Messeri, A., Congedo, L., Munafò, M., <strong>2021</strong>. Surface Urban Heat Islands in Italian Metropolitan Cities: Tree Cover and Impervious Surface Influences. Science of The Total Environment 751, 142334. <a href="https://doi.org/10.1016/j.scitotenv.2020.142334">https://doi.org/10.1016/j.scitotenv.2020.142334</a>.</p>
Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers"
<p>Data supporting "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers" by Piccoli et al. (2021) Agronomy 2021, 11, 1472. https://doi.org/10.3390/agronomy11081472</p>
Madrid Real State Pricing
<p><strong>The fields presents in the dataset are the following</strong></p> <ul> <li><strong>price: Accommodation price (in Euros).</strong></li> <li><strong>bedrooms: Number of Bedrooms.</strong></li> <li><strong>bathrooms: Number of Bathrooms.</strong></li> <li><strong>sqm: Squared meters of the accommodation.</strong></li> <li><strong>floor: Floor number.</strong></li> <li><strong>type: Accommodation type. </strong></li> <li><strong>orientation: Orientation of the accommodation.</strong></li> <li><strong>condition: Condition of the accommodation.</strong></li> <li><strong>hot water: The way to obtain hot water.</strong></li> <li><strong>elevator: If is present a elevator in the building.</strong></li> </ul>
Full data set for "Real-Space Observation of Fluctuating Antiferromagnetic Domains" article in Science Advances
<p>This is the full data set for the article, sufficient to reproduce any results shown. The data and data files description, and data retrieval instructions are in the Readme.txt file. </p>
Real-Life Indoor Sound Event Dataset (ReaLISED) for Sound Event Classification (SEC)
<p>The Real-Life Indoor Sound Event Dataset (ReaLISED) offers the scientific community the possibility of testing Sound Event Classification (SEC) algorithms with new real indoor audio event recordings. The full set is made up of 2479 sound recordings of 18 events. The 18 event classes are the following: beater, cooking, cupboard/wardrobe, dishwasher, door, drawer, furniture movement, microwave, object falling, smoke extractor, speech, switch, television, vacuum cleaner, walking, washing machine, water tap, and window. There are 2479 clips of isolated sounds, which result in 3624.51 seconds. The number of events in each class is between 104 for the "Window" class and 190 for the “Speech” class, with a mean value of 138 events and a standard deviation of 25.</p> <p>Four Olympus LS-100 recorders were used. The sampling frequency was set to 44.1 kHz and 24 bits per sample. The stereo mode was used, and a medium sensitivity of the microphone was set. The distance between the recorder and the sound source was set to approximately 30-40 cm.</p> <p>Apart from the labels related to the class of event, extra information for each recording is provided in order to be exploited if necessary in the future, with other research purposes. This extra information completes the description of the sound source.</p> <p>The dataset is introduced to the scientific community by providing all the .flac files which composed it. The name of the files is built with 5 pieces of information, separated with underscores (“_”), with the format “abc_123_45_67_8.flac′′:</p> <ul> <li> <p>“abc”: the first three letters indicate the source that produces the sound. This segment can take 18 different values: ‘bea’ (beater), ‘coo’ (cooking), ‘cup’ (cupboard/wardrobe), ‘dis’ (dishwasher), ‘doo’ (door), ‘dra’ (drawer), ‘fur’ (furniture movement), ‘mic’ (microwave), ‘obj’ (object falling), ‘smo’ (smoke extractor), ‘spe’ (speech), ‘swi’ (switch), ‘tel’ (television), ‘vac’ (vacuum cleaner), ‘wal’ (walking), ‘was’ (washing machine), ‘wat’ (water tap), win’ (window).</p> </li> <li> <p>“123”: this set of digits identifies the event among the number of events produced by the source identified with “abc”. This segment can take all the values between ‘001’ and ‘190’, which is the maximum number of events of a particular class we can find in the dataset (speech).</p> </li> <li> <p>“45”: this set of digits identifies the action that produce the sound. This segment can take 11 different values: ‘01’ (close), ‘02’ (open), ‘03’ (throw), ‘04’ (turn on), ‘05’ (turn off), ‘06’ (move), ‘07’ (plug), ‘08’ (unplug), ‘09’ (raise), ‘10’ (lower), and ‘00’ (there is no information about the action).</p> </li> <li> <p>“67”: this set of digits identifies the material the sound source is made of. This segment can take 14 different values: ’01’ (wood), ’02’ (glass), ’03’ (metal), ’04’ (plastic), ’05’ (ceramic), ’06’ (synthetic), ’07’ (cardboard), ’08’ (marble), ’09’ (floating platform), ’10' (platelet), ’11’ (wicker), ’12’ (carpet), ’13’ (medium-density fibreboard MDF), and ’00’ (there is no information about the material).</p> </li> <li> <p>“8”: the last digit gives approximate information about the intensity of the recorded sound. It can take 4 different values: ’1’ (low intensity), ’2’ (medium intensity), ’3’ (high intensity), ’0’ (there ir no information about the intensity).</p> <p>For clarity, some examples of audio file names with this code are shown hereunder:</p> </li> <li> <p>“doo_040_02_00_3.flac” is the name of the 40th file in the Door class, described as “opening a door of unknown material with high intensity”.</p> </li> <li> <p>“fur_058_06_01_2.flac” is the name of the 58th file in the furniture movement class, described as “moving a wooden furniture with medium intensity”.</p> </li> <li> <p>“vac_001_00_00_0.flac” is the name of the 1st audio file in the vacuum cleaner class, described as “using the vacuum cleaner, without information about the action, neither the material or the intensity”.</p> </li> </ul>
Dataset for the paper "Real-Time in Situ Monitoring of CO2 Electroreduction in the Liquid and Gas Phases by Coupled Mass Spectrometry and Localized Electrochemistry" DOI: 10.1021/acscatal.2c00609
<p>The data in the attached spreadsheet was used to produce the figures in the paper</p> <p>Authors:Guohui Zhang, Youxin Cui, Anthony Kucernak</p> <p>Title:Real-Time in Situ Monitoring of CO2 Electroreduction in the Liquid and Gas Phases by Coupled Mass Spectrometry and Localized Electrochemistry</p> <p>Journal:ACS Catalysis</p> <p>DOI:10.1021/acscatal.2c00609</p> <p>Please cite the above reference if you wish to use this data</p> <p>DOI of data:10.5281/zenodo.6526651</p>
Dolutegravir in Real Life: self-reported mental and physical health outcomes after transitioning from efavirenz- to dolutegravir-based antiretroviral therapy in a prospective cohort study in Lesotho
<p>Pseudonymized dataset for the accepted manuscript "<em>Dolutegravir in Real Life: self-reported mental and physical health outcomes after transitioning from efavirenz- to dolutegravir-based antiretroviral therapy in a prospective cohort study in Lesotho</em>"</p>
A real-time feedback system stabilises the regulation of worker reproduction under various colony sizes
<p>Based on individual trait expression, an agent-based simulation was used to identify an explicit mechanism for understanding colony size dependent behaviour. This is the code for and data from the agent-based simulation</p>
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time
<p>This RGB-D dataset is part is part of our publication</p> <p>Heindl, Christoph, et al. "Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time." <em>Tenth International Conference on Machine Vision (ICMV 2017)</em>. Vol. 10696. SPIE, 2018.</p> <p>Our capture setup consists of a RGB-D sensor looking towards a known planar object. The sensor is coupled with an electronic linear axis to adjust distance. We captured data at distances [40cm, 90cm, 10cm steps] in the temperate range of [25°C, 35°C, 1°C steps]. At each temperature/distance tuple we grabbed 50 images from both RGB and IR (aligned with RGB) sensors. We then created an artificial depth map for all RGB images utilizing the known calibration target in sight.</p> <p>For more information visit https://github.com/cheind/rgbd-correction</p>
A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems
<p>Sequence alignment is a fundamentally memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory architectures alleviate this bottleneck by providing the memory with computing competencies. We propose Alignment-in-Memory (AIM), a framework for high-throughput sequence alignment using processing-in-memory, and evaluate it on UPMEM, the first publicly-available general-purpose programmable processing-in-memory system.</p>
Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"
<p>Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"</p> <p> </p> <p>Please find below an explanation for the <strong>files </strong>in this repository:</p> <p><br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong></p> <p>Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset"</p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>03_ExampleMeasurement.zip</strong></p> <p>One measurement file and a corresponding scatterplot</p> <p><strong>04_Dataset_load.zip</strong></p> <p>The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv".</p> <p><strong>05_RF_training</strong></p> <p>Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv").</p> <p><strong>07_pytranskit</strong></p> <p>Scripts for training and evaluating CDT-PLDA classifier</p> <p> </p> <p> </p>
Real-time magnetic resonance imaging to study orthostatic intolerance mechanisms in human beings: Proof of concept
<p>This dataset was acquired at DLR, Cologne, Germany. The study complied with the Declaration of Helsinki, and was approved by the local ethics committee. All subjects gave written informed consent.</p> <p> </p> <p><strong>Data acquisition</strong><br> <em>MRI data</em></p> <p>All MR images were acquired on a Siemens 3T mMR Biograph using cardiac, spine and head coils. The scanning protocol consisted of the following sequences.</p> <ul> <li><strong>MPI70_SA20_FOV320_16X6MM_33MS</strong>: cardiac real-time MRI of the short axis (TR=2.56 ms; TE=1.62 ms; FA= 10°; 1.6x1.6 mm; 6 mm slice thickness; 20 slices; FoV320x320 mm; radial spokes 13)</li> <li><strong>MPI70_TP_PCMV100_FOV320_15X6MM_33MS</strong>: blood flow real-time MRI of the pulmonary trunk (TR=3.33 ms; TE=2.24 ms; FA= 10°; 1.5x1.5 mm; 6 mm slice thickness; FoV=320x320 mm; VENC=100 cm/s; radial spokes 5)</li> <li><strong>MPI70_PCMV100_FOV192_075X6MM_80MS:</strong> blood flow real-time MRI of the middle cerebral artery (TR=4.44 ms; TE=3.10 ms; FA= 12°; 0.75x0.75 mm; 6 mm slice thickness; FoV=192x192 mm; VENC=100 cm/s; radial spokes 9)</li> </ul> <p> </p> <p>Cinematic real-time MRI of the short axis view and blood flow measurements of the pulmonary trunk and in the left and right middle cerebral artery were acquired at baseline and during -30mmHg LBNP.</p> <p> </p> <p>Video files:</p> <p><a href="https://zenodo.org/record/7066642/files/Series_040_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1">https://zenodo.org/record/7066642/files/Series_040_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1</a></p> <p><a href="https://zenodo.org/record/7066642/files/Series_103_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1">https://zenodo.org/record/7066642/files/Series_103_mpi70_SA20_FOV320_16x6mm_33ms_Slice_14.wmv?download=1</a></p> <p> </p> <p> </p>
Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device"
<p><strong>Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device".</strong></p> <pre>Code to analyze data produced by the Quantum-Si benchtop device and semiconductor chip is provided in a Python library <strong>qsi_algo</strong> under several submodules: - <strong>rs_caller.py</strong>: Algorithm for calling RS segments (also called ROI segments throughout code). - <strong>rs_caller_controller.py</strong>: Code framework for executing RS calling and property computation in a distributed manner - <strong>rs_properties</strong>: Code for computing properties of identified RS - <strong>rs_classifier</strong>: Algorithms for identifying peptide states (i.e. residue calls) associated with an RS - <strong>utils.py</strong>: shared helper code - <strong>pulse_reader</strong>: reader for binary pulse file - <strong>filters</strong>: ROI and pulse filtering utilities - <strong>plotting</strong>: functions for visualization of data relevant to the analyses presented Jupyter notebooks (<strong>.ipynb</strong>) files are named according to the manuscript figure they are associated with. Analysis code inside uses provided RS (recognition segment) data to demonstrate filtering and residue-calling techniques required to replicate analyses shown in manuscript figures. Please note: several methods rely on randomization for model initialization and/or data sampling which can cause small deviations from equivalent analyses in published figures. The raw data produced from the Quantum-Si benchtop device and semiconductor chip for the assays presented in the accompanying study is presented in a pulse-called binary file format. Pulses can be used as input for RS identification and peptide state identification. Pre-segmented (RS-identified) files are included for convenience. The data contained in the files include: <strong>{run_id}.bin</strong>: Binary format for storing pulse info. The reader provided in <strong>qsi_algo.pulse_reader</strong> produces the following columns: - <strong>aperture_index</strong>: unique aperture index on chip - <strong>start_f</strong>: index of first frame in pulse, counted from the beginning of the run - <strong>end_f</strong>: index of last frame in pulse, counted from the beginning of the run - <strong>dur_f</strong>: duration of pulse in frames - <strong>dur_s</strong>: duration of pulse in seconds - <strong>ipd_f</strong>: interpulse duration in frames (number of frames since end of preceding pulse) - <strong>ipd_s</strong>: interpulse duration in seconds (time in seconds elapsed since end of preceding pulse) - <strong>snr</strong>: signal-to-noise ratio (bin1_intensity / bin1_bg_std) - <strong>intensity</strong>: intensity of pulse (counts above baseline in bin1) - <strong>bin0_intensity</strong>: counts above baseline in bin0 - <strong>intensity_display</strong>: bin1_intensity + bin1_bg_mean - <strong>binratio</strong>: bin0_intensity / bin1_intensity - <strong>bg_mean</strong>: bin1 background mean in region of pulse - <strong>bg_std</strong>: bin1 background standard deviation in region pulse - <strong>bin0_bg_mean</strong>: bin0 background mean in region of pulse - <strong>bin0_bg_std</strong>: bin0 background standard deviation in region pulse <strong>{run_id}.csv.gz</strong>: Compressed comma-separated value file containing RS/ROI properties computed from raw pulses.bin file by included RS caller (example in <strong>rs_caller.py</strong>). - <strong>ap</strong>: unique aperture index on chip - <strong>ROI</strong>: ordinal ROI number in the aperture, 0-indexed - <strong>start_p</strong>: index (.loc) of first pulse in the ROI (inclusive) in pulse dataframe - <strong>end_p</strong>: index (.loc) of last pulse in the ROI (inclusive) in pulse dataframe - <strong>start_f</strong>: first frame of the first pulse in the ROI (inclusive) - <strong>end_f</strong>: Last frame of the last pulse in the ROI (exclusive) - <strong>start_s</strong>: Time (in seconds elapsed from beginning of run) of the start of the ROI - <strong>end_s</strong>: Time (in seconds elapsed from beginning of run) of the end of the ROI - <strong>dur_f</strong>: Duration in frames of the ROI - <strong>dur_s</strong>: Duration in seconds of the ROI - <strong>num_pulses</strong>: Number of pulses in the ROI (that also passed filtering during ROI-calling) - <strong>pw_mean</strong>: Mean pulse duration (in seconds) of pulses in the ROI - <strong>ipd_mean</strong>: Mean inter-pulse duration (in seconds) of pulses in the ROI - <strong>snr_mean</strong>: Mean signal-to-noise ratio of pulses in the ROI - <strong>intensity_mean</strong>: Mean intensity above baseline of pulses in the ROI - <strong>binratio_norm</strong>: Estimated pulse bin ratio of pulses in the ROI, according to the following equation: sum(bin0_intensity*dur_f) / np.sum(bin1_intensity*dur_f) - <strong>ROI_score</strong>: ROI quality score (0-1 from least to most likely to contain recognizer-peptide recognition pulsing) - <strong>binratio_skew</strong>: bin ratio correction factor accounting for binning signal timing differences across the chip. This factor has already been applied to the binratio_norm column</pre>
Real-time imaging of thermally induced microcracks in granite with ultrahigh-temperature instrument
<p>An ultrahigh-temperature heating platform (HS1400G, Instec, Britain) on the optical microscope (BX51M, Olympus, Japan) was developed to observe the microcrack propagation of thin-section samples during heating in real time.</p> <p>Singapore Bukit Timah granite rock cores were examined in this study. The granite rock is widespread and one of the major rock formations in Singapore. It shows significant mechanical deterioration under the influence of heat. The rock sample was prepared as doubly polished thin sections of approximately 60 μm in thickness for petrographic analysis and microthermometric observation. Before the experiment, the slide was heated with an alcohol lamp to dissolve the resin, and the thin-slice sample was removed and immersed in acetone to wash the surface glue to avoid affecting the observation.</p> <p>The granite sample was heated in the following heating process: heat from room temperature of 25℃ to 100℃ at a rate of 20℃/min; maintain the temperature for 5 min, as the thin-section sample is sufficiently small to generate a homogeneous thermal field quickly; heat to 100℃, 200℃, 300℃, 400℃, 500℃, 600℃, 700℃, 800℃ and 900℃ successively; finally, reduce the temperature to room temperature at a cooling rate of 20℃/min from the maximum temperature.</p>
Energy consumption, execution time and fail requests rate of a proactive energy-aware auto-scaling solution for edge-based infrastructures applied to real-world workload.
<p>Spreadsheet of the results obtained with our horizontal auto-scaling proposal presented in "A proactive energy-aware auto-scaling solution for edge-based infrastructures". In that research, we present a proactive horizontal auto-scaling framework for edge infrastructures, which considers both the base (idle) and dynamic (due to application execution) energy consumption of edge nodes and the node scaling mechanism. Simulations were performed with the EdgeCloudSim simulator with a workload provided by Shanghai Telecom and the results show up to a 92.5% decrease in energy consumption, a failed request rate of up to 0%, and reasonable execution times of the auto-scaling process for different problem sizes.</p> <p>Proactive auto-scaling mechanisms in edge-based infrastructures can anticipate user service requests by allocating computing resources while supporting the quality of service needed by a vast range of applications requiring, e.g., a low latency or response time. </p> <p>This work is supported by the European Union's H2020 research and innovation program under grant agreement DAEMON 101017109 and by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 (MCI/AEI) and RHEA P18-FR-1081.</p>
Reale Skulptur
**Die reale Skulptur wurde 1999 in einer Open-Air-Vorstellung im Französischen Garten in Celle gezeigt | The real sculpture was shown in an open-air performance in Celle's Französischer Garten (French Garden) in 1999** Weitere Informationen: Die Skulptur besteht aus diversen geometrischen Figuren und stehen im deutlichem Kontrast zur der Natur, sowie aus dem immer wieder überraschenden vielfältigen Industriematerial Stahl. | Further information: The sculpture consists of various geometric figures and stands in clear contrast to nature, as well as of the ever surprisingly diverse industrial material steel. Aufnahmen von | captured by Luisa, Jule, Amelie, Juliane & Emma (28.03.2022) Dank an | Thanks [kulturschokolade.org](https://sketchfab.com/3dtwins) für das gemeinsame Projekt! Source: Objaverse 1.0 / Sketchfab
Ánade real (Anas platyrhynchos)
**Ejemplar:** *Anas platyrhynchos* **Nombre común**: Ánade real, ànec collverd **Descripción:** Cráneo largo, ancho, plano y redondeado. Macho 10 meses. Alimentación plantas acuáticas, y también pequeños invertebrados y peces. Presenta un marcado dimorfismo sexual. Altura (5 cm), anchura (7 cm) y profundidad (16 cm). **Sigla museo, colección y entidad:** VER0000441. Colección del Dpto. de Zoología (Facultad Ciencias Biológicas). MUVHN. **Técnica digitalización / modelo**: escaneado superficial, escáner 3D Einscan Pro **Software empleado**: einscan v3.1.0.2, modo manual con plataforma giratoria, calidad media **Autor digitalización:** Natalia Conejero-Ortega **Cita ejemplar:** modelo 3D Colección cráneos de aves del Dpto. de Zoología. Museo Universitat de València de Historia Natural (MUVHN).  VER0000075. Ejemplar taxidermizado. Colección Histórica de Aves MUVHN. 1932. Source: Objaverse 1.0 / Sketchfab
Estatua de San Tirso el Real, Oviedo
Estatua que refleja la imagen de San Tirso, santo que fue militar romano y que fue martirizado en el siglo III. Se encuentra en la fachada de la Iglesia de San Tirso el Real, en la Plaza de la Catedral de Oviedo, templo originalmente prerrománico, mandado construir por Alfonso II el casto, dentro del conjunto palaciego. Sólo conserva el testero, que tenemos también modelado en 3D. La figura está bastante erosionada, al encontrarse expuesta a los elementos atmosféricos de forma directa. Source: Objaverse 1.0 / Sketchfab
Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"
<p>To cite it:</p> <p><code>@misc{bfdataset,<br> author = {Martin Monperrus},<br> title = {Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"},<br> year = 2017,<br> doi = {10.5281/zenodo.1004734},<br> url = {https://doi.org/10.5281/zenodo.1004734}<br> }</code></p> <pre> </pre> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.