Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Mapping the core of the Tarantula Nebula with VLT-MUSE. III. A template for metal-poor starburst regions in the visual and far-ultraviolet

Cumulative optical (VLT/MUSE) and far-ultraviolet (mix of HST empirical and ULLYSES templates) spectrum of NGC2070 (2x2 arcmin^2) presented in Figures 2 and 4, respectively, of Crowther & Castro (MNRAS in press, https://arxiv.org/abs/2311.07642) which should be cited if either dataset is used. Contents:MUSE.dat (ascii format, column 1 wavelength in Angstrom, column 2 flux in erg/s/cm^2/Ang). Further details of MUSE dataset is described in N. Castro et al. (2018 A&A 614 A147)ULLYSES.dat (ascii format, column 1 wavelength in Angstrom, column 2 flux in erg/s/cm^2/Ang, some detector gaps). Further details of ULLYSES survey is described in R. Roman-Duval et al. (2020, Research Notes of AAS, 4, 205)

opencc-by-4.0Jan 2017View details →

MUSES - Infographics

Infographics from the MUSES project

opencc-by-4.0Aug 2024View details →

Investigating the asymmetry of young stellar outflows: A combined MUSE-X-Shooter study of the Th 28 jet

<h3>This record contains supplementary tables and figures for the article 'Investigating the asymmetry of young stellar outflows: A combined MUSE-X-Shooter study of the Th 28 jet' by A. Murphy, E. T. Whelan, F. Bacciotti, D. Coffey, F. Comeron, J. Eisloffel, B. Nisini, S. Antoniucci, J. M. Alcala and T. P. Ray, accepted for publication by Astronomy & Astrophysics.</h3>   Abstract: Characterising stellar jet asymmetries is key to providing robust constraints for jet launching models, and hence to understanding the underlying mechanisms of jet launching. This study aims to characterise the asymmetric properties of the bipolar jet from the Classical T Tauri Star Th 28. We combine data from integral field spectroscopy with VLT/MUSE and high-resolution spectra from VLT/X-Shooter to map optical emission line ratios in both jet lobes. We carry out a diagnostic analysis of these ratios to compare the density, electron temperature, and ionisation fraction within both lobes. The mass accretion rate is derived from the emission lines at the source, and compared with the mass outflow rate derived in both lobes using the estimated densities and measured [O I]λ6300 and [S II]λ6731 luminosities. The blue-shifted jet shows a significantly higher electron temperature and moderately higher ionisation fraction than the red-shifted jet. In contrast to previous studies we also estimate higher densities n H in the blue-shifted jet by a factor &sim;2. These asymmetries are traced to within 1′′ (160 au) of the source in the line ratio maps. We find Ṁacc = 2.4 × 10^−7 M⊙ yr ^−1 , with an estimated obscuration factor of &sim;54 due to grey scattering around the star. Estimated values of Ṁout range between 0.66 – 13.7 × 10^−9 M⊙ yr^−1 in the blue-shifted jet and 5-9 × 10^−9 M⊙ yr^−1 in the red-shifted jet. The emission line maps and diagnostic results suggest that the jet asymmetries originate close to the source and are likely intrinsic to the jet. Furthermore, the combined dataset allows access to a broad array of accretion tracers. This in turn enables a more accurate estimation of the mass accretion rate, revealing Ṁacc higher by a factor > 350 than would otherwise be determined.   Summary of supplemental material: Table 1: Emission lines detected in X-shooter observations of the jet. Fluxes are measured from the red-shifted jet lobe. Tables 2 and 3: Mass accretion rates measured from MUSE and X-Shooter observations of Th 28, respectively, assuming an on-source extinction of 2.5 mags. Tables 4 and 5: As in Tables 2 and 3, for an on-source extinction of 1.26 mags. Figures 1-4: Position-velocity maps of detected emission lines from the UV and VIS arms of the X-shooter observations. Figure 5: Accretion luminosities measured from MUSE and X-Shooter data, before and after correction for on-source obscuration. Left and right panels show the corresponding values if the fluxes are corrected for a wavelength-dependent extinction of 2.5 and 1.26 mags, respectively. <div> </div> <div> </div>

opencc-by-4.0Jul 2021View details →

Fornax3D Planetary Nebulae Catalouge: MUSE emission residual cubes

The residual data cubes that contain the residual emission lines, focused on the [OIII] 5007 Angstrom region, resulting from passing MUSE data cubes through the GIST pipeline ( < v.2). Header contains all the information to run in the MUSE_PNe_fitting pipeline, which was developed to run on these residual data cubes. The full MUSE cubes can be obtained from the ESO data portal.

opencc-by-4.0Mar 2021View details →

Muse EEG Subconscious Decisions Dataset

The first Muse EEG Dataset for Subconscious Decision Making Study. Dataset Description: - 20 subject data - Different trials per each subject   The data provided at MUSE folder have the following variables:  <ul> <li>Timestamp: date and time with millisecond precision of the captured data. It is stored in the format YYYYY-MM-DD HH:mm:SS.fff, where YYYYY to the year, MM to the month, DD to the day, HH to the hour, mm to the minute, SS to the second and fff to the millisecond.</li> <li>Delta: brain waves with the largest wave amplitude, mainly active with deep sleep phases, so they are related to processes that do not depend on a state of consciousness. These waves have a frequency of between 1 and 4 Hz.</li> <li>Theta: the brain waves with the largest wave amplitude after theta waves, present in deep calm, relaxation and immersion stages in memories, so they are associated with a present consciousness but disconnected from reality and focused on imaginary experiences. These waves have a frequency of between 4 and 8 Hz.</li> <li>Alpha: the waves with the largest wave amplitude after Theta waves, present in stages of relaxation such as a walk or watching TV, and are therefore related to calm related to processes of deep calm with present awareness. These waves have a frequency of between 7.5 and 13 Hz.</li> <li>Beta: these are the lowest amplitude waves, after gamma waves, present in states that require a certain level of attention or alertness, in which one has to be aware of the changes These waves have a frequency of between 13 and 30 Hz.</li> <li>Gamma: these are the lowest amplitude waves, present in states of wakefulness, which are associated with a broadening of focus and memory management. These waves have a frequency between 30 and 44 Hz.</li> <li>Raw: these are the representation of the raw electrical signals captured by Muse.</li> <li>AUX_RIGHT: raw waveforms captured by an auxiliary USB sensor.</li> <li>Mellow: User relaxation.</li> <li>Concentration: User concentration.</li> <li>Accelerometer (X, Y, Z): detects device movements, tilts, tilts up, tilts down and tilts</li> <li>upwards, downwards and sideways.</li> <li>Gyro (X, Y, Z): gyroscope movement over time.</li> <li>HeadBandOn: indicates if the band is on the head.</li> <li>HSI: sensor quality, the closer to 1 the better the quality.</li> <li>Battery: remaining battery of the device.</li> <li>Elements: different actions that the subject can perform, such as blinking or jaw clenching.</li> </ul>   The data provided at LOCAL folder include the decision timing measures:   <ul> <li>ID: identifier of the participant in the experiment. The identifier is an integer, which starts at 0 and has consecutive values. In our case case, it will go up to 19.</li> <li>Trial: session of the experiment in which the results have been recorded. A session runs from the time the "Start Experiment" button is clicked until it appears again. The number of the session is an integer number starting at 0. It is an integer starting at 0 and has consecutive values. In our specific case, it will reach up to 9</li> <li>Response: identifier of each response of each session. A response is from the moment a blank screen is displayed until the letter displayed on the screen is chosen at the moment the impulse to press a key is felt. It is an integer starting at 0 and has consecutive values.ç</li> <li>Start time: the time at which the response starts, i.e. the time from when a blank screen appears until the decision-making process begins. The decision making process is initiated.</li> <li>Letter appearance time: the time at which random letters start to appear on the screen. From this point onwards, the participant can press the right or left key at any time he/she wishes.</li> <li>Time of the keystroke: the time at which the participant presses the P (right) or Q (left) key.</li> <li>Chosen key: choice made by the participant. It shall have as possible values p and q.</li> <li>Time of appearance of the observed letter: time at which the letter that the participant was asked to.</li> <li>The time of occurrence of the observed letter: time at which the letter appears that the user has been asked to remember at the time he/she makes the free will decision. It is interpreted as the time of the decision.</li> <li>Observed letter: response to the recall of the letter that appeared on the screen at the moment of feeling the free will impulse. It will have as possible values: S, R, N, D, L, C, T, M or #.</li> </ul>

opencc-by-4.0Aug 2020View details →

EmoKey Moments Muse EEG Dataset (EKM-ED): A Comprehensive Collection of Muse S EEG Data and Key Emotional Moments

EmoKey Moments Muse EEG Dataset (EKM-ED): A Comprehensive Collection of Muse S EEG Data and Key Emotional Moments   Dataset Description: The EmoKey Moments EEG Dataset (EKM-ED) is an intricately curated dataset amassed from 47 participants, detailing EEG responses as they engage with emotion-eliciting video clips. Covering a spectrum of emotions, this dataset holds immense value for those diving deep into human cognitive responses, psychological research, and emotion-based analyses. Dataset Highlights: <ol> <li>Precise Timestamps: Capturing the exact millisecond of EEG data acquisition, ensuring unparalleled granularity.</li> <li>Brainwave Metrics: Illuminating the variety of cognitive states through the prism of Delta, Theta, Alpha, Beta, and Gamma waves.</li> <li>Motion Data: Encompassing the device's movement in three dimensions for enhanced contextuality.</li> <li>Auxiliary Indicators: Key elements like the device's positioning, battery metrics, and user-specific actions are meticulously logged.</li> <li>Consent and Ethics: The dataset respects and upholds privacy and ethical standards. Every participant provided informed consent. This endeavor has received the green light from the Ethics Committee at the University of Granada, documented under the reference: 2100/CEIH/2021.</li> </ol> A pivotal component of this dataset is its focus on "key moments" within the selected video clips, honing in on periods anticipated to evoke heightened emotional responses. Curated Video Clips within Dataset: Film Emotion Duration (seconds) The Lover Baseline 43 American History X Anger 106 Cry Freedom Sadness 166 Alive Happiness 310 Scream Fear 395 The cornerstone of EKM-ED is its innovative emphasis on these key moments, bringing to light the correlation between distinct cinematic events and specific EEG responses. Key Emotional Moments in Dataset: Film Emotion Key moment timestamps (seconds) American History X Anger 36, 57, 68 Cry Freedom Sadness 112, 132, 154 Alive Happiness 227, 270, 289 Scream Fear 23, 42, 79, 226, 279, 299, 334 Citation: Gilman, T. L., et al. (2017). A film set for the elicitation of emotion in research. Behavior Research Methods, 49(6). <a href="https://doi.org/10.3758/s13428-016-0842-x">Link to the study</a> With its unparalleled depth and focus, the EmoKey Moments EEG Dataset aims to advance research in fields such as neuroscience, psychology, and affective computing, providing a comprehensive platform for understanding and analyzing human emotions through EEG data.   ——————————————————————————————————— FOLDER STRUCTURE DESCRIPTION ——————————————————————————————————— - questionnaires: all there response questionnaires (Spanish); raw and preprocessed Including SAM | ——preprocessed: Ficha_Evaluacion_Participante_SAM_Refactored.csv: the SAM responses for every film clip - key_moments: the key moment timestamps for every emotion’s clip - muse_wearable_data: XXXX | |—raw |——1: ID = 1 of subject |————muse: EEG data of Muse device |—————————ANGER_XXX.csv :  leg data of the anger elicitation |—————————FEAR_XXX.csv :  leg data of the fear elicitation |—————————HAPPINESS_XXX.csv :  leg data of the happiness elicitation |—————————SADNESS_XXX.csv :  leg data of the sadness elicitation |————order: film elicitation order of play: For example: HAPPINESS,SADNESS,ANGER,FEAR … | |—preprocessed |——unclean-signals: without removing EEG artifacts, noise, etc. |————muse: EEG data of Muse device |—————————0.0078125: data downsampled to 128 Hz from 256Hz recorded |——clean-signals: removed EEG artifacts, noise, etc. |————muse: EEG data of Muse device |—————————0.0078125: data downsampled to 128 Hz from 256Hz recorded The ethical consent for this dataset was provided by La Comisión de Ética en Investigación de la Universidad de Granada, as documented in the approval titled: 'DETECCIÓN AUTOMÁTICA DE LAS EMOCIONES BÁSICAS Y SU INFLUENCIA EN LA TOMA DE DECISIONES MEDIANTE WEARABLES Y MACHINE LEARNING' registered under 2100/CEIH/2021.

opencc-byDec 2016View details →

André Chénier Muse

André Chénier Muse (1888), Denys Pieere Puech (1854-1918), Marble, Musée du Luxembourg, Paris, The Royal Cast Collection (Copenhagen,Denmark). Made with Memento Beta (now ReMake) from Autodesk. The idea of the sculptor is to illustrate the death of the poet André Chénier, guillotined place de la Barrière on July 25, 1794. "... after the chopper felt, a white form appeared at the foot of the guillotine ... Little by little an outline became clearer and we saw a woman body (...) of a virgin.His hands she took the head with the black hair ... Then sitting down, and bringing his long hair on his chest , She deposited the dear head, which she kissed on the forehead, while she said in a breath: "Sleep well, dear poet, your muse will watch over your memory" (in a letter from Denys Puech to Chabrier in 1886). Thanks @Pattarrian for the reference! For more updates, please follow @Geoffrey.Marchal on Twitter. Source: Objaverse 1.0 / Sketchfab

opencc-byMay 2022View details →

Lamborghini Countach (MUSE Version)

A model of the 80's legend named the Lamborghini Countach LP500 S. The MUSE version is a version made by the band MUSE and they made the Countach almost like a Bill & Ted Lamborghini where it can travel between dimensions. Please like and remember all of my models will be downloadable, Thanks! Source: Objaverse 1.0 / Sketchfab

opencc-byMay 2021View details →

Render (Sleeping Muse)

Render (Sleeping Muse) by Realf Heygate 30 x 20 cm, oil on canvas, 2021 Source: Objaverse 1.0 / Sketchfab

opencc-byApr 2021View details →

Muse

Figure of a muse in the garden of Belvedere Palace. This muse is probably the Olympic muse Terpsichore. SShe is the muse of choral lyricism and dance. Source: Objaverse 1.0 / Sketchfab

opencc-byApr 2020View details →

Apollo and the nine Muses. 200-210 aD.

Roman sarcophagus with the representation of the god Apollo accompanied by the nine Muses. 200-210 aD. Carrara marble. Piece found in the Corpus Christi chapel of the Cathedral of Tarragona. Old refectory room of the Diocesan Museum of Tarragona. Cathedral of Tarragona. 208 x 65 x 12 cm The nine muses: 1. Clio: Discovered history and guitar. 2. Euterpe: Discovered several musical instruments, courses and dialectic. 3. Thalia: She was the protector of comedy. 4. Melpomene: Opposite from Thalia, Muse Melpomene was the protector of Tragedy. 5. Terpsichore: She was the protector of dance; she invented dances, the harp and education. 6. Erato: She was the protector of Love and Love Poetry. 7. Polymnia: She was the protector of the divine hymns and mimic art. 8. Ourania: She was the protector of the celestial objects and stars. 9. Calliope: She was the superior Muse. She was accompanying kings and princes in order to impose justice and serenity. http://museu.diocesa.arqtgn.cat/?lang=en Source: Objaverse 1.0 / Sketchfab

opencc-by-4.0Jul 2024View details →

MUSE Analysis of Gas around Galaxies (MAGG) -- VI. The cool and enriched gas environment of z≳3 Lyα emitters

Full sample of MgII absorption-line systems identified at z>3 in the MUSE Analysis of Gas around Galaxies (MAGG) survey presented in Galbiati et al. 2024. Each absorber has been modeled by a combination of Voigt profiles which are shown on top of the NIR quasar spectra obtained with X-shooter.

opencc-by-4.0Aug 2021View details →

MUSE: Multimodal Separators for Efficient Route Planning in Transportation Networks

This dataset was used in the experimental evaluation of the MUSE route planning algorithm. It encompasses the Ile-de-France multimodal network. The dataset contains: 1. The raw osm file of the Ile-de-France region from OpenStreetMap. 2. The raw GTFS data for the public transit network. 3. The multimodal graph based on 5 partitions: 100, 200, 300, 400, and 500 cells. 4. The Nondeterministic Finite Automata (NFA) used during the preprocessing and query stages of MUSE. 5. The graph overlay evaluated during the preprocessing stage of MUSE. To test MUSE and review the details of this dataset, please visit https://github.com/aminefalek/muse

opencc-by-4.0Dec 2022View details →

MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2019

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2019. Please <a href="https://zenodo.org/record/7485123#.Y6p12n1ByUl">click here</a> to download the MUSES LAI product in 2018, and click here to download the MUSES LAI product in 2020. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 1km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-by-4.0Dec 2022View details →

MUSES Leaf Area Index (LAI) 8-Day Global 1km SIN Grid in 2018

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global LAI product at 1km spatial resolution and 8-day temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs)  (Xiao et al., 2014; Xiao et al., 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2018.  Please <a href="https://zenodo.org/record/7578514#.Y9UHGXZByUk">click here</a> to download the MUSES LAI product in 2017, and <a href="https://zenodo.org/record/7483992#.Y6pzZNVBw2x">click here</a> to download the MUSES LAI product in 2019. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 1km</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 1000</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. IEEE Transactions on Geoscience and Remote Sensing, 52, 209-223.</li> <li>Xiao Zhiqiang, et al. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 54, 5301-5318.</li> <li>Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. International Journal of Remote Sensing, 43(4), 1199-1225.</li> <li>Xiao Zhiqiang, et al. (2017). Evaluation of four long time-series global leaf area index products. Agricultural and Forest Meteorology, 246, 218-230.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2019 (001–177)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2019 (001–177). Please <a href="https://zenodo.org/record/7500321#.Y7S1dNVBw2y">click here</a> to download the MUSES FVC product in 2018 (185–361), and <a href="https://zenodo.org/record/7499330#.Y7OTKdVBw2x">click here</a> to download the MUSES FVC product in 2019 (185–361). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2018 (185–361)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2018 (185–361). Please <a href="https://zenodo.org/record/7502675#.Y7WAotVBw2x">click here</a> to download the MUSES FVC product in 2018 (001–177) and <a href="https://zenodo.org/record/7498056#.Y7OSfdVBw2x">click here</a> to download the MUSES FVC product in 2019 (001–177). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018 (185–361)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2018 (001–177)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2018 (001–177). Please <a href="https://zenodo.org/record/7503627#.Y7ZZEn1ByUk">click here</a> to download the MUSES FVC product in 2017 (185–361), and <a href="https://zenodo.org/record/7500321#.Y7TEIdVBw2x">click here</a> to download the MUSES FVC product in 2018 (185–361). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2017 (185–361)

The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>). This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao et al., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2017 (185–361). Please <a href="https://zenodo.org/record/7505201#.Y7durn1ByUk">click here</a> to download the MUSES FVC product in 2017 (001–177) and <a href="https://zenodo.org/record/7502675#.Y7WENNVBw2x">click here</a> to download the MUSES FVC product in 2018 (001–177). Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017 (185–361)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. Remote Sensing, 8, 337.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).