Find research datasets worth reusing

opencc-zeroMar 2023View details →

Data from: Long-term capture data uncover shifts in the daily activity patterns of Amazonian birds

Although the duration of biological rhythms varies from milliseconds to decades, daily cycles are especially widespread for individual organisms. These circadian rhythms are synchronized by light-dark cycles, which govern predictable changes in other microclimate variables. Despite the fixed, regular nature of these abiotic cycles at low latitudes, little is known about how different animals structure activity along these gradients. We used 25 years of capture-recapture data and ~25,000 unique captures to characterize 'activity' (time of capture) for 65 species of Amazonian birds in the lowest forest stratum (0–2.5 m). To quantify vertical niche breadths, we also measured foraging heights for these same species in the field. Activity peaked within the first two hours after sunrise for most species (71%), while others peaked 1–3 hours later (29%). This difference was associated with an apparent preference for specific vertical strata, as terrestrial and near-ground birds peaked ~1 hour after sunrise, while birds from higher strata delayed their first bouts of activity in the lower understory (by an average of 40 to 100 minutes). Peak capture times also correlated with long-term abundance trends, as only species with the earliest capture times showed strong declines, suggesting that associations with mid- or late-morning microclimates may contribute to resiliency in birds typical of higher strata. Because arboreal species largely avoid the lower understory in the early morning, when terrestrial birds are active, we infer that they are poorly adapted to low-light environments. However, with their broad vertical niches and reliance on brighter microhabitats, we suggest that an arboreal lifestyle diminishes specialization and may increase the capacity to cope with a wider range of daily microclimate conditions. We predict that subtle, but important, shifts in daily activity patterns would materialize if other biodiversity-monitoring programs could analyze similar temporal datasets.

Long-range inhibition synchronizes and updates prefrontal task activity

Trial-by-trial physiology data (synchrony measurements and population activity vectors used to compute population vector similarity) supporting Cho et al., 2023. Any use of this data should cite this DOI as well as the associated publication.

opencc-by-4.0Jul 2011View details →

Vickery's late ideas on classification by phenomena and activities

Classification was always a major interest for Brian Vickery. In his last years he contributed to the theoretical debate on classification with original ideas that are not well known yet, as some of them were consigned to ephemeral Web pages or private discussion. This paper attempts to report them and to discuss their implications for the current theory of knowledge organization. Vickery’s proposals especially concern: application of the theory of integrative levels to classification as recommended by the Classification Research Group; the various dimensions of knowledge that are involved in the steps ‘from the world to the classifier’; progressive identification by science of phenomena within reality; interplay between the phenomena studied and the human activities providing the context and purpose for studying them; identification of facets of activities as well as facets of phenomena. It is concluded that these ideas offer a substantial contribution to the theory of knowledge organization, and thus should be known, discussed and further developed. industry body. Instead, it requires a bold leap from an informed theoretical base to an implementable strategy.

Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations"

Simulation dataset of "Wave activities throughout a low-Mach number quasi-parallel shock: 2-D hybrid simulations", including magnetic fields, number density and average velocities of particles.

opencc-zeroMar 2023View details →

Are breeding activities risky for northern bobwhites? An assessment of survival costs of reproduction

Behaviors associated with breeding can increase mortality risk. This increased risk can be thought of as a cost of reproduction. Increased movements prior to breeding are common as individuals search for food and breeding sites. These increased movements are thought to entail greater predation risks as individuals travel through unfamiliar areas but few studies have looked at how these prebreeding movements affect survival, especially at a fine temporal resolution. Costs of reproduction may also occur during reproduction. For birds, incubation and brood-rearing can increase predation risk because individuals spend most of their time at nest sites or with broods, which may make them more easily detected and captured by predators. Using time- and individual-specific predictors of survival, I examined the relationship between survival, movements, habitat use, and breeding status of northern bobwhites Colinus virginianus in Colorado, USA. I found that prebreeding ranges were larger for breeders (29 ha) than non-breeders (18.7 ha) but daily movement distance was not different (163 m). Range size did not affect survival; however, longer recent daily movement distances (within 10 days) resulted in higher survival. Breeding status also affected survival; laying individuals experienced the highest daily survival rates followed by incubating, non-breeding and brood-rearing individuals. Overall, there appears to be a survival cost of reproduction for individuals during brood-rearing, but I found no evidence that increased movements results in decreased survival.

Movie of the molecular dynamics simulation of a crucial step in the activation of the STING protein

STING (stimulator of interferon genes) is a homodimer protein involved in regulation of the innate immune system and plays a role in antitumor immunity by inducing the production of cytokines. Activation of STING stems from binding of endogenous cyclic dinucleotides (CDNs), which induce a conformational change to initiate signaling. The MP4 movie shows a molecular dynamics simulation of a crucial step of the activation process.

opencc-zeroMar 2023View details →

Data from: Postnatal administration of S-adenosylmethionine restores developmental AHR activation-induced deficits in CD8+ T cell function during influenza A virus infection

Abstract Developmental exposures can influence life-long health; yet, counteracting negative consequences is challenging due to poor understanding of cellular mechanisms. The aryl hydrocarbon receptor (AHR) binds many small molecules, including numerous pollutants. Developmental exposure to the signature environmental AHR ligand 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) significantly dampens adaptive immune responses to influenza A virus (IAV) in adult offspring. CD8+ cytotoxic T lymphocytes (CTL) are crucial for successful infection resolution, which depends on the number generated and the complexity of their functionality. Prior studies showed developmental AHR activation significantly reduced the number of virus-specific CD8+ T cells, but impact on their functions is less clear. Other studies showed developmental exposure was associated with differences in DNA methylation in CD8+ T cells. Yet, empirical evidence that differences in DNA methylation are causally related to altered CD8+ T cell function is lacking. The two objectives were to ascertain whether developmental AHR activation affects CTL function, and whether differences in methylation contribute to reduced CD8+ T cell responses to infection. Developmental AHR triggering significantly reduced CTL polyfunctionality, and modified the transcriptional program of CD8+ T cells. S-adenosylmethionine (SAM), which increases DNA methylation, but not Zebularine, which diminishes DNA methylation, restored polyfunctionality and boosted the number of virus-specific CD8+ T cells. These findings suggest that diminished methylation, initiated by developmental exposure to an AHR-binding chemical, contributes to durable changes in antiviral CD8+ CTL functions later in life. Thus, deleterious consequence of development exposure to environmental chemicals are not permanently fixed, opening the door for interventional strategies to improve health.

Antimicrobial activity, in millimeters, of Irreversible Hydrocolloids against S. aureus

Antimicrobial activity, in millimeters, of Irreversible Hydrocolloids incorporated with various disinfectant against S. aureus

Antimicrobial activity, in millimeters, of Irreversible Hydrocolloids against C. albicans

Antimicrobial activity, in millimeters, of Irreversible Hydrocolloids incorporated with against C. albicans

Shaping active matter from crystalline solids to active turbulence

Qianghong Yang and Maoqiang Jiang contributed equally to this work.  Corresponding author: Lailai Zhu (lailai_zhu@nus.edu.sg)

Data for: Microhabitat conditions remedy heat stress effects on insect activity

This is the data set for the study entitled "Microhabitat conditions remedy heat stress effects on insect activity". Here we measured movement activity of terrestrial insects in response to heat stress and varying microhabitat conditions using Radio Frequency Identification (RFID-tracking) for terrestrial, running insects.

opencc-by-4.0Feb 2023View details →

Extended figures: Tumour immune rejection triggered by activation of alpha2-adrenergic receptors

Code and data to reproduce the extended figures from Tumour immune rejection triggered by activation of alpha2-adrenergic receptors by Zhu et al.

MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m 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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao et al., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2019. Please <a href="https://zenodo.org/record/7793505#.ZCt_1XZBypo">click here</a> to download the MUSES FAPAR product in 2018, and click here to download the MUSES FAPAR product in 2020. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. Remote Sensing of Environment, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, et al., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. IEEE Transactions on Geoscience and Remote Sensing, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. Remote Sensing, 14, 3304, 2022.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-by-4.0Feb 2023View details →

mmWave-based Fitness Activity Recognition Dataset

Description:This mmWave Datasets are used for fitness activity identification. This dataset (FA Dataset) contains 14 common fitness daily activities. The data are captured by the mmWave radar TI-AWR1642. The dataset can be used by fellow researchers to reproduce the original work or to further explore other machine-learning problems in the domain of mmWave signals.Format: .png formatSection 1: Device Configuration<ul><li>A commodity mmWave radar TI AWR1642, which integrates a 2 × 4 antenna array. The detailed information of it can be found at <a href="https://www.ti.com/product/AWR1642#:~:text=The%20AWR1642%20is%20an%20ideal,of%2076%20to%2081%20GHz">https://www.ti.com/product/AWR1642#:~:text=The%20AWR1642%20is%20an%20ideal,of%2076%20to%2081%20GHz.</a></li><li>A TI DCA1000EVM data capture card is used to collect data from the mmWave device and send data to a laptop. The detailed information can be found at <a href="https://www.ti.com/tool/DCA1000EVM?keyMatch=DCA1000EVM">https://www.ti.com/tool/DCA1000EVM?keyMatch=DCA1000EVM</a>.</li><li>mmWave radar work at the frequency in the range of 77~81GHz. The sampling rate is fixed at 100 frames per second and each frame has 17 chirps.</li></ul>Section 2: Data FormatWe provide our mmWave data in heatmaps for this dataset. The data file is in the png format. The details are shown in the following:<ul><li>14 activities are included in the FA Dataset.</li><li>2 participants are included in the FA Dataset.</li><li>FA_d_p_i_u_j.png:<ul><li>d represents the date to collect the fitness data.</li><li>p represents the environment to collect the fitness data.</li><li>i represents fitness activity type index</li><li>u represents user id</li><li>j represents sample index</li></ul></li><li>Example:<ul><li>FA_20220101_lab_1_2_3 represents the 3rd data sample of user 2 of activity 1 collected in the lab</li></ul></li></ul>Section 3: Experimental Setup<ul><li>We place the mmWave device on a table with a height of 60cm.</li><li>The participants are asked to perform fitness activity in front of a mmWave device with a distance of 2m.</li><li>The data are collected at an lab with a size of (5.0m×3.0m).</li></ul>Section 4: Data Description<ul><li>We develop a spatial-temporal heatmap to integrates multiple activity features, including the range of movement, velocity, and time duration of each activity repetition.</li></ul> <ul><li>We first derive the Doppler-range map of the users' activity by calculating Range-FFT and Doppler-FFT. Then, we generate the spatial-temporal heatmap by accumulating the velocity of every distance in every Doppler-range map together. Next, we normalize the derived velocity information and present the velocity-distance relationship in time dimension. In this way, we transfer the original instantaneous velocity-distance relationship to a more comprehensive spatial-temporal heatmap which describes the process of a whole activity.</li></ul> <ul><li>As shown in Figure attached, in each spatial-temporal heatmap, the horizontal axis represents the time duration of an activity repetition while the vertical axis represents the range of movement. The velocity is represented by color.</li></ul> <ul><li>We create 14 zip files to store the the dataset. There are 14 zip files starting with "FA", each contains repetitions from the same fitness activity.</li></ul>14 common daily activities and their corresponding files File Name               Activity Type                             File Name              Activity TypeFA1                          Crunches                                    FA8                        SquatsFA2                          Elbow plank and reach               FA9                       BurpeesFA3                          Leg raise                                     FA10                     Chest squeezesFA4                          Lunges                                        FA11                     High kneesFA5                          Mountain climber                        FA12                     Side leg raiseFA6                          Punches                                      FA13                     Side to side chopsFA7                          Push ups                                     FA14                     Turning kicks Section 5: Raw Data and Data Processing Algorithms<ul><li>We also provide the mmWave raw data (.mat format) stored in the same zip file corresponding to the heatmap datasets. Each .mat file can store one set of activity repetitions (e.g., 4 repetations) from a same user.<ul><li>For example: FA_d_p_i_u_j.mat:<ul><li>d represents the data to collect the data.</li><li>p represents the environment to collect the data.</li><li>i represents the activity type index</li><li>u represents the user id</li><li>j represents the set index</li></ul></li></ul></li><li>We plan to provide the data processing algorithms (heatmap_generation.py) to load the mmWave raw data and generate the corresponding heatmap data.</li></ul>Section 6: CitationsIf your paper is related to our works, please cite our papers as follows.<a href="https://ieeexplore.ieee.org/document/9868878/">https://ieeexplore.ieee.org/document/9868878/</a>Xie, Yucheng, Ruizhe Jiang, Xiaonan Guo, Yan Wang, Jerry Cheng, and Yingying Chen. "mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave." In 2022 International Conference on Computer Communications and Networks (ICCCN), pp. 1-10. IEEE, 2022.Bibtex:@inproceedings{xie2022mmfit,  title={mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave},  author={Xie, Yucheng and Jiang, Ruizhe and Guo, Xiaonan and Wang, Yan and Cheng, Jerry and Chen, Yingying},  booktitle={2022 International Conference on Computer Communications and Networks (ICCCN)},  pages={1--10},  year={2022},  organization={IEEE}}

MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m SIN Grid in 2017

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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao et al., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2017. Please <a href="https://zenodo.org/record/7798475#.ZC0eo3ZBypo">click here</a> to download the MUSES FAPAR product in 2016, and <a href="https://zenodo.org/record/7793505#.ZCuBFntBw2x">click here</a> to download the MUSES FAPAR product in 2018. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. Remote Sensing of Environment, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, et al., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. IEEE Transactions on Geoscience and Remote Sensing, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. Remote Sensing, 14, 3304, 2022.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m 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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao et al., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2018. Please <a href="https://zenodo.org/record/7796631#.ZCwOB3ZBypo">click here</a> to download the MUSES FAPAR product in 2017, and <a href="https://zenodo.org/record/7755485#.ZCt_hntBw2x">click here</a> to download the MUSES FAPAR product in 2019. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. Remote Sensing of Environment, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, et al., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. IEEE Transactions on Geoscience and Remote Sensing, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. Remote Sensing, 14, 3304, 2022.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m SIN Grid in 2016

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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao et al., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2016. Please <a href="https://zenodo.org/record/7798473#.ZC4C0nZBypo">click here</a> to download the MUSES FAPAR product in 2015, and <a href="https://zenodo.org/record/7796631#.ZCwO7XtBw2x">click here</a> to download the MUSES FAPAR product in 2017. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2016</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. Remote Sensing of Environment, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, et al., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. IEEE Transactions on Geoscience and Remote Sensing, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. Remote Sensing, 14, 3304, 2022.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m SIN Grid in 2014

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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao et al., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2014. Please <a href="https://zenodo.org/record/7804559#.ZC9MDXZBypo">click here</a> to download the MUSES FAPAR product in 2013, and <a href="https://zenodo.org/record/7798473#.ZC4IEntBw2x">click here</a> to download the MUSES FAPAR product in 2015. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2014</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. Remote Sensing of Environment, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, et al., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. IEEE Transactions on Geoscience and Remote Sensing, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. Remote Sensing, 14, 3304, 2022.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).

opencc-zeroApr 2023View details →

Data from: Pooled genome-wide CRISPR activation screening for rapamycin resistance genes in Drosophila cells

Loss-of-function and gain-of-function genetic perturbations provide valuable insights into gene function. In Drosophila cells, while genome-wide loss-of-function screens have been extensively used to reveal mechanisms of a variety of biological processes, approaches for performing genome-wide gain-of-function screens are still lacking. Here, we describe a pooled CRISPR activation (CRISPRa) screening platform in Drosophila cells and apply this method to both focused and genome-wide screens to identify rapamycin resistance genes. The screens identified three genes as novel rapamycin resistance genes: a member of SLC16 family of monocarboxylate transporters (CG8468), a member of the lipocalin protein family (CG5399), and a zinc finger C2H2 transcription factor (CG9932). Mechanistically, we demonstrate that CG5399 overexpression activates the RTK-Akt-mTOR signaling pathway and that activation of insulin receptor (InR) by CG5399 requires cholesterol and clathrin-coated pits at the cell membrane. This study establishes a novel platform for functional genetic studies in Drosophila cells.

MUSES Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) 8-Day Global 500m SIN Grid in 2013

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 FAPAR product at 500m spatial resolution and 8-day temporal resolution. The MUSES FAPAR product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500m resolution and other ancillary information using the complement to unity of the transmittance of PAR through the entire canopy (Xiao et al., 2015). The MUSES FAPAR values are the instantaneous values at 10:30 am local time, close approximation of daily average PAPAR values, and they are physically consistent with the corresponding MUSES LAI values. The MUSES FAPAR product is spatially complete and temporally continuous. This dataset is the MUSES FAPAR product in 2013. Please <a href="https://zenodo.org/record/7806987#.ZC_ZkHZBypo">click here</a> to download the MUSES FAPAR product in 2012, and <a href="https://zenodo.org/record/7803901#.ZC5yaHtBw2x">click here</a> to download the MUSES FAPAR product in 2014. Dataset Characteristics: <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2013</li> <li>Spatial Resolution: 500m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.004</li> <li>Valid Range: 0 – 250</li> </ul> Citation (Please cite this paper whenever these data are used): <ol> <li>Xiao Zhiqiang, et al., Estimating the fraction of absorbed photosynthetically active radiation from the MODIS databased GLASS leaf area index product. Remote Sensing of Environment, 171,105-117, 2015.</li> <li>Xiao Zhiqiang, et al., Evaluation of Three Long Time Series for Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products. IEEE Transactions on Geoscience and Remote Sensing, 56, 5509-5524, 2018.</li> <li>Zheng Y., Xiao Z., Li J., Yang H., Song J., Evaluation of Global Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Products at 500 m Spatial Resolution. Remote Sensing, 14, 3304, 2022.</li> </ol> If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).