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    Authors: xie, jianfei;

    A multivariate feature fusion dataset that meets the Chinese grading criteria. The multivariate feature fusion dataset. Specifically, we collected information on three types of fruits, namely, Kuril balsam pear, sainfoin, and satsuma orange. In order to ensure the broadness and representativeness of the data, for each fruit we We randomly collected and videotaped about 150 samples, and also measured fruit diameter, overall fruit Fruit diameter, overall appearance, overall defects and weight were measured in detail to ensure that the data were broad and representative. Detailed measurements of defects and weight were taken to ensure that each sample had detailed data corresponding to it.

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    Authors: JOHN, JAIYEOLA;

    Smart Home Automation System

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    Authors: Mendes, Gilberto;

    Raw data associated with the paper Growth promotion of coffee plants by Aspergillus niger in a P-fixing soil: evidence from biometry and remote sensing. Check the paper for details about the data. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Biro, Arielle;

    # Nitrogen and phosphorus availability alter tree-grass competition intensity in savannas ## Description of the data and file structure There are two files: treedata.csv and grassdata.csv. The tree data file contains all data for tree species in the experiment while the grass data contains all data for grass species. ## Tree Data Column Names Pot = pot number in experiment Species = Species Code Block = Block in the randomized block design NTrt = nutrient treatment. C = low N + low P, N = high N + low P, P = low N + high P, NP = high N + high P GTrt = grass treatment. G- = without grass, G+ = with grass Sample_ID = sample ID used for phosphatase enzyme assays RSDiam = Basal diameter of tree (in cm)\ Diam10cm = tree diameter at 10 cm (if tall enough) in cm StemHt = stem height in cm RootLn = root length in cm RootDiam = root diameter in cm FPARBase = FPAR (nm of light) at the base of the tree FPARTree = FPAR at tree canopy height FPARGrass = FPAR at grass sward height FPARDate = date FPAR measurement was taken FPARWeather = weather description (how it could potentially impact FPAR measurement) FPARTime = time of day the measurement was taken Notes = any notes taken at harvest (eg was tree dead at harvest) BagStem = bag weight of stem (g) StemWet = weight of wet stem + bag (g) StemDry = weight of dry stem + bag (g) BagLeaf = bag weight of leaf (g) LeafWet = weight of wet leaf + bag (g) LeafDry = weight of dry leaf + bag (g) BagCroot = bag weight of coarse root (g) CrootWet = weight of wet coarse root + bag (g) CrootDry = weight of dry coarse root + bag (g) BagFine12 = bag weight of fine root (1-2mm) (g) Fine12Wet = weight of wet fine root (1-2mm) + bag (g) Fine12Dry = weight of dry fine root (1-2 mm) + bag (g) BagFine01 = bag weight of fine root (0-1 mm) (g) Fine01Wet = weight of wet fine root (0-1 mm) + bag (g) Fine01Dry = weight of dry fine root (0-1 mm) + bag (g) HarvestDate.x = date the tree was harvested Fresh_root_g = weight of fresh root taken for phosphatase measurement (g) Dry_root_g = weight of dry root taken for phosphatase measurement (g) Total_fresh_fine01root_g = total fresh weight of fine (0-1 mm) root to include all fine roots (0-1mm) including those taken for phosphatase measurements - in g Total_dry_fine01root_g = total dry weight of fine (0-1 mm) root to include all fine roots (0-1mm) including those taken for phosphatase measurements - in g Total_fresh_fine02root_g = total fresh weight of fine (0-2 mm) root to include all fine roots (0-2mm) including those taken for phosphatase measurements - in g Total_dry_fine02root_g = total dry weight of fine (0-2 mm) root to include all fine roots (0-2mm) including those taken for phosphatase measurements - in g\ Total_fresh_root_g = total fresh weight of all roots, including all those taken for phosphatase measurements - in g Total_dry_root_g = total dry weight of all roots, including those taken for phosphatase measurements - in g Total_fresh_stem_g = total wet stem weight (bag weight subtracted- in g) Total_dry_stem_g = total dry stem weight (bag weight subtracted- in g) Total_fresh_leaf_g = total wet leaf weight (bag weight subtracted- in g) Total_dry_leaf_g = total dry leaf weight (bag weight subtracted- in g) Total_fresh_AGB_g = total wet aboveground weight (bag weight subtracted - in g) Total_dry_AGB_g = total dry aboveground weight (bag weight subtracted - in g) SDryF = total dry stem weight (bag weight subtracted- in g)\ LDryF = total dry leaf weight (bag weight subtracted - in g) CRDryF = total dry coarse root weight (bag weight subtracted - in g) FR1DryF = total dry fine root (1-2 mm) weight (bag weight subtracted - in g) FR0DryF = total dry fine root (0-1mm) weight (bag weight subtracted - in g) RSRatio = root shoot ratio DryTotal = all biomass dry weights added in g (stem + leaf + coarse root + fine root (1-2mm) + fine root (0-1mm) FineRootDry = all dry fine root biomass in g (fine root 1-2mm + fine 0-1mm) DryRootTotal = all dry root biomass in g (coarse root + fine root 1-2mm + fine 0-1mm) Treatment = nutrient treatment. C = low N + low P, N = high N + low P, P = low N + high P, NP = high N + high P RSDmm = basal diameter converted to mm Nitrogen = nitrogen treatment. N+ = high N, N- = low N Phosphorus = phosphorus treatment. P+ = high P, P- = low P Competition = competition treatment. G+ = with grass, G- = without grass Speciesy = full species names BNFAbility = fixing or non-fixing Family = phylogenetic family Encroacher = encroacher status Stmm = stem diameter converted to mm lgStmm = stem diameter logged after converting to mm shootelongation = shootelongation RTLmm = root length in mm RTDmm = root diameter in mm lgRTLmm = log of root length in mm lgRTLmm = log of root length in mm RTbeta1 = root elongation\ propfine = fine root mass fraction propcoarse = coarse root mass fraction propleaf = leaf mass fraction prop01fine = fine root (0-1mm) mass fraction prop12fine = fine root (1-2mm) mass fraction proproot = total root mass fraction propstem = stem massfraction propsum = checking that all prop parts add to 1 propabg = proportion aboveground biomass PlantID = treeID SowedDate = date sowed TransplateDate = date transplanted to pots HarvestDate.y = date of Harvest DaysGrownSinceTransplant = number of days the tree grew since transplant DaysGrownSinceSowed = number of days grown since seeds were planted mean_initialbiom = initial biomass of seedlings RGR = relative growth rate finalminusinitial = log(TotalDry) - log(mean_initialbiomass) massgain = TotalDry - mean_initalbiomass (in g) mean_phos = phosphatase activity for the tree (averaged across 3 replicates per tree) ## Grass Data Columns Pot = pot number GrassSpecies = Melinis Repens, grass species Species = species of tree the grasses were grown with Block = block in the randomized block design NTrt = nutrient treatment. C = low N, low P, N = high N, low P, P = low N, high P, NP = high N, high P GTrt = grass treatment (all G+ since they are grasses) Sample_ID = phosphatase enzyme assay sample ID BasalC1 = basal diameter of grass 1 in pot (cm) MaxSwardHt1 = maximum sward height of grass 1 in pot (cm) MaxCulmHt1 = maximum culm height of grass 1 in pot (cm) BasalC = basal diameter of grass 2 in pot (cm) MaxSwartHt = maximum swart height of grass 2 in pot (cm) MaxCulmHt = maximum culm height of grass 2 in pot (cm) BagGrass1 = bag weight of grass 1 (g) Grass1Wet = total wet weight of aboveground grass 1 + bag (g) Grass1Dry = total dry weight of aboveground grass 1 + bag (g) BagGrass2 = bag weight of grass 2 (g) Grass2Wet = total wet weight of aboveground grass 2 + bag (g) Grass2Dry = total dry weight of aboveground grass 2 + bag (g) BagGrassRoot = bag weight of root biomass bag (g) GrassRootWet = total wet weight of belowground biomass grass 1 + 2 (g) GrassRootDry = total dry weight of belowground biomass grass 1 + 2 (g) HarvestDate = date pot was harvested Fresh_root_g = fresh root (g) taken for phosphatase Dry_root_g = dry root (g) taken for phosphatase Total_fresh_root_g = total fresh root (g) of grass + root taken for phosphatase Total_dry_root_g = total dry root (g) of grass + roots taken for phoshpatase Total_fresh_ABG_g = total fresh aboveground biomass of grass (bag weight subtracted, grasses added together) Total_dry_ABG_g = total dry aboveground biomass of grass (bag weight subtracted, grasses added together) GDry1F = aboveground biomass of grass 1 (g - bag subtracted) GDry2F = aboveground biomass of grass 2 (g - bag subtracted) GDryRootF = belowground biomass of grasses (g - bag subtracted) RSRatio = root shoot ratio DryTotal = total dry weight in the pot AbvTot = total dry weight aboveground biomass in pot Treatment = nutrient treatment Competition = competition treatment, T- = without tree, T+ = with tree Nitrogen = nitrogen treatment, N+=high N, N- = low N Phosphorus = phosphorus treatment, P+ = high N, P- = low N Speciesy = full species name of tree grown with grass BNFAbility = nitrogen fixation ability of the tree grown with grass Family = phylogenetic family of tree grown with grass Encroacher = encroacher category of tree grown with grass PlantID = unique plant ID SowedDate = date grass seeds were sowed TransplantDate = date grasses were transplanted HarvestDate.y = date grasses were harvested DaysGrownSinceTransplant = days grown since grass transplant DaysGrownSinceSowed = days grown since grass seeds were planted Grass.Treatment = tree species and grass treatment mean_initialbiom = mean initial biomass (grasses harvested at transplant) of grasses RGR = relative growth rate of grasses finalminusinitial = log(TotalDry) - log(massgain) massgain = TotalDry - mean_initialbiom mean_phos = phosphatase activity (averaged 3 replicates per pot) ## Code/Software All analyses were run in R using packages: lme4, emmeans, lsmeans, and ggplot2. The primary goal of the experiment was to understand how trees and grasses respond to different nutrient and competition regimes. Six tree species and one grass species were selected based on phylogenetic diversity and nitrogen fixation ability for this experiment (Table 1). The grass species chosen for this experiment was Melinis repens, the only annual grass species common in Kruger National Park (Wigley-Coetsee and Staver 2020), one of the largest natural reserves that is situated in the low-lying savannas of South Africa. Plants were grown in a full-factorial nutrient-addition experiment with water availability held constant across treatments. Each pot was watered three times a day for a total of 400 mL per day, designed to mimic a wet growing season in the Pretoriuskop region of Kruger National Park, which receives about 750 mm of rainfall per year. The four nutrient treatments were (1) low N and P (), (2) high N and low P (), (3) low N and high P (), and (4) high N and P (). These nutrient treatments were applied factorially to trees grown with and without grasses and to grasses grown without trees for a total of 260 pots. Each nutrient and competition treatment was replicated five times in a randomized block design (except Schotia brachypetala, of which we had enough seedlings for only three replicates each). Grass and tree seeds were collected in Kruger National Park or purchased from Silverhill Seed Company in South Africa in 2019 and stored until germination. In July-August 2020, tree seeds were clipped and soaked in water for 24-72 hours before planted in potting soil. After sowing, seeds were watered and placed in growth chambers set at 28°C and 500 mmol/m2/s of light for the duration of germination. Grass seeds were germinated on watered potting soil in the same growth chambers. In September 2020, germinated tree seedlings and grasses were transplanted into 30-liter tree pots (Stuewe & Sons, Inc., 0.25 m diameter x 0.60 m height, sold as 8-gallon pots) filled with turface (All-Season Turface, SiteOne Landscaping, New Haven CT). One tree sapling was planted per pot without grass competition, and two grass tufts were planted per pot without tree competition. For competition treatments, one tree sapling and two grass tufts were planted per pot (tree sapling in the middle, one tuft on either side). All pots were fertilized once a week for 16 weeks at their assigned levels from October 2020-January 2021. Low nutrient treatments were designed to replicate nutrient limitation, while high nutrient treatments were designed to alleviate nutrient limitation while avoiding nitrogen or phosphorus toxicity. To determine the appropriate concentrations of N and P for each treatment, we derived monthly estimates of fertilizer from Tomlinson et al. (2019) along with field nutrient estimates from Ludwig et al. (2004). All treatments received 100 mg N and treatments 10 mg P per pot per week to mimic field conditions via ammonium nitrate and potassium phosphate, with treatments receiving 800 mg N and treatments 100 mg P per pot per week. Along with N and P fertilization, each treatment received the same quantity of N- and P-free Hoagland’s micronutrient solution, which included all essential micronutrients: potassium, calcium, sulfur, magnesium, chloride, sodium, manganese, zinc, copper, molybdenum, boron and iron. Pots were located in a single greenhouse bay at Yale Marsh Botanical Gardens in a block-randomized design, kept at 28°C for the duration of the experiment, and supplemented with grow lights for 12 hours a day to replicate a sub-tropical growing season. Soil moisture was measured throughout the experiment to ensure that soil moisture remained constant across pots. Plant Responses At the time of transplanting, 10 individuals of each tree and grass species were harvested for initial size and biomass estimates. For tree seedlings, we recorded starting biomass, stem height, basal diameter, diameter at 10 cm height, root diameter, root length, wet and dry leaf stem mass, and fine (sorted into two diameter classes: 0-1 mm and 1-2 mm) and coarse root (> 2 mm) dry mass. For grasses, we recorded basal circumference, maximum culm and sward height, root diameter, and wet and dry weight of aboveground and belowground grass biomass. Throughout the experiment, measurements were taken every four weeks. Tree measurements consisted of stem height, basal diameter, and diameter at 10 cm. Grass measurements consisted of basal circumference and sward height. Along with tree and grass measurements, light availability was measured using an fPAR meter (fraction of incident photosynthetically active radiation, 400-700 nm) at three different heights (tree base, tree canopy height, and grass height). At the final harvest in January 2021, we repeated all routine measurements and measured wet and dry weights of each plant tissue type. Time to harvest did not differ among species or blocks nor with respect to any experimental treatment (n = 260). To calculate mass gain, we subtracted the mean total dry weight of the 10 tree seedlings or grasses harvested at the start of the experiment from the final total dry weights at the end of the experiment. We used allometric relationships to evaluate whether competition and nutrient treatments influenced the growth form of trees. To do this, we calculated the allometric exponent b as follows (Feldpausch et al. 2011): for both shoot and root systems. For shoots, we used stem height and basal diameter (mm). For roots, we used taproot length for ‘height’ and root diameter (mm). We refer to the traits represented by these allometric exponents as ‘shoot elongation’ and ‘root elongation’, respectively. Competition intensity was calculated by dividing the average mass gain of a tree or grass grown with competition by the average mass gain of a tree or grass grown out of competition and subtracted by one for each nutrient treatment within one species (i.e., only individuals of the same species were compared). Negative values (< 0) indicate competitive effects while positive values (> 0) indicate facilitative effects (Carlyle et al. 2010, Jabot and Pottier 2012). To quantify root phosphatase activity, we used a method adapted from Turner et al. (2001), using para-nitrophenyl (pNPP) as an analogue for phosphomonoesterase, a root phosphatase that hydrolyzes simple phosphate monoesters to release a phosphate ion for plant uptake (Browman and Tabatabai 1978, Tabatabai 1994). For each plant within a pot, we collected three subsamples of fine roots for measuring phosphomonoesterase activity and one for measuring color production without substrate added. For each plant, 500 mg of fresh root was added to a glass vial with 9 mL of 50 mM sodium acetate-acetic acid buffer (pH 5.0) and shaken in a water bath at 28°C for five minutes to simulate wet season surface soil temperatures at Kruger National Park. To initiate the reaction, we added 1.0 mL of 50 mM pNPP (5.0 mM final concentration). The reaction was terminated by mixing 0.5 mL of buffer/substrate solution with 4.5 mL of 0.11 M NaOH, which was then vortexed and measured for absorbance at 405 nm against paranitrophenol (pNP) standards. Two sodium acetate-acetic acid buffer and substrate blanks were run for every assay. Roots were removed from the vials and dried at 65°C for 3 days to express the activity as μmol pNP g-1 dry root mass h-1. Plant essential macronutrients like nitrogen (N) and phosphorus (P) can limit savanna tree growth and are important determinants of savanna vegetation dynamics, along with rainfall, fire, and herbivory. How nitrogen and phosphorus shape tree-grass competition and their coexistence remain unclear, hindering our ability to predict how savannas may respond to altered nutrient cycling. Here, we evaluate (1) if trees and grasses respond differently to N vs. P availability, or (2) if grasses are more competitive in low nutrient environments while trees are more competitive in high nutrient environments. To do this, we grew saplings of 6 tree and 1 grass species from Kruger National Park, South Africa, for 16 weeks under fully factorial nutrient and competition treatments (with/without competitors, low/high rate of N supply, and low/high rate of P supply) under a watering regime designed to mimic wet season rainfall in a mesic savanna. Trees and grasses foraged most aggressively for nitrogen and allocated biomass differently depending on nitrogen availability. Overall, tree growth decreased in competition with grass, even in high nutrient environments where they grew faster. Grasses were always better belowground competitors, utilizing aggressive nutrient foraging strategies, including high root phosphatase activity in response to nitrogen and large root biomass allocation. Synthesis: In low nutrient environments (e.g., on nutrient-poor sandy soils), nutrients may limit tree growth. Nutrient rich environments enable tree growth, but grasses continue to compete effectively with trees. Understanding what this means for ecosystem responses to nutrient availability is not trivial, especially in the context of fire and herbivory. However, it is clear that soil nutrients likely affect tree and grass growth and competition in savannas, which suggests that future changes in nutrient cycling, such as N deposition, may have important effects on savanna vegetation.

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    Authors: Nidamanuri, R;

    Eggplant crop hyperspectral data was acquired using a terrestrial hyperspectral imaging spectroradiometer. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Nidamanuri, R;

    Cabbage crop hyperspectral data was acquired using a terrestrial hyperspectral imaging spectroradiometer. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Nidamanuri, R;

    Cabbage and eggplant crops combined hyperspectral data was acquired using a terrestrial hyperspectral imaging spectroradiometer. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    The study aimed to investigate how implementation of the Enhanced Recovery after Surgery (ERAS) program in patients undergoing robotic hysterectomy for benign indications affects the length of hospital stay (LOHS) in comparison with conventional management. It also aimed to compare postoperative complications within 30 days after surgery and quality of life. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Pereira Cavalcante de Castro, Pedro Arfux Pereira Cavalcante de Castro;

    This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - October, 2023). Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset. Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG). THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Pereira Cavalcante de Castro, P;

    This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - November, 2023). Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset. Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG). THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: xie, jianfei;

    A multivariate feature fusion dataset that meets the Chinese grading criteria. The multivariate feature fusion dataset. Specifically, we collected information on three types of fruits, namely, Kuril balsam pear, sainfoin, and satsuma orange. In order to ensure the broadness and representativeness of the data, for each fruit we We randomly collected and videotaped about 150 samples, and also measured fruit diameter, overall fruit Fruit diameter, overall appearance, overall defects and weight were measured in detail to ensure that the data were broad and representative. Detailed measurements of defects and weight were taken to ensure that each sample had detailed data corresponding to it.

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    Authors: JOHN, JAIYEOLA;

    Smart Home Automation System

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    Authors: Mendes, Gilberto;

    Raw data associated with the paper Growth promotion of coffee plants by Aspergillus niger in a P-fixing soil: evidence from biometry and remote sensing. Check the paper for details about the data. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Biro, Arielle;

    # Nitrogen and phosphorus availability alter tree-grass competition intensity in savannas ## Description of the data and file structure There are two files: treedata.csv and grassdata.csv. The tree data file contains all data for tree species in the experiment while the grass data contains all data for grass species. ## Tree Data Column Names Pot = pot number in experiment Species = Species Code Block = Block in the randomized block design NTrt = nutrient treatment. C = low N + low P, N = high N + low P, P = low N + high P, NP = high N + high P GTrt = grass treatment. G- = without grass, G+ = with grass Sample_ID = sample ID used for phosphatase enzyme assays RSDiam = Basal diameter of tree (in cm)\ Diam10cm = tree diameter at 10 cm (if tall enough) in cm StemHt = stem height in cm RootLn = root length in cm RootDiam = root diameter in cm FPARBase = FPAR (nm of light) at the base of the tree FPARTree = FPAR at tree canopy height FPARGrass = FPAR at grass sward height FPARDate = date FPAR measurement was taken FPARWeather = weather description (how it could potentially impact FPAR measurement) FPARTime = time of day the measurement was taken Notes = any notes taken at harvest (eg was tree dead at harvest) BagStem = bag weight of stem (g) StemWet = weight of wet stem + bag (g) StemDry = weight of dry stem + bag (g) BagLeaf = bag weight of leaf (g) LeafWet = weight of wet leaf + bag (g) LeafDry = weight of dry leaf + bag (g) BagCroot = bag weight of coarse root (g) CrootWet = weight of wet coarse root + bag (g) CrootDry = weight of dry coarse root + bag (g) BagFine12 = bag weight of fine root (1-2mm) (g) Fine12Wet = weight of wet fine root (1-2mm) + bag (g) Fine12Dry = weight of dry fine root (1-2 mm) + bag (g) BagFine01 = bag weight of fine root (0-1 mm) (g) Fine01Wet = weight of wet fine root (0-1 mm) + bag (g) Fine01Dry = weight of dry fine root (0-1 mm) + bag (g) HarvestDate.x = date the tree was harvested Fresh_root_g = weight of fresh root taken for phosphatase measurement (g) Dry_root_g = weight of dry root taken for phosphatase measurement (g) Total_fresh_fine01root_g = total fresh weight of fine (0-1 mm) root to include all fine roots (0-1mm) including those taken for phosphatase measurements - in g Total_dry_fine01root_g = total dry weight of fine (0-1 mm) root to include all fine roots (0-1mm) including those taken for phosphatase measurements - in g Total_fresh_fine02root_g = total fresh weight of fine (0-2 mm) root to include all fine roots (0-2mm) including those taken for phosphatase measurements - in g Total_dry_fine02root_g = total dry weight of fine (0-2 mm) root to include all fine roots (0-2mm) including those taken for phosphatase measurements - in g\ Total_fresh_root_g = total fresh weight of all roots, including all those taken for phosphatase measurements - in g Total_dry_root_g = total dry weight of all roots, including those taken for phosphatase measurements - in g Total_fresh_stem_g = total wet stem weight (bag weight subtracted- in g) Total_dry_stem_g = total dry stem weight (bag weight subtracted- in g) Total_fresh_leaf_g = total wet leaf weight (bag weight subtracted- in g) Total_dry_leaf_g = total dry leaf weight (bag weight subtracted- in g) Total_fresh_AGB_g = total wet aboveground weight (bag weight subtracted - in g) Total_dry_AGB_g = total dry aboveground weight (bag weight subtracted - in g) SDryF = total dry stem weight (bag weight subtracted- in g)\ LDryF = total dry leaf weight (bag weight subtracted - in g) CRDryF = total dry coarse root weight (bag weight subtracted - in g) FR1DryF = total dry fine root (1-2 mm) weight (bag weight subtracted - in g) FR0DryF = total dry fine root (0-1mm) weight (bag weight subtracted - in g) RSRatio = root shoot ratio DryTotal = all biomass dry weights added in g (stem + leaf + coarse root + fine root (1-2mm) + fine root (0-1mm) FineRootDry = all dry fine root biomass in g (fine root 1-2mm + fine 0-1mm) DryRootTotal = all dry root biomass in g (coarse root + fine root 1-2mm + fine 0-1mm) Treatment = nutrient treatment. C = low N + low P, N = high N + low P, P = low N + high P, NP = high N + high P RSDmm = basal diameter converted to mm Nitrogen = nitrogen treatment. N+ = high N, N- = low N Phosphorus = phosphorus treatment. P+ = high P, P- = low P Competition = competition treatment. G+ = with grass, G- = without grass Speciesy = full species names BNFAbility = fixing or non-fixing Family = phylogenetic family Encroacher = encroacher status Stmm = stem diameter converted to mm lgStmm = stem diameter logged after converting to mm shootelongation = shootelongation RTLmm = root length in mm RTDmm = root diameter in mm lgRTLmm = log of root length in mm lgRTLmm = log of root length in mm RTbeta1 = root elongation\ propfine = fine root mass fraction propcoarse = coarse root mass fraction propleaf = leaf mass fraction prop01fine = fine root (0-1mm) mass fraction prop12fine = fine root (1-2mm) mass fraction proproot = total root mass fraction propstem = stem massfraction propsum = checking that all prop parts add to 1 propabg = proportion aboveground biomass PlantID = treeID SowedDate = date sowed TransplateDate = date transplanted to pots HarvestDate.y = date of Harvest DaysGrownSinceTransplant = number of days the tree grew since transplant DaysGrownSinceSowed = number of days grown since seeds were planted mean_initialbiom = initial biomass of seedlings RGR = relative growth rate finalminusinitial = log(TotalDry) - log(mean_initialbiomass) massgain = TotalDry - mean_initalbiomass (in g) mean_phos = phosphatase activity for the tree (averaged across 3 replicates per tree) ## Grass Data Columns Pot = pot number GrassSpecies = Melinis Repens, grass species Species = species of tree the grasses were grown with Block = block in the randomized block design NTrt = nutrient treatment. C = low N, low P, N = high N, low P, P = low N, high P, NP = high N, high P GTrt = grass treatment (all G+ since they are grasses) Sample_ID = phosphatase enzyme assay sample ID BasalC1 = basal diameter of grass 1 in pot (cm) MaxSwardHt1 = maximum sward height of grass 1 in pot (cm) MaxCulmHt1 = maximum culm height of grass 1 in pot (cm) BasalC = basal diameter of grass 2 in pot (cm) MaxSwartHt = maximum swart height of grass 2 in pot (cm) MaxCulmHt = maximum culm height of grass 2 in pot (cm) BagGrass1 = bag weight of grass 1 (g) Grass1Wet = total wet weight of aboveground grass 1 + bag (g) Grass1Dry = total dry weight of aboveground grass 1 + bag (g) BagGrass2 = bag weight of grass 2 (g) Grass2Wet = total wet weight of aboveground grass 2 + bag (g) Grass2Dry = total dry weight of aboveground grass 2 + bag (g) BagGrassRoot = bag weight of root biomass bag (g) GrassRootWet = total wet weight of belowground biomass grass 1 + 2 (g) GrassRootDry = total dry weight of belowground biomass grass 1 + 2 (g) HarvestDate = date pot was harvested Fresh_root_g = fresh root (g) taken for phosphatase Dry_root_g = dry root (g) taken for phosphatase Total_fresh_root_g = total fresh root (g) of grass + root taken for phosphatase Total_dry_root_g = total dry root (g) of grass + roots taken for phoshpatase Total_fresh_ABG_g = total fresh aboveground biomass of grass (bag weight subtracted, grasses added together) Total_dry_ABG_g = total dry aboveground biomass of grass (bag weight subtracted, grasses added together) GDry1F = aboveground biomass of grass 1 (g - bag subtracted) GDry2F = aboveground biomass of grass 2 (g - bag subtracted) GDryRootF = belowground biomass of grasses (g - bag subtracted) RSRatio = root shoot ratio DryTotal = total dry weight in the pot AbvTot = total dry weight aboveground biomass in pot Treatment = nutrient treatment Competition = competition treatment, T- = without tree, T+ = with tree Nitrogen = nitrogen treatment, N+=high N, N- = low N Phosphorus = phosphorus treatment, P+ = high N, P- = low N Speciesy = full species name of tree grown with grass BNFAbility = nitrogen fixation ability of the tree grown with grass Family = phylogenetic family of tree grown with grass Encroacher = encroacher category of tree grown with grass PlantID = unique plant ID SowedDate = date grass seeds were sowed TransplantDate = date grasses were transplanted HarvestDate.y = date grasses were harvested DaysGrownSinceTransplant = days grown since grass transplant DaysGrownSinceSowed = days grown since grass seeds were planted Grass.Treatment = tree species and grass treatment mean_initialbiom = mean initial biomass (grasses harvested at transplant) of grasses RGR = relative growth rate of grasses finalminusinitial = log(TotalDry) - log(massgain) massgain = TotalDry - mean_initialbiom mean_phos = phosphatase activity (averaged 3 replicates per pot) ## Code/Software All analyses were run in R using packages: lme4, emmeans, lsmeans, and ggplot2. The primary goal of the experiment was to understand how trees and grasses respond to different nutrient and competition regimes. Six tree species and one grass species were selected based on phylogenetic diversity and nitrogen fixation ability for this experiment (Table 1). The grass species chosen for this experiment was Melinis repens, the only annual grass species common in Kruger National Park (Wigley-Coetsee and Staver 2020), one of the largest natural reserves that is situated in the low-lying savannas of South Africa. Plants were grown in a full-factorial nutrient-addition experiment with water availability held constant across treatments. Each pot was watered three times a day for a total of 400 mL per day, designed to mimic a wet growing season in the Pretoriuskop region of Kruger National Park, which receives about 750 mm of rainfall per year. The four nutrient treatments were (1) low N and P (), (2) high N and low P (), (3) low N and high P (), and (4) high N and P (). These nutrient treatments were applied factorially to trees grown with and without grasses and to grasses grown without trees for a total of 260 pots. Each nutrient and competition treatment was replicated five times in a randomized block design (except Schotia brachypetala, of which we had enough seedlings for only three replicates each). Grass and tree seeds were collected in Kruger National Park or purchased from Silverhill Seed Company in South Africa in 2019 and stored until germination. In July-August 2020, tree seeds were clipped and soaked in water for 24-72 hours before planted in potting soil. After sowing, seeds were watered and placed in growth chambers set at 28°C and 500 mmol/m2/s of light for the duration of germination. Grass seeds were germinated on watered potting soil in the same growth chambers. In September 2020, germinated tree seedlings and grasses were transplanted into 30-liter tree pots (Stuewe & Sons, Inc., 0.25 m diameter x 0.60 m height, sold as 8-gallon pots) filled with turface (All-Season Turface, SiteOne Landscaping, New Haven CT). One tree sapling was planted per pot without grass competition, and two grass tufts were planted per pot without tree competition. For competition treatments, one tree sapling and two grass tufts were planted per pot (tree sapling in the middle, one tuft on either side). All pots were fertilized once a week for 16 weeks at their assigned levels from October 2020-January 2021. Low nutrient treatments were designed to replicate nutrient limitation, while high nutrient treatments were designed to alleviate nutrient limitation while avoiding nitrogen or phosphorus toxicity. To determine the appropriate concentrations of N and P for each treatment, we derived monthly estimates of fertilizer from Tomlinson et al. (2019) along with field nutrient estimates from Ludwig et al. (2004). All treatments received 100 mg N and treatments 10 mg P per pot per week to mimic field conditions via ammonium nitrate and potassium phosphate, with treatments receiving 800 mg N and treatments 100 mg P per pot per week. Along with N and P fertilization, each treatment received the same quantity of N- and P-free Hoagland’s micronutrient solution, which included all essential micronutrients: potassium, calcium, sulfur, magnesium, chloride, sodium, manganese, zinc, copper, molybdenum, boron and iron. Pots were located in a single greenhouse bay at Yale Marsh Botanical Gardens in a block-randomized design, kept at 28°C for the duration of the experiment, and supplemented with grow lights for 12 hours a day to replicate a sub-tropical growing season. Soil moisture was measured throughout the experiment to ensure that soil moisture remained constant across pots. Plant Responses At the time of transplanting, 10 individuals of each tree and grass species were harvested for initial size and biomass estimates. For tree seedlings, we recorded starting biomass, stem height, basal diameter, diameter at 10 cm height, root diameter, root length, wet and dry leaf stem mass, and fine (sorted into two diameter classes: 0-1 mm and 1-2 mm) and coarse root (> 2 mm) dry mass. For grasses, we recorded basal circumference, maximum culm and sward height, root diameter, and wet and dry weight of aboveground and belowground grass biomass. Throughout the experiment, measurements were taken every four weeks. Tree measurements consisted of stem height, basal diameter, and diameter at 10 cm. Grass measurements consisted of basal circumference and sward height. Along with tree and grass measurements, light availability was measured using an fPAR meter (fraction of incident photosynthetically active radiation, 400-700 nm) at three different heights (tree base, tree canopy height, and grass height). At the final harvest in January 2021, we repeated all routine measurements and measured wet and dry weights of each plant tissue type. Time to harvest did not differ among species or blocks nor with respect to any experimental treatment (n = 260). To calculate mass gain, we subtracted the mean total dry weight of the 10 tree seedlings or grasses harvested at the start of the experiment from the final total dry weights at the end of the experiment. We used allometric relationships to evaluate whether competition and nutrient treatments influenced the growth form of trees. To do this, we calculated the allometric exponent b as follows (Feldpausch et al. 2011): for both shoot and root systems. For shoots, we used stem height and basal diameter (mm). For roots, we used taproot length for ‘height’ and root diameter (mm). We refer to the traits represented by these allometric exponents as ‘shoot elongation’ and ‘root elongation’, respectively. Competition intensity was calculated by dividing the average mass gain of a tree or grass grown with competition by the average mass gain of a tree or grass grown out of competition and subtracted by one for each nutrient treatment within one species (i.e., only individuals of the same species were compared). Negative values (< 0) indicate competitive effects while positive values (> 0) indicate facilitative effects (Carlyle et al. 2010, Jabot and Pottier 2012). To quantify root phosphatase activity, we used a method adapted from Turner et al. (2001), using para-nitrophenyl (pNPP) as an analogue for phosphomonoesterase, a root phosphatase that hydrolyzes simple phosphate monoesters to release a phosphate ion for plant uptake (Browman and Tabatabai 1978, Tabatabai 1994). For each plant within a pot, we collected three subsamples of fine roots for measuring phosphomonoesterase activity and one for measuring color production without substrate added. For each plant, 500 mg of fresh root was added to a glass vial with 9 mL of 50 mM sodium acetate-acetic acid buffer (pH 5.0) and shaken in a water bath at 28°C for five minutes to simulate wet season surface soil temperatures at Kruger National Park. To initiate the reaction, we added 1.0 mL of 50 mM pNPP (5.0 mM final concentration). The reaction was terminated by mixing 0.5 mL of buffer/substrate solution with 4.5 mL of 0.11 M NaOH, which was then vortexed and measured for absorbance at 405 nm against paranitrophenol (pNP) standards. Two sodium acetate-acetic acid buffer and substrate blanks were run for every assay. Roots were removed from the vials and dried at 65°C for 3 days to express the activity as μmol pNP g-1 dry root mass h-1. Plant essential macronutrients like nitrogen (N) and phosphorus (P) can limit savanna tree growth and are important determinants of savanna vegetation dynamics, along with rainfall, fire, and herbivory. How nitrogen and phosphorus shape tree-grass competition and their coexistence remain unclear, hindering our ability to predict how savannas may respond to altered nutrient cycling. Here, we evaluate (1) if trees and grasses respond differently to N vs. P availability, or (2) if grasses are more competitive in low nutrient environments while trees are more competitive in high nutrient environments. To do this, we grew saplings of 6 tree and 1 grass species from Kruger National Park, South Africa, for 16 weeks under fully factorial nutrient and competition treatments (with/without competitors, low/high rate of N supply, and low/high rate of P supply) under a watering regime designed to mimic wet season rainfall in a mesic savanna. Trees and grasses foraged most aggressively for nitrogen and allocated biomass differently depending on nitrogen availability. Overall, tree growth decreased in competition with grass, even in high nutrient environments where they grew faster. Grasses were always better belowground competitors, utilizing aggressive nutrient foraging strategies, including high root phosphatase activity in response to nitrogen and large root biomass allocation. Synthesis: In low nutrient environments (e.g., on nutrient-poor sandy soils), nutrients may limit tree growth. Nutrient rich environments enable tree growth, but grasses continue to compete effectively with trees. Understanding what this means for ecosystem responses to nutrient availability is not trivial, especially in the context of fire and herbivory. However, it is clear that soil nutrients likely affect tree and grass growth and competition in savannas, which suggests that future changes in nutrient cycling, such as N deposition, may have important effects on savanna vegetation.

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    Authors: Nidamanuri, R;

    Eggplant crop hyperspectral data was acquired using a terrestrial hyperspectral imaging spectroradiometer. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Nidamanuri, R;

    Cabbage crop hyperspectral data was acquired using a terrestrial hyperspectral imaging spectroradiometer. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Nidamanuri, R;

    Cabbage and eggplant crops combined hyperspectral data was acquired using a terrestrial hyperspectral imaging spectroradiometer. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    The study aimed to investigate how implementation of the Enhanced Recovery after Surgery (ERAS) program in patients undergoing robotic hysterectomy for benign indications affects the length of hospital stay (LOHS) in comparison with conventional management. It also aimed to compare postoperative complications within 30 days after surgery and quality of life. THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Pereira Cavalcante de Castro, Pedro Arfux Pereira Cavalcante de Castro;

    This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - October, 2023). Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset. Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG). THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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    Authors: Pereira Cavalcante de Castro, P;

    This is a dataset of an experiment performed in in the School Farm of the Federal University of Mato Grosso do Sul (UFMS). UFMS is in the Midwest region of Brazil (Mato Grosso do Sul State). The school farm is located at 20°26'37.7"S 54°50'58.5"W. The data was collected in a 16ha paddock containing 26 Nelore breed animals with brachiaria Decumbens forage. The dataset contains weight and supplementation data for each animal, Multispectral data, and environmental data for a period of one year (December, 2022 - November, 2023). Along the period, every 13-28 days, all 26 animals in the paddock got their weights registered, as well as their supplementation data (supplementation delivered (kg) per day, average daily weight gain, time of each supplementation, and data for how many times and for how long each animal went to the feeder). For each days-interval, dates (START_DATE and FINAL_DATE), period identification (PERIOD) and day range (AMOUNT_DAYS) were registered, as well as animal identification (ANIMAL), its weight at the start and end (START_WEIGHT and FINAL_WEIGHT), its average daily weight gain (ADG), average daily supplementation (ADS), time for each daily supplementation (SUP_TIME), total supplementation amount for the whole herd (SUP_TOTAL), total time the animal spent at the feeder (TOTAL_TIME), how many times the animal went to the feeder (TOTAL_ATTENDANCE) and total of different days the animal spent at the feeder (TOTAL_DAYS).Specifically, a couple more specific filter were applied to attendance and days data in order to get more specific supplementation data for each animal. We split the data for specific ranges of time ((6-12h), (12-18h), (18-00h), (6-8h), (12-14h), (18-20h)) and for specific date ranges (spring (09/23-12/21), summer (12/22-03/20), autumn (03/23-06/21), winter (06/22-09/22))Multispectral data was acquired looking at the paddock location. The data was collected using Google Earth Engine API, based on Sentinel-2 multispectral images. Twenty sentinel-2 bands (BAND1, BAND2, BAND3, BAND4, BAND5, BAND6, BAND7, BAND8A, BAND9, BAND11, and BAND12) were acquired, and eight spectral indices (NDVI_INDEX, NDWI_INDEX, EVI_INDEX, LAI_INDEX, DVI_INDEX, GCI_INDEX, GEMI_INDEX, and SAVI_INDEX) were calculated and integrated into the dataset. Weather data was also collected using the Open-Meteo weather API. The data acquired are Mean Temperature of the period (TEMP_AVG), Rain sum registered in the period (RAIN_SUM), Average daily rain registered during the period (RAIN_AVG), Solar Radiation sum and Average Solar Radiation registered during the period (RAD_SOL_SUM,RAD_SOL_AVG), Average evapotranspiration registered during the period (EVAPOT), Average Relative Humidity registered during the period (HUM_REL_AVG) and Average Atmospheric Pressure registered during the period (PRES_ATM_AVG). THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOVE

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