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  • Rural Digital Europe
  • Research data
  • European Marine Science

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ahmad, Jamil;

    The dataset consists of field images captured from healthy and diseased peach fruits and leaves. If you use this dataset in your research, please consider citing the below article. Thank you Farman, Haleem, Jamil Ahmad, Bilal Jan, Yasir Shahzad, Muhammad Abdullah, and Atta Ullah. "Efficientnet-based robust recognition of peach plant diseases in field images." Comput. Mater. Contin 71 (2022): 2073-2089.

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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Mendeley Dataarrow_drop_down
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • Authors: Evans, J. S.; Deighan, J.; Jain, S.; Veibell, V.; +19 Authors

    # Retrieval of Ar, N2, O, and CO in the Martian Thermosphere Using Dayglow Limb Observations by EMM EMUS [https://doi.org/10.5061/dryad.dv41ns253](https://doi.org/10.5061/dryad.dv41ns253) The data set contains altitude profiles of observed (kiloRayleighs) and retrieved intensities (Rayleighs) and number densities (cm-3) for Ar, N2, O, and CO for eight EMM insertion orbit observations. The density retrieval method is described by Evans et al. (2015; doi:10.1002/2015GL065489) and Evans et al. (2023; doi:10.1029/2022JE007325). ## Description of the data and file structure The observed intensities are stored in ASCII files contained within a compressed tar file (emus_data.tgz). Retrieved intensities and densities are stored in ASCII files contained within a separate compressed tar file (retrieval_data.tgz). The folder names provide the EMUS observation date and time. The content of each intensity file (Ar1066.txt, CO4PGfull.txt, NI1200.txt, OI1356.txt) is described within the file. The retrieval files (retrieve.out) contain the following information: na, ndat: Number of state vector elements (na-1 densities and one scale factor); number of EMUS data points xa: a priori densities (cm-3) and scale factor (unitless); floating point array with dimension [na] xhat: retrieved densities (cm-3) and forward model scale factor (unitless); floating point array with dimension [na] shat: covariance matrix for retrieved densities (cm-6) and forward model scale factor (unitless); floating point array with dimensions [na,na] lk: zenith angles (degrees) of the EMUS observation; floating point array with dimension [ndat] ya: a priori forward model intensities (R); floating point array with dimension [ndat] yhat: retrieved forward model intensities (R); floating point array with dimension [ndat] The Emirates Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) Hope probe images Mars at wavelengths extending from approximately 100 to 170 nm. EMUS observations began in February 2021 and cover over a full Mars year. We report the first limb scan observations at Mars of ultraviolet emissions Ar I 106.6 nm, N I 120 nm, and carbon monoxide (CO) Fourth Positive Group (A - X) band system excited by electron impact on CO. We use EMUS limb scan observations to retrieve number density profiles of argon, molecular nitrogen, atomic oxygen, and CO in the upper atmosphere of Mars from 130 to 160 km. CO is a sensitive tracer of the thermal profile and winds in Mars' middle atmosphere and the chemistry that balances CO2 in the atmosphere of Mars. EMUS insertion orbit special observations demonstrate that far ultraviolet limb measurements of the Martian thermosphere can be spectroscopically analyzed with a robust retrieval algorithm to further quantify variations of CO composition in the Martian upper atmosphere.

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    DRYAD
    Dataset . 2024
    License: CC 0
    Data sources: Datacite
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      DRYAD
      Dataset . 2024
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Symagulov, Adilkhan;

    Annotated photos of soybean and sugarcane fields for using in the task of object recognition with YOLO neural network (with various classes of weeds). Contains photos and related txt files with annotations.

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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Islam, Md Monirul;

    IoT devices are used in the data-gathering procedure to get real-time information on air pollution in Dhaka, Bangladesh. The hardware design is made to ensure that suitable sensors are fitted to detect the levels of carbon monoxide, nitrogen dioxide, sulfur dioxide, ozone, particulate matter 2.5, and particulate matter 10. To facilitate connectivity with the IoT devices, the Arduino IDE code was uploaded. The appropriate Excel file is opened after starting the Excel data streamer. The Arduino IDE chooses the proper COM port to connect the IoT devices to the PC. The gathered data are stored in a designated file location to facilitate analysis and interpretation. This approach enables future research and decision-making procedures about air pollution in Dhaka. It also ensures continuous monitoring and complete assessment of air quality metrics. The record of the dataset is 1 lac 55 thousand 4 hundred 6.

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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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    Mendeley Data
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Mendeley Dataarrow_drop_down
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      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Mendeley Data
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hu, Yuhang; Chen, Boyuan; Lin, Jiong; Wang, Yunzhe; +3 Authors

    # Dataset for Paper "Human-Robot Facial Co-expression" ## Overview This dataset accompanies the research on human-robot facial co-expression, aiming to enhance nonverbal interaction by training robots to anticipate and simultaneously execute human facial expressions. Our study proposes a method where robots can learn to predict forthcoming human facial expressions and execute them in real time, thereby making the interaction feel more genuine and natural. [https://doi.org/10.5061/dryad.gxd2547t7](https://doi.org/10.5061/dryad.gxd2547t7) ## Description of the data and file structure The dataset is organized into several zip files, each containing different components essential for replicating our study's results or for use in related research projects: * **pred_training_data.zip**: Contains the data used for training the predictive model. This dataset is crucial for developing models that predict human facial expressions based on input frames. * **pred_model.zip**: Contains the trained models capable of predicting human target facial expressions. These models are trained on the data from pred_training_data.zip. * **inverse_model.zip**: Includes trained models for generating motor commands based on landmarks input. These models are vital for translating predicted facial expressions into actionable motor commands for robot facial actuators. * **Coexpression.zip**: Houses the software necessary for data analysis and reproducing the results presented in our paper. It includes code for both the predictive and inverse modeling processes, along with utilities for evaluating model performance. * **0_input.npy:** An example input data file showcasing the structure expected by the predictive model. This NumPy array file has a shape of (9, 2, 113), representing 9 sequential frames, each frame consisting of 2D coordinates for 113 facial landmarks. This sample data can be used as a reference for the format and scale of input data necessary for model training and inference. The training data within pred_training_data.zip is structured to facilitate easy loading into machine learning frameworks. It consists of input facial landmarks and target facial landmarks. The deep neural networks in pred_model.zip and inverse_model.zip is constructed using PyTorch. Details on the model architecture and parameters are included within each zip file. The software provided in Coexpression.zip includes scripts for processing data, training models, and evaluating the system's performance. ## Citation Please cite our work if you use this dataset or the associated models/software in your research: Hu, Yuhang, et al. "Human-Robot Facial Co-expression". *Science Robotics (March 2024)* Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that they appear natural, timely, and genuine. Here we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial co-expression feels more genuine since it requires correctly inferring the human's emotional state for timely execution. We find that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles, and using a learned inverse kinematic facial self-model, co-express the smile simultaneously with the human. We demonstrate this ability using a robot face comprising 26 degrees of freedom. We believe that the ability co-express simultaneous facial expressions could improve human-robot interaction. During the data collection phase, the robot generated symmetrical facial expressions, which we thought can cover most of the situation and could reduce the size of the model. We used an Intel RealSense D435i to capture RGB images and cropped them to 480 320. We logged each motor command value and robot images to form a single data pair without any human labeling.

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    DRYAD; ZENODO
    Dataset . 2024
    License: CC 0
    Data sources: Datacite; ZENODO
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ DRYAD; ZENODOarrow_drop_down
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      DRYAD; ZENODO
      Dataset . 2024
      License: CC 0
      Data sources: Datacite; ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Parra, Adriana; Greenberg, Jonathan;

    This README file was generated on 2024-03-04 by Adriana Parra. ## GENERAL INFORMATION 1\. Title of Dataset: **Climate-limited vegetation change in the conterminous United States of America** 2\. Author Information A. First Author Contact Information Name: Adriana Parra Institution: University of Nevada, Reno Address: Reno, NV USA Email: adrianaparra@unr.edu B. Co-author Contact Information Name: Jonathan Greenberg Institution: University of Nevada, Reno Address: Reno, NV USA Email: jgreenberg@unr.edu 3\. Coverage period of the dataset: 1986-2018 4\. Geographic location of dataset: Conterminous United States 5\. Description: This dataset contains the input and the resulting rasters for the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal. The dataset includes a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor. Additionally, the dataset includes a legend file for the identified climatic limiting factor rasters. ## SHARING/ACCESS INFORMATION 1\. Links to publications that cite or use the data: **Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204)** 2\. Links to other publicly accessible locations of the data: None 3\. Links/relationships to ancillary data sets: None 4\. Was data derived from another source? Yes A. If yes, list source(s): "Vegetative Lifeform Cover from Landsat SR for CONUS" product publicly available in the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) TerraClimate data catalog publicly available at the website https://www.climatologylab.org/terraclimate.html 5\. Recommended citation for this dataset: Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204) ## DATA & FILE OVERVIEW This dataset contains 16 geotiff files, and one csv file. There are 4 geotiff files per each of the lifeform classes evaluated in this study: herbaceous, tree, shrub, and non-vegetation. The files corresponding to each lifeform class are indicated by the first two letters in the file name, HC indicates herbaceous cover, TC indicates tree cover, SC indicates shrub cover, and NC indicates non-vegetation cover. 1\. File List: a) Observed change: Trends of vegetation change between 1986 and 2018. b) Potential predict: Predicted rates of vegetation change form the climate limiting factor analysis. c) Potential observed difference: Difference between the potential and the observed vegetation rates of change. d) Limiting variable: Climate variable identified as the limiting factor for each pixel the conterminous United States. e) Legend of the Limiting variable raster All the geotiff files are stored as Float 32 type, and in CONUS Albers Equal Area coordinate system (EPSG:5070) The csv file included in the dataset is the legend for the limiting variable geotiff files. This file includes the name of the climate variable corresponding to each number in the limiting variable files, as well as information on the variable type and the corresponding time lag. 2\. Relationship between files, if important: None 3\. Additional related data collected that was not included in the current data package: None 4\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA Input data We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover. The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF). Preprocessing of vegetation fractional cover data We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992). Preprocessing of climate variables data To process the climate data, we defined a year time step as the months from July of one year to July of the next. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018. The annual maps of each climate variable were further summarized per pixel, into mean and slope (calculated as the Theil-Sen slope) across one, two, three, four, five, ten-, and 15-year lags. Estimation of climate potential We constructed a final multilayer dataset of response and predictor variables for the CONUS including the resulting maps of fractional cover rate of change (four response variables), the mean and slope maps for the climate variables for all the time-lags (70 predictor variables), and the initial percent cover for each lifeform in the year 1986 (four predictor variables). We evaluated for each pixel in the CONUS which of the predictor variables produced the minimum potential rate of change in fractional cover for each lifeform class. To do that, we first calculated the 100% quantile hull of the distribution of each predictor variable against each response variable. To calculate the 100% quantile of the predictor variables’ distribution we divided the total range of each predictor variable into equal-sized bins. The size and number of bins were set specifically per variable due to differences in their data distribution. For each of the bins, we calculated the maximum value of the vegetation rate of change, which resulted in a lookup table with the lower and upper boundaries of each bin, and the associated maximum rate of change. We constructed a total of 296 lookup tables, one per lifeform class and predictor variable combination. The resulting lookup tables were used to construct spatially explicit maps of maximum vegetation rate of change from each of the predictor variable input rasters, and the final climate potential maps were constructed by stacking all the resulting maps per lifeform class and selecting for each pixel the minimum predicted rate of change and the predictor variable that produced that rate. Identifying climate-limited areas We defined climate-limited areas as the parts of the CONUS with little or no differences between the estimated climate potential and the observed rates of change in fractional cover. To identify these areas, we subtracted the raster of observed rates of change from the raster of climate potential for each lifeform class. In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor.

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    DRYAD; ZENODO
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    Authors: Maß, Virginia;

    Annotated image dataset of Fire blight symptoms for object detection in orchards. Monitoring plant diseases in tree nurseries, breeding farms and orchards is essential for maintaining plant health. One of the most dangerous diseases in fruit growing is fire blight (Erwinia amylovora), as it can spread epidemically and cause enormous economic damage. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards. Data was collected on a total of nine dates in 2021, 2022 and 2023 under different weather conditions and with different cameras. The RGB images were taken of different apple genotypes after artificial inoculation with Erwinia amylovora, including varieties, wild species and progeny from breeding. The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280 x 1280 pixels of fire blight infected shoots, flowers and leaves at different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were recorded and integrated into the ERWIAM dataset as a separate class. The dataset contains a total of 1,611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital plant disease monitoring systems.

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    Authors: Reyes Avila, Alexander;

    The satellite data from 1985 to 2022 to obtain NDWI and surface area data for Lake Yojoa.

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    Authors: Lubitz, Nicolas;

    Study areas Both study areas are located along Western Boundary Currents (WBCs): the Agulhas Current in southern Africa and the East Australian Current in eastern Australia. In southern Africa, the area spans from the warm-temperate Breede River estuary on the south coast of South Africa (34.5°S) to northern Mozambique (16°S) (Figs. 1, 5a main MS). In eastern Australia, the area extends from the warm-temperate Sydney Harbour estuary (33.5˚S) to tropical Queensland (16.5˚S) (Fig. 5b main MS). On both continents, the study areas were divided into two distinctive zones (Fig. 5a, b main MS). First, an upwelling zone, comprising multiple coastal upwelling cells inshore of each WBC, extending from the southern limit equatorward to 32.4°S along the Agulhas Current and 28.6°S along the East Australian Current. This zone includes the Breede River and Sydney Harbour estuaries in South Africa and Australia, respectively (Fig. 5a, b main MS). The second zone, in each case, was the subtropical/tropical section of coastline equatorward of the upwelling zones. Both the Agulhas and East Australian Current closely follow the continental shelf, with their warm cores usually situated between 50-100 km offshore from the shelf edge16, 56. Note that although upwelling can occur in the subtropical/tropical areas to the north, it is most prominent and intense in both upwelling zones16, 30. In the case of the Agulhas Current, coastal upwelling can be caused by current forcing as well as by meanders in the current16, 19, 31. Such meanders can be associated with cold-core inshore eddies that travel south-ward along the shelf, often called “Natal Pulses” 17. Current-driven coastal upwelling is most common along the south-east coast of South Africa, due the proximity of the shelf edge to the coast16, 19, 31. Upwelling-favourable easterly winds can also contribute to coastal upwelling here16. As the shelf widens south of Port Elizabeth, this becomes the main mechanism of coastal upwelling, with a weakening influence of the current. Similarly, along the East Australia Current, current and eddy-driven upwelling mostly occurs along the narrow shelf from 24˚S to 32.5˚S, after which the current separates and wind-driven upwelling becomes the most prominent mechanism31, 32, 56. Collection of environmental variables On March 2nd 2021, dead marine organisms began washing up on beaches inshore of the Agulhas Current, between Port Elizabeth to ca. 100 km north of East London (Fig. 1a main MS). Specimens were collected and cataloged by Greg Brett in association with the East London Museum (Supplemental Table 2). To determine whether rapid-temperature drops associated with upwelling could be responsible for the deaths and, if so, to qualify characteristics of lethal upwelling events, we investigated sea-surface temperature (SST) from satellite imagery, in-situ temperatures at 30 m depth and wind speed and direction. We used the Aqua Modis sea-surface temperature data set from the Moderate Resolution Spectroradiometer on the AQUA NASA satellite, a daily, gridded level-3 data set at 0.04166° resolution, with data availability from 2003-2022. To analyse the duration as well as maximum spatial extent and rate of temperature declines for potential upwelling before, during and after the dead specimens washed up on local beaches, we generated mapped images in ArcGIS based on NetCDF-files downloaded from the OceanColor webserver (https://oceancolor.gsfc.nasa.gov/l3/). Satellite-derived temperature data was complemented with in-situ temperatures collected by a temperature logger deployed 3 km offshore from Port Alfred (Fig. 1a main MS) at 30 m,, which recorded temperature at hourly intervals. Finally, wind data collected by the South African Weather Service at Port Alfred was used to determine whether upwelling-favourable easterly winds (45°-135°) occurred before and during the period dead marine organisms washed up on local beaches. Wind speed in m/s and wind direction in degrees was collected at 6-hour intervals daily between 8 am and 8 pm local time. Modelling trends in upwelling characteristics over time To investigate whether there has been an increasing trend in the proportion of upwelling-favourable winds on the south and south-east coast of South Africa, we used wind data collected by the South African Weather Service from three stations within the upwelling zone. Note that a similar analysis has previously been done for eastern Australia35. Daily wind speed in m/s and wind direction in degrees was collected at 6-hour intervals between 8 am and 8 pm local time. Data was available from November 2001 until March 2022 at Port Alfred, from October 1992 until March 2022 at Port Elizabeth, and from October 1988 until March 2022 at Plettenberg Bay. Based on the orientation of the coastline in the upwelling zone and wind direction in degrees, the data set was divided into upwelling-favourable winds (north-east to south-east (45°-135°) and non-upwelling favourable winds (all other directions)57. To estimate the daily prevailing wind speed and direction, data was averaged per day. Since we aimed to investigate potential impacts of increasing trends in upwelling on mobile marine species at their distributional limit, only wind data between October and April was included in the models. Based on bull shark tracking data from this study, this was the period during which animals tagged in the Breede River and Sydney Harbour estuaries were mainly present in the upwelling zone. Furthermore, this period also encompasses the austral summer months which are the main period of upwelling activity inshore of the Agulhas and East Australian Currents16, 19, 30, 34. Thus, annual data from October to April were combined to represent corresponding upwelling seasons. As a result, we had data for 21 upwelling seasons at Port Alfred, 30 at Port Elizabeth and 34 at Plettenberg Bay. Although timeseries at Port Alfred were shorter than 30 years, which is the recommended length for trend analysis21, we included this site to inspect a potential trend and compare it with Port Elizabeth and Plettenberg Bay sites. We then calculated the proportion of days during which upwelling-favourable winds occurred in each upwelling season. The resulting values were modelled against time (upwelling season) using generalised linear models (GLM) with binomial error distribution. Model fit and diagnostics, including tests for temporal autocorrelation were evaluated using the Dharma package in R. Only trends in the South African study areas were modelled as those in the east Australian coastline has previously been examined35. In order to investigate whether the number, mean intensity (in °C) and the mean rate of onset (in °C/day) of potential upwelling events has increased in upwelling cells inshore of both WBCs, we used the long-term, interpolated, reanalysed, daily Global Ocean OSTIA Sea Surface Temperature data set58. This gridded, level-4 data set covers the period from 10/01/1981 until present at a resolution of 0.05°, combining satellite data as well as in-situ data. We chose this data set because of long-term data availability of 41 years, the high standard of quality control and overall accuracy and applicability59. Although level-4 data sets can underestimate thermal signatures of upwelling zones due to warm biases, the OSTIA data set has been shown to perform better than other long-term data sets36. Furthermore, this bias is more pronounced in Eastern Boundary Current compared to Western Boundary Current upwelling systems33, 59. Next, we extracted sea-surface temperature data for prominent upwelling cells inshore of each WBC. This was off Port Alfred, Port Elizabeth and Plettenberg Bay for the Agulhas Current and Port Stephens, Diamond Head and Ballina for the East Australian Current16, 19, 30, 34, 37, 47. We then divided the shelf into inshore (0-15 km from shore) and midshelf (15-30 km from shore) sections. For each station, a pixel was randomly selected from inshore and midshelf sections from the gridded OSTIA SST data set along a transect (Fig. 7 main MS). Each transect extended from the coastline, across the shelf in a direction perpendicular to the shelf edge. Choosing inshore and midshelf pixel allowed us to compare trends across areas of varying shelf width where each WBC would influence upwelling differentially. Additionally, as a control, we selected an offshore pixel within the warm core of each current along the same transects for each station. No upwelling occurs within the offshore core of the currents and possible cold events would be due to temperature anomalies other than upwelling, hence no trends should be evident. We used the R package heatwaveR to detect cold events in each randomly selected pixel over time60. The algorithm implemented in this package is based on the hierarchical definition of extreme temperature events in21 and follows a similar methodology to characterising such events. In short, marine extreme temperature events are defined as discrete, anomalously warm or cold events with characteristics such as intensity, rate of onset, and spatial extent21. For extreme cold events, these characteristics are calculated in cases when temperatures fall below the 10th percentile threshold, which is based on a baseline climatology at least 30 years in duration to avoid impacts of decadal variability. Thus, data sets with long-term data availability, such as the OSTIA SST data set, available from 01/01/1981-present, are preferred21. Before calculating the baseline climatology at each pixel, we detrended the daily OSTIA sea surface temperature data as long-term trends in the climatology can affect detection of extreme temperature events using the detrend() function61. Within the detect_event() function of the heatwaveR package, we set the minimum duration for cold events to one day, in order to include short-term upwelling events which are common along both WBCs57. From the output of this function, we summed all discrete cold events each year from October to April, the time period tagged sharks were mainly present in both upwelling zones. We also calculated the annual mean intensity (in °C) and mean rate of onset (°C/day) for events from October to April. Visual inspection of the output suggested that trends in either variable remained similar using detrended and non-detrended data. We used generalised linear models to investigate trends in the number, mean intensity and mean rate of onset of upwelling events at each pixel between 1981 and 2022, with upwelling season (aggregated annually between October and April) as the independent variable. The number of upwelling events was modelled assuming a Poisson error distribution with a log-link function, while mean intensity and mean rate of onset were modelled assuming a Gaussian error distribution. Model fit and diagnostics were inspected using the Dharma package. When significant non-linearity and/or quantile deviations were detected, we fitted generalised additive models (GAMS) (Supplemental Tables 6 and 7), which were also inspected using Dharma and the gam.check() function in the mgcv package. Finally, as decadal variability and biases associated with L4-satellite SST products can confound trends in upwelling signals, we utilised temperature logger data from four locations within the upwelling cells of the Agulhas current (deployments from 1991 to 2022) and three different satellite SST products to validate our analyses above. Loggers were Star OddiTM temperature loggers with accuracy of ± 0.05°C located at Ystervarkpunt (78 km east of the Breede River), Mossel Bay (127 km east of the Breede River), Tsitsikamma (37 km east of Plettenberg Bay) and Mosterts Hoek (85 km west of Port Elizabeth) and serviced every 1-2 years (for deployment details see Supplemental Table 5). Satellite SST products used were the L4 OSTIA SST dataset from our original analyses (5 km resolution), the L4 CCI SST product (5 km resolution) and the L3 Pathfinder AVHRR night and day products combined into a single product (5 km resolution). Warming/cooling trends in SST products and logger data were estimated using the Theil-Sen estimator. The estimator is nonparametric and hence more robust than the parametric least-squares regression as it is resistant to outliers and does not draw from any probability distribution. Theil-Sen uses the median to estimate the slope as opposed to least-squares which makes use of a weighted mean for slope estimation. Spatial SST trends were calculated using each grid point within the designated area of the gridded SST product. Additionally, the SST and logger data trends were calculated using the anomalies to avoid the complicating effects of seasonality on trend values. Trend analyses for all logger locations and SST products ranged from June 1991 to August 2022. Animal tagging and tracking Tagged bull sharks were part of previously published data sets from southern Africa and eastern Australia and were acoustically tracked in coastal receiver arrays. For details about tagging methods and receiver arrays see27, 39, 62 , Supplemental Table 8). In short, in Australia, 25 bull sharks were internally tagged in Sydney Harbour with InnovaSeaTM V16TP tags that also recorded depth and temperature at the time of detection (tag nominal delay 30-90 s). Out of 41 bull sharks acoustically tagged in southern Africa as part of a separate study 27, 62, 15 individuals were also equipped with temperature and depth recording pop-up satellite archival transmitters (Wildlife ComputersTM Mk10-PSAT and miniPAT tags) and included again in this study (Supplemental Table 8). Five of these sharks were tagged inside the warm-temperate Breede River Estuary, and 10 at the subtropical Pinnacle Reef. PSAT-tags archived depth, temperature, and light level data at pre-programmed intervals of three seconds (n=1), five seconds (n=11) and 25 seconds (n=3). Additionally, binned data at 24-hour intervals was collected for temperature (9-11°C, 11-13°C, 13-15°C, 15-17°C, 17-19°C, 19-21°C, 21-23°C, 23-25°C, 25-27°C, 27-29°C, 29-31°C, 31-33°C) and depth (0-5m, 5-10m, 10-20m, 20-30m, 30-40m, 40-50m, 50-100m, 100-200m, 200-300m, 300-400m) for all PSAT-tags. Tags were released from animals after pre-programmed deployment durations (90-360 days). Tags then floated to the surface and transmitted data to the Argos satellites. Out of the 15 PSAT-tags deployed, 10 were physically recovered by the authors via boat, providing full archived temperature and depth data (one from a bull shark tagged at the Breede River and 9 from Pinnacle Reef). Transmitted data was initially decoded using the manufacturer’s software (WC-DAP 3.0, Wildlife Computers, Inc., Redmond, WA). The Wildlife ComputersTM GPE3 State-Space model was used to generate estimates of most likely locations from acoustic detections and PSAT-tag light level data using a swimming speed of 0.7 m/s64 and to create movement tracks for each shark (Supplemental Figs. 19-23). Then, in the statistical software R63 each shark location was matched with depth and temperature data collected by the PSAT-tags to presence in the two regions of interest: The upwelling zone (distinguishing locations inside the Breede River and outside), and the subtropical/tropical areas past the upwelling zone (Supplemental Figs. 19-23). For example, if an acoustic detection or geolocation estimate occurred in the upwelling zone (Fig. 5 main MS), the corresponding depth and temperature value at this timestamp was assigned to the upwelling zone. Note, this was only done for sharks tagged in the Breede River, as animals tagged at Pinnacle Reef remained in subtropical/tropical areas with limited upwelling. Similarly, for sharks acoustically tagged in Sydney Harbour, detections at receivers were assigned to either being in the upwelling zone, (distinguishing detections inside Sydney Harbour and outside), or past the upwelling zone (subtropical-tropical Queensland) (Fig. 5b main MS). Studies from multiple geographical locations have shown that bull shark abundance declines significantly when seasonal temperatures fall below a long-term thermal affinity threshold of 19°C38, 39, 40, 41. To further test this threshold and investigate thermal preferences for bull sharks, we established a tentative thermal range, using bull shark occurrence records in southern Africa and eastern Australia from the Global Biodiversty Information Facility (GBIF)65in combination with our acoustic detections. For this, we filtered combined bull shark occurrences to daily, verifed human and machine observations and spatio-temporally matched each occurrence with a pixel from the AQUA Modis L3-satellite temperature data set. Similar analysis using occurrence data was also performed for three megafauna species that have had fatalities in severe upwelling events, the whale shark Rhincodon typus manta rays, Mobula birostris and Mobula alfredi. For South African sharks for which PSATs were retrieved and full archival data was available (Supplemental Table 15), we plotted continuous temperature and depth over time, while indicating the occupied zone. Binned data from all Breede River bull sharks (full archival and partial data) was used to calculate percent of time at depth and temperatures to compare this in both upwelling and non-upwelling zones Pinnacle Reef sharks were detected only outside of upwelling cells). The maximum duration in consecutive hours, for which the Breede sharks were experiencing temperatures below 19°C for each zone was also calculated. Similarly, we binned temperature and depth data recorded from acoustic detections of bull sharks tagged in Sydney Harbour to determine percent of time at depth/temperature. Acoustic detections at depth/temperature were also plotted over time for sharks tagged in Sydney Harbour and indicated in which zone they occurred. Lastly, data from temperature loggers deployed outside the Breede estuary at 20 m and off Port Alfred at 30 m, as well as the loggers used for Theil-Sen estimation (see previous section) were used to spatio-temporally match temperature and depth recorded by PSAT-tags of the Breede River sharks with upwelling events at nearby loggers based on shark acoustic detections/PSAT-geolocations. This was plotted in combination with a detection timeline from combined acoustic and PSAT-tag locations to indicate when and where PSAT-tagged sharks encountered active upwelling cells. Temperature data for outside Sydney Harbour was recorded by a mooring located 14 km offshore, between 12-20 m depth, which was used to plot upwelling events with acoustic shark detections. Method References 56. Archer MR, Roughan M, Keating SR, Schaeffer A. On the variability of the East Australian Current: Jet structure, meandering, and influence on shelf circulation. Journal of Geophysical Research: Oceans 2017, 122(11): 8464-8481. 57. Abrahams A, Schlegel RW, Smit AJ. A novel approach to quantify metrics of upwelling intensity, frequency, and duration. PLoS One 2021, 16(7): e0254026. 58. Good S, Fiedler E, Mao C, Martin MJ, Maycock A, Reid R, et al. The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses. Remote Sensing 2020, 12(4): 720. 59. Yang C, Leonelli FE, Marullo S, Artale V, Beggs H, Nardelli BB, et al. Sea surface temperature intercomparison in the framework of the Copernicus Climate Change Service (C3S). Journal of Climate 2021, 34(13): 5257-5283. 60. Schlegel RW, Smit AJ. heatwaveR: A central algorithm for the detection of heatwaves and cold-spells. Journal of Open Source Software 2018, 3(27): 821. 61. Schlegel RW, Oliver EC, Hobday AJ, Smit AJ. Detecting marine heatwaves with sub-optimal data. Frontiers in Marine Science 2019, 6: 737. 62. Daly R, Smale MJ, Cowley PD, Froneman PW. Residency patterns and migration dynamics of adult bull sharks (Carcharhinus leucas) on the east coast of southern Africa. PloS one 2014, 9(10): e109357. 63. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ 64. Brunnschweiler JM. Sharksucker–shark interaction in two carcharhinid species. Marine Ecology 2006, 27(1): 89-94. 65. Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J., & Bates, A. E. (2015). Thermal biases and vulnerability to warming in the world’s marine fauna. Nature, 528(7580), 88-92. # Climate-change-driven cooling can kill marine megafauna at their distributional limits Bull Shark PSAT Data and example code for modeling of upwelling variables: ## Description of the Data and file structure These data include two different data sets Data set 1 contains 5 csv. files with raw depth and temperature data collected by PSAT-tags externally attached to bull sharks tagged in the Breede River in South Africa (n=5, 1 per shark). For this study, recorded depth (in m) and temperature (in °C) were the main variables used. For Shark ID1, ID2, ID4 and ID5 PSAT-tag series data was available. This includes the columns DeployID and PTT which are each sharks individual identifier number, DepthSensor which is the depth sensor resolution in m, a Source column which describes the source of the data, in the case of our PSAT-tags this is by Transmission, an Instrument column which describes the tag type, in our case MiniPAT, Day and Time columns provided in UTC. The Depth column provides Depth in m, while the DepthRange column provides the depth range experienced by each individual shark during each time bin (in our case 10 min interval). Similarly, the Temperature column provides temperature values in °C with the TemperatureRange column indicating the range of temperatures are shark experienced in a binned time interval of 10 mins. Activity and ARange columns were not used for this study. LocationQuality, Latitude and Longitude are not given in Series data and will populated with NA. Lastly, for SharkID3 full Archival Data was available, thus the data structure for this individual differs slightly. Here, the Time column provides a combined date and time value in UTC, with Depth and Temperature given in meters and °C respectively. Lightlevel data is used to determine geolocation for an animal at a given timestamp by measuring light intensity in W cm^-2. Columns Ax, Ay, Az, Dry, Aux, One Minute Light Level, Smoothed Light Level and Events are not applicable to this study and can be ignored. Combined tracks for each shark tagged in the Breede River are given in supplemental figures 18-22 to match with depth/temperature values. All rows containing NA means that tags were unable to measure and/or transmit to a satellite the given value at that time stamp. Data set 2 includes example code for how we modelled number of upwelling events, mean intensity of upwelling events and mean rate of onset of upwelling events over time (see methods). For this, we also included a Net_CDF file for an example location which includes a column for sea surface temperature and Date. All other data used in the MS is publicly available at: Both the OSTIA and the CCI satellite SST product were accessed at [https://marine.copernicus.eu/](https://marine.copernicus.eu/). The AVHRR Night satellite SST product was accessed at [https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstnmday_Lon0360.html](https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstnmday_Lon0360.html), while AVHRR Day satellite SST product was accessed at [https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstdmday_Lon0360.html](https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstdmday_Lon0360.html). The L3 Aqua-Modis satellite SST product was accessed at [https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1sstd1dayR20190SQ.html](https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1sstd1dayR20190SQ.html). These data sets provide temperature in °C, a date and latitude and longitude in decimal degrees. NetCDF-files used to map the progression of the lethal upwelling event were downloaded from the OceanColor webserver ([https://oceancolor.gsfc.nasa.gov/l3/](https://oceancolor.gsfc.nasa.gov/l3/). Long-term wind data for South Africa can be requested from the South African Weather Service [https://www.weathersa.co.za/](https://www.weathersa.co.za/). Wind data for key dates in main manuscript is supplied in the supplemental material, including a column for wind speed in m/s and wind direction in degrees for a given date. Data associated with bull sharks tagged in Sydney Harbour (temperature in °C, depth in m and acoustic detections at a given receiver) is publicly available from the animal tracking facility at IMOS [https://animaltracking.aodn.org.au/](https://animaltracking.aodn.org.au/) (see supplemental material for tagging dates of sharks). Similarly, acoustic tracking data for bull sharks tagged in Southern Africa is available from the South African Acoustic Tracking Array Platform [https://www.saiab.ac.za/atap.htm](https://www.saiab.ac.za/atap.htm). Mooring temperature data for Sydney in °C is publicly available at the Australian Ocean Data Network on [https://portal.aodn.org.au/](https://portal.aodn.org.au/) (Mooring code: SYD100). In-situ temperature data collected by a logger at Port Alfred in in °C is supplied in the supplemental material. The impacts on marine species from secular warming and heatwaves are well demonstrated, however the impacts of extreme cold events are poorly understood. Here, we link the death of organisms from 81 species to an intense cold upwelling event in the Agulhas Current, and show trends of increasing frequency and intensification of upwelling in the Agulhas Current and East Australian Current. Using electronic tagging, we illustrate potential impacts of upwelling events on the movement behaviour of bull sharks, Carcharhinus leucas including alterations of migratory patterns and maintenance of shallower dive profiles when transiting through upwelling cells. Increasing upwelling could result in “bait-and-switch” situations, where climate change expands subtropical species’ distribution, while simultaneously exposing climate-migrants to increased risk of cold-stun/mortality events at poleward distributional limits. This shows the potential impacts of increased cold events, an understudied aspect of climate-change research, and highlights the complexities of climate change effects on marine ecosystems.

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    Authors: Bafana, P;

    This dataset contains the R-codes used to create the figures/charts (8-11) in the review article titled: "Invasive Alien Plant species detection using remote sensing, machine learning, and deep learning: A review." The software used to produce the code for the charts was the RStudio 2022.07.0+548 "Spotted Wakerobin" Release (34ea3031089fa4e38738a9256d6fa6d70629c822, 2022-07-06) for Windows, Build 548, 2009-2022, PBC. 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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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ahmad, Jamil;

    The dataset consists of field images captured from healthy and diseased peach fruits and leaves. If you use this dataset in your research, please consider citing the below article. Thank you Farman, Haleem, Jamil Ahmad, Bilal Jan, Yasir Shahzad, Muhammad Abdullah, and Atta Ullah. "Efficientnet-based robust recognition of peach plant diseases in field images." Comput. Mater. Contin 71 (2022): 2073-2089.

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  • Authors: Evans, J. S.; Deighan, J.; Jain, S.; Veibell, V.; +19 Authors

    # Retrieval of Ar, N2, O, and CO in the Martian Thermosphere Using Dayglow Limb Observations by EMM EMUS [https://doi.org/10.5061/dryad.dv41ns253](https://doi.org/10.5061/dryad.dv41ns253) The data set contains altitude profiles of observed (kiloRayleighs) and retrieved intensities (Rayleighs) and number densities (cm-3) for Ar, N2, O, and CO for eight EMM insertion orbit observations. The density retrieval method is described by Evans et al. (2015; doi:10.1002/2015GL065489) and Evans et al. (2023; doi:10.1029/2022JE007325). ## Description of the data and file structure The observed intensities are stored in ASCII files contained within a compressed tar file (emus_data.tgz). Retrieved intensities and densities are stored in ASCII files contained within a separate compressed tar file (retrieval_data.tgz). The folder names provide the EMUS observation date and time. The content of each intensity file (Ar1066.txt, CO4PGfull.txt, NI1200.txt, OI1356.txt) is described within the file. The retrieval files (retrieve.out) contain the following information: na, ndat: Number of state vector elements (na-1 densities and one scale factor); number of EMUS data points xa: a priori densities (cm-3) and scale factor (unitless); floating point array with dimension [na] xhat: retrieved densities (cm-3) and forward model scale factor (unitless); floating point array with dimension [na] shat: covariance matrix for retrieved densities (cm-6) and forward model scale factor (unitless); floating point array with dimensions [na,na] lk: zenith angles (degrees) of the EMUS observation; floating point array with dimension [ndat] ya: a priori forward model intensities (R); floating point array with dimension [ndat] yhat: retrieved forward model intensities (R); floating point array with dimension [ndat] The Emirates Ultraviolet Spectrometer (EMUS) onboard the Emirates Mars Mission (EMM) Hope probe images Mars at wavelengths extending from approximately 100 to 170 nm. EMUS observations began in February 2021 and cover over a full Mars year. We report the first limb scan observations at Mars of ultraviolet emissions Ar I 106.6 nm, N I 120 nm, and carbon monoxide (CO) Fourth Positive Group (A - X) band system excited by electron impact on CO. We use EMUS limb scan observations to retrieve number density profiles of argon, molecular nitrogen, atomic oxygen, and CO in the upper atmosphere of Mars from 130 to 160 km. CO is a sensitive tracer of the thermal profile and winds in Mars' middle atmosphere and the chemistry that balances CO2 in the atmosphere of Mars. EMUS insertion orbit special observations demonstrate that far ultraviolet limb measurements of the Martian thermosphere can be spectroscopically analyzed with a robust retrieval algorithm to further quantify variations of CO composition in the Martian upper atmosphere.

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    Authors: Symagulov, Adilkhan;

    Annotated photos of soybean and sugarcane fields for using in the task of object recognition with YOLO neural network (with various classes of weeds). Contains photos and related txt files with annotations.

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    Authors: Islam, Md Monirul;

    IoT devices are used in the data-gathering procedure to get real-time information on air pollution in Dhaka, Bangladesh. The hardware design is made to ensure that suitable sensors are fitted to detect the levels of carbon monoxide, nitrogen dioxide, sulfur dioxide, ozone, particulate matter 2.5, and particulate matter 10. To facilitate connectivity with the IoT devices, the Arduino IDE code was uploaded. The appropriate Excel file is opened after starting the Excel data streamer. The Arduino IDE chooses the proper COM port to connect the IoT devices to the PC. The gathered data are stored in a designated file location to facilitate analysis and interpretation. This approach enables future research and decision-making procedures about air pollution in Dhaka. It also ensures continuous monitoring and complete assessment of air quality metrics. The record of the dataset is 1 lac 55 thousand 4 hundred 6.

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    Authors: Hu, Yuhang; Chen, Boyuan; Lin, Jiong; Wang, Yunzhe; +3 Authors

    # Dataset for Paper "Human-Robot Facial Co-expression" ## Overview This dataset accompanies the research on human-robot facial co-expression, aiming to enhance nonverbal interaction by training robots to anticipate and simultaneously execute human facial expressions. Our study proposes a method where robots can learn to predict forthcoming human facial expressions and execute them in real time, thereby making the interaction feel more genuine and natural. [https://doi.org/10.5061/dryad.gxd2547t7](https://doi.org/10.5061/dryad.gxd2547t7) ## Description of the data and file structure The dataset is organized into several zip files, each containing different components essential for replicating our study's results or for use in related research projects: * **pred_training_data.zip**: Contains the data used for training the predictive model. This dataset is crucial for developing models that predict human facial expressions based on input frames. * **pred_model.zip**: Contains the trained models capable of predicting human target facial expressions. These models are trained on the data from pred_training_data.zip. * **inverse_model.zip**: Includes trained models for generating motor commands based on landmarks input. These models are vital for translating predicted facial expressions into actionable motor commands for robot facial actuators. * **Coexpression.zip**: Houses the software necessary for data analysis and reproducing the results presented in our paper. It includes code for both the predictive and inverse modeling processes, along with utilities for evaluating model performance. * **0_input.npy:** An example input data file showcasing the structure expected by the predictive model. This NumPy array file has a shape of (9, 2, 113), representing 9 sequential frames, each frame consisting of 2D coordinates for 113 facial landmarks. This sample data can be used as a reference for the format and scale of input data necessary for model training and inference. The training data within pred_training_data.zip is structured to facilitate easy loading into machine learning frameworks. It consists of input facial landmarks and target facial landmarks. The deep neural networks in pred_model.zip and inverse_model.zip is constructed using PyTorch. Details on the model architecture and parameters are included within each zip file. The software provided in Coexpression.zip includes scripts for processing data, training models, and evaluating the system's performance. ## Citation Please cite our work if you use this dataset or the associated models/software in your research: Hu, Yuhang, et al. "Human-Robot Facial Co-expression". *Science Robotics (March 2024)* Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that they appear natural, timely, and genuine. Here we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial co-expression feels more genuine since it requires correctly inferring the human's emotional state for timely execution. We find that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles, and using a learned inverse kinematic facial self-model, co-express the smile simultaneously with the human. We demonstrate this ability using a robot face comprising 26 degrees of freedom. We believe that the ability co-express simultaneous facial expressions could improve human-robot interaction. During the data collection phase, the robot generated symmetrical facial expressions, which we thought can cover most of the situation and could reduce the size of the model. We used an Intel RealSense D435i to capture RGB images and cropped them to 480 320. We logged each motor command value and robot images to form a single data pair without any human labeling.

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    Authors: Parra, Adriana; Greenberg, Jonathan;

    This README file was generated on 2024-03-04 by Adriana Parra. ## GENERAL INFORMATION 1\. Title of Dataset: **Climate-limited vegetation change in the conterminous United States of America** 2\. Author Information A. First Author Contact Information Name: Adriana Parra Institution: University of Nevada, Reno Address: Reno, NV USA Email: adrianaparra@unr.edu B. Co-author Contact Information Name: Jonathan Greenberg Institution: University of Nevada, Reno Address: Reno, NV USA Email: jgreenberg@unr.edu 3\. Coverage period of the dataset: 1986-2018 4\. Geographic location of dataset: Conterminous United States 5\. Description: This dataset contains the input and the resulting rasters for the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal. The dataset includes a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor. Additionally, the dataset includes a legend file for the identified climatic limiting factor rasters. ## SHARING/ACCESS INFORMATION 1\. Links to publications that cite or use the data: **Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204)** 2\. Links to other publicly accessible locations of the data: None 3\. Links/relationships to ancillary data sets: None 4\. Was data derived from another source? Yes A. If yes, list source(s): "Vegetative Lifeform Cover from Landsat SR for CONUS" product publicly available in the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) TerraClimate data catalog publicly available at the website https://www.climatologylab.org/terraclimate.html 5\. Recommended citation for this dataset: Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204) ## DATA & FILE OVERVIEW This dataset contains 16 geotiff files, and one csv file. There are 4 geotiff files per each of the lifeform classes evaluated in this study: herbaceous, tree, shrub, and non-vegetation. The files corresponding to each lifeform class are indicated by the first two letters in the file name, HC indicates herbaceous cover, TC indicates tree cover, SC indicates shrub cover, and NC indicates non-vegetation cover. 1\. File List: a) Observed change: Trends of vegetation change between 1986 and 2018. b) Potential predict: Predicted rates of vegetation change form the climate limiting factor analysis. c) Potential observed difference: Difference between the potential and the observed vegetation rates of change. d) Limiting variable: Climate variable identified as the limiting factor for each pixel the conterminous United States. e) Legend of the Limiting variable raster All the geotiff files are stored as Float 32 type, and in CONUS Albers Equal Area coordinate system (EPSG:5070) The csv file included in the dataset is the legend for the limiting variable geotiff files. This file includes the name of the climate variable corresponding to each number in the limiting variable files, as well as information on the variable type and the corresponding time lag. 2\. Relationship between files, if important: None 3\. Additional related data collected that was not included in the current data package: None 4\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA Input data We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover. The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF). Preprocessing of vegetation fractional cover data We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992). Preprocessing of climate variables data To process the climate data, we defined a year time step as the months from July of one year to July of the next. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018. The annual maps of each climate variable were further summarized per pixel, into mean and slope (calculated as the Theil-Sen slope) across one, two, three, four, five, ten-, and 15-year lags. Estimation of climate potential We constructed a final multilayer dataset of response and predictor variables for the CONUS including the resulting maps of fractional cover rate of change (four response variables), the mean and slope maps for the climate variables for all the time-lags (70 predictor variables), and the initial percent cover for each lifeform in the year 1986 (four predictor variables). We evaluated for each pixel in the CONUS which of the predictor variables produced the minimum potential rate of change in fractional cover for each lifeform class. To do that, we first calculated the 100% quantile hull of the distribution of each predictor variable against each response variable. To calculate the 100% quantile of the predictor variables’ distribution we divided the total range of each predictor variable into equal-sized bins. The size and number of bins were set specifically per variable due to differences in their data distribution. For each of the bins, we calculated the maximum value of the vegetation rate of change, which resulted in a lookup table with the lower and upper boundaries of each bin, and the associated maximum rate of change. We constructed a total of 296 lookup tables, one per lifeform class and predictor variable combination. The resulting lookup tables were used to construct spatially explicit maps of maximum vegetation rate of change from each of the predictor variable input rasters, and the final climate potential maps were constructed by stacking all the resulting maps per lifeform class and selecting for each pixel the minimum predicted rate of change and the predictor variable that produced that rate. Identifying climate-limited areas We defined climate-limited areas as the parts of the CONUS with little or no differences between the estimated climate potential and the observed rates of change in fractional cover. To identify these areas, we subtracted the raster of observed rates of change from the raster of climate potential for each lifeform class. In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor.

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    Authors: Maß, Virginia;

    Annotated image dataset of Fire blight symptoms for object detection in orchards. Monitoring plant diseases in tree nurseries, breeding farms and orchards is essential for maintaining plant health. One of the most dangerous diseases in fruit growing is fire blight (Erwinia amylovora), as it can spread epidemically and cause enormous economic damage. In this context, a digital disease monitoring system for fire blight based on RGB images was developed for orchards. Data was collected on a total of nine dates in 2021, 2022 and 2023 under different weather conditions and with different cameras. The RGB images were taken of different apple genotypes after artificial inoculation with Erwinia amylovora, including varieties, wild species and progeny from breeding. The presented ERWIAM dataset contains manually labelled RGB images with a size of 1280 x 1280 pixels of fire blight infected shoots, flowers and leaves at different stages of development as well as background images without symptoms. In addition, symptoms of other plant diseases were recorded and integrated into the ERWIAM dataset as a separate class. The dataset contains a total of 1,611 annotated images and 87 background images. This dataset can be used as a resource for researchers and developers working on digital plant disease monitoring systems.

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    Authors: Reyes Avila, Alexander;

    The satellite data from 1985 to 2022 to obtain NDWI and surface area data for Lake Yojoa.

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    Authors: Lubitz, Nicolas;

    Study areas Both study areas are located along Western Boundary Currents (WBCs): the Agulhas Current in southern Africa and the East Australian Current in eastern Australia. In southern Africa, the area spans from the warm-temperate Breede River estuary on the south coast of South Africa (34.5°S) to northern Mozambique (16°S) (Figs. 1, 5a main MS). In eastern Australia, the area extends from the warm-temperate Sydney Harbour estuary (33.5˚S) to tropical Queensland (16.5˚S) (Fig. 5b main MS). On both continents, the study areas were divided into two distinctive zones (Fig. 5a, b main MS). First, an upwelling zone, comprising multiple coastal upwelling cells inshore of each WBC, extending from the southern limit equatorward to 32.4°S along the Agulhas Current and 28.6°S along the East Australian Current. This zone includes the Breede River and Sydney Harbour estuaries in South Africa and Australia, respectively (Fig. 5a, b main MS). The second zone, in each case, was the subtropical/tropical section of coastline equatorward of the upwelling zones. Both the Agulhas and East Australian Current closely follow the continental shelf, with their warm cores usually situated between 50-100 km offshore from the shelf edge16, 56. Note that although upwelling can occur in the subtropical/tropical areas to the north, it is most prominent and intense in both upwelling zones16, 30. In the case of the Agulhas Current, coastal upwelling can be caused by current forcing as well as by meanders in the current16, 19, 31. Such meanders can be associated with cold-core inshore eddies that travel south-ward along the shelf, often called “Natal Pulses” 17. Current-driven coastal upwelling is most common along the south-east coast of South Africa, due the proximity of the shelf edge to the coast16, 19, 31. Upwelling-favourable easterly winds can also contribute to coastal upwelling here16. As the shelf widens south of Port Elizabeth, this becomes the main mechanism of coastal upwelling, with a weakening influence of the current. Similarly, along the East Australia Current, current and eddy-driven upwelling mostly occurs along the narrow shelf from 24˚S to 32.5˚S, after which the current separates and wind-driven upwelling becomes the most prominent mechanism31, 32, 56. Collection of environmental variables On March 2nd 2021, dead marine organisms began washing up on beaches inshore of the Agulhas Current, between Port Elizabeth to ca. 100 km north of East London (Fig. 1a main MS). Specimens were collected and cataloged by Greg Brett in association with the East London Museum (Supplemental Table 2). To determine whether rapid-temperature drops associated with upwelling could be responsible for the deaths and, if so, to qualify characteristics of lethal upwelling events, we investigated sea-surface temperature (SST) from satellite imagery, in-situ temperatures at 30 m depth and wind speed and direction. We used the Aqua Modis sea-surface temperature data set from the Moderate Resolution Spectroradiometer on the AQUA NASA satellite, a daily, gridded level-3 data set at 0.04166° resolution, with data availability from 2003-2022. To analyse the duration as well as maximum spatial extent and rate of temperature declines for potential upwelling before, during and after the dead specimens washed up on local beaches, we generated mapped images in ArcGIS based on NetCDF-files downloaded from the OceanColor webserver (https://oceancolor.gsfc.nasa.gov/l3/). Satellite-derived temperature data was complemented with in-situ temperatures collected by a temperature logger deployed 3 km offshore from Port Alfred (Fig. 1a main MS) at 30 m,, which recorded temperature at hourly intervals. Finally, wind data collected by the South African Weather Service at Port Alfred was used to determine whether upwelling-favourable easterly winds (45°-135°) occurred before and during the period dead marine organisms washed up on local beaches. Wind speed in m/s and wind direction in degrees was collected at 6-hour intervals daily between 8 am and 8 pm local time. Modelling trends in upwelling characteristics over time To investigate whether there has been an increasing trend in the proportion of upwelling-favourable winds on the south and south-east coast of South Africa, we used wind data collected by the South African Weather Service from three stations within the upwelling zone. Note that a similar analysis has previously been done for eastern Australia35. Daily wind speed in m/s and wind direction in degrees was collected at 6-hour intervals between 8 am and 8 pm local time. Data was available from November 2001 until March 2022 at Port Alfred, from October 1992 until March 2022 at Port Elizabeth, and from October 1988 until March 2022 at Plettenberg Bay. Based on the orientation of the coastline in the upwelling zone and wind direction in degrees, the data set was divided into upwelling-favourable winds (north-east to south-east (45°-135°) and non-upwelling favourable winds (all other directions)57. To estimate the daily prevailing wind speed and direction, data was averaged per day. Since we aimed to investigate potential impacts of increasing trends in upwelling on mobile marine species at their distributional limit, only wind data between October and April was included in the models. Based on bull shark tracking data from this study, this was the period during which animals tagged in the Breede River and Sydney Harbour estuaries were mainly present in the upwelling zone. Furthermore, this period also encompasses the austral summer months which are the main period of upwelling activity inshore of the Agulhas and East Australian Currents16, 19, 30, 34. Thus, annual data from October to April were combined to represent corresponding upwelling seasons. As a result, we had data for 21 upwelling seasons at Port Alfred, 30 at Port Elizabeth and 34 at Plettenberg Bay. Although timeseries at Port Alfred were shorter than 30 years, which is the recommended length for trend analysis21, we included this site to inspect a potential trend and compare it with Port Elizabeth and Plettenberg Bay sites. We then calculated the proportion of days during which upwelling-favourable winds occurred in each upwelling season. The resulting values were modelled against time (upwelling season) using generalised linear models (GLM) with binomial error distribution. Model fit and diagnostics, including tests for temporal autocorrelation were evaluated using the Dharma package in R. Only trends in the South African study areas were modelled as those in the east Australian coastline has previously been examined35. In order to investigate whether the number, mean intensity (in °C) and the mean rate of onset (in °C/day) of potential upwelling events has increased in upwelling cells inshore of both WBCs, we used the long-term, interpolated, reanalysed, daily Global Ocean OSTIA Sea Surface Temperature data set58. This gridded, level-4 data set covers the period from 10/01/1981 until present at a resolution of 0.05°, combining satellite data as well as in-situ data. We chose this data set because of long-term data availability of 41 years, the high standard of quality control and overall accuracy and applicability59. Although level-4 data sets can underestimate thermal signatures of upwelling zones due to warm biases, the OSTIA data set has been shown to perform better than other long-term data sets36. Furthermore, this bias is more pronounced in Eastern Boundary Current compared to Western Boundary Current upwelling systems33, 59. Next, we extracted sea-surface temperature data for prominent upwelling cells inshore of each WBC. This was off Port Alfred, Port Elizabeth and Plettenberg Bay for the Agulhas Current and Port Stephens, Diamond Head and Ballina for the East Australian Current16, 19, 30, 34, 37, 47. We then divided the shelf into inshore (0-15 km from shore) and midshelf (15-30 km from shore) sections. For each station, a pixel was randomly selected from inshore and midshelf sections from the gridded OSTIA SST data set along a transect (Fig. 7 main MS). Each transect extended from the coastline, across the shelf in a direction perpendicular to the shelf edge. Choosing inshore and midshelf pixel allowed us to compare trends across areas of varying shelf width where each WBC would influence upwelling differentially. Additionally, as a control, we selected an offshore pixel within the warm core of each current along the same transects for each station. No upwelling occurs within the offshore core of the currents and possible cold events would be due to temperature anomalies other than upwelling, hence no trends should be evident. We used the R package heatwaveR to detect cold events in each randomly selected pixel over time60. The algorithm implemented in this package is based on the hierarchical definition of extreme temperature events in21 and follows a similar methodology to characterising such events. In short, marine extreme temperature events are defined as discrete, anomalously warm or cold events with characteristics such as intensity, rate of onset, and spatial extent21. For extreme cold events, these characteristics are calculated in cases when temperatures fall below the 10th percentile threshold, which is based on a baseline climatology at least 30 years in duration to avoid impacts of decadal variability. Thus, data sets with long-term data availability, such as the OSTIA SST data set, available from 01/01/1981-present, are preferred21. Before calculating the baseline climatology at each pixel, we detrended the daily OSTIA sea surface temperature data as long-term trends in the climatology can affect detection of extreme temperature events using the detrend() function61. Within the detect_event() function of the heatwaveR package, we set the minimum duration for cold events to one day, in order to include short-term upwelling events which are common along both WBCs57. From the output of this function, we summed all discrete cold events each year from October to April, the time period tagged sharks were mainly present in both upwelling zones. We also calculated the annual mean intensity (in °C) and mean rate of onset (°C/day) for events from October to April. Visual inspection of the output suggested that trends in either variable remained similar using detrended and non-detrended data. We used generalised linear models to investigate trends in the number, mean intensity and mean rate of onset of upwelling events at each pixel between 1981 and 2022, with upwelling season (aggregated annually between October and April) as the independent variable. The number of upwelling events was modelled assuming a Poisson error distribution with a log-link function, while mean intensity and mean rate of onset were modelled assuming a Gaussian error distribution. Model fit and diagnostics were inspected using the Dharma package. When significant non-linearity and/or quantile deviations were detected, we fitted generalised additive models (GAMS) (Supplemental Tables 6 and 7), which were also inspected using Dharma and the gam.check() function in the mgcv package. Finally, as decadal variability and biases associated with L4-satellite SST products can confound trends in upwelling signals, we utilised temperature logger data from four locations within the upwelling cells of the Agulhas current (deployments from 1991 to 2022) and three different satellite SST products to validate our analyses above. Loggers were Star OddiTM temperature loggers with accuracy of ± 0.05°C located at Ystervarkpunt (78 km east of the Breede River), Mossel Bay (127 km east of the Breede River), Tsitsikamma (37 km east of Plettenberg Bay) and Mosterts Hoek (85 km west of Port Elizabeth) and serviced every 1-2 years (for deployment details see Supplemental Table 5). Satellite SST products used were the L4 OSTIA SST dataset from our original analyses (5 km resolution), the L4 CCI SST product (5 km resolution) and the L3 Pathfinder AVHRR night and day products combined into a single product (5 km resolution). Warming/cooling trends in SST products and logger data were estimated using the Theil-Sen estimator. The estimator is nonparametric and hence more robust than the parametric least-squares regression as it is resistant to outliers and does not draw from any probability distribution. Theil-Sen uses the median to estimate the slope as opposed to least-squares which makes use of a weighted mean for slope estimation. Spatial SST trends were calculated using each grid point within the designated area of the gridded SST product. Additionally, the SST and logger data trends were calculated using the anomalies to avoid the complicating effects of seasonality on trend values. Trend analyses for all logger locations and SST products ranged from June 1991 to August 2022. Animal tagging and tracking Tagged bull sharks were part of previously published data sets from southern Africa and eastern Australia and were acoustically tracked in coastal receiver arrays. For details about tagging methods and receiver arrays see27, 39, 62 , Supplemental Table 8). In short, in Australia, 25 bull sharks were internally tagged in Sydney Harbour with InnovaSeaTM V16TP tags that also recorded depth and temperature at the time of detection (tag nominal delay 30-90 s). Out of 41 bull sharks acoustically tagged in southern Africa as part of a separate study 27, 62, 15 individuals were also equipped with temperature and depth recording pop-up satellite archival transmitters (Wildlife ComputersTM Mk10-PSAT and miniPAT tags) and included again in this study (Supplemental Table 8). Five of these sharks were tagged inside the warm-temperate Breede River Estuary, and 10 at the subtropical Pinnacle Reef. PSAT-tags archived depth, temperature, and light level data at pre-programmed intervals of three seconds (n=1), five seconds (n=11) and 25 seconds (n=3). Additionally, binned data at 24-hour intervals was collected for temperature (9-11°C, 11-13°C, 13-15°C, 15-17°C, 17-19°C, 19-21°C, 21-23°C, 23-25°C, 25-27°C, 27-29°C, 29-31°C, 31-33°C) and depth (0-5m, 5-10m, 10-20m, 20-30m, 30-40m, 40-50m, 50-100m, 100-200m, 200-300m, 300-400m) for all PSAT-tags. Tags were released from animals after pre-programmed deployment durations (90-360 days). Tags then floated to the surface and transmitted data to the Argos satellites. Out of the 15 PSAT-tags deployed, 10 were physically recovered by the authors via boat, providing full archived temperature and depth data (one from a bull shark tagged at the Breede River and 9 from Pinnacle Reef). Transmitted data was initially decoded using the manufacturer’s software (WC-DAP 3.0, Wildlife Computers, Inc., Redmond, WA). The Wildlife ComputersTM GPE3 State-Space model was used to generate estimates of most likely locations from acoustic detections and PSAT-tag light level data using a swimming speed of 0.7 m/s64 and to create movement tracks for each shark (Supplemental Figs. 19-23). Then, in the statistical software R63 each shark location was matched with depth and temperature data collected by the PSAT-tags to presence in the two regions of interest: The upwelling zone (distinguishing locations inside the Breede River and outside), and the subtropical/tropical areas past the upwelling zone (Supplemental Figs. 19-23). For example, if an acoustic detection or geolocation estimate occurred in the upwelling zone (Fig. 5 main MS), the corresponding depth and temperature value at this timestamp was assigned to the upwelling zone. Note, this was only done for sharks tagged in the Breede River, as animals tagged at Pinnacle Reef remained in subtropical/tropical areas with limited upwelling. Similarly, for sharks acoustically tagged in Sydney Harbour, detections at receivers were assigned to either being in the upwelling zone, (distinguishing detections inside Sydney Harbour and outside), or past the upwelling zone (subtropical-tropical Queensland) (Fig. 5b main MS). Studies from multiple geographical locations have shown that bull shark abundance declines significantly when seasonal temperatures fall below a long-term thermal affinity threshold of 19°C38, 39, 40, 41. To further test this threshold and investigate thermal preferences for bull sharks, we established a tentative thermal range, using bull shark occurrence records in southern Africa and eastern Australia from the Global Biodiversty Information Facility (GBIF)65in combination with our acoustic detections. For this, we filtered combined bull shark occurrences to daily, verifed human and machine observations and spatio-temporally matched each occurrence with a pixel from the AQUA Modis L3-satellite temperature data set. Similar analysis using occurrence data was also performed for three megafauna species that have had fatalities in severe upwelling events, the whale shark Rhincodon typus manta rays, Mobula birostris and Mobula alfredi. For South African sharks for which PSATs were retrieved and full archival data was available (Supplemental Table 15), we plotted continuous temperature and depth over time, while indicating the occupied zone. Binned data from all Breede River bull sharks (full archival and partial data) was used to calculate percent of time at depth and temperatures to compare this in both upwelling and non-upwelling zones Pinnacle Reef sharks were detected only outside of upwelling cells). The maximum duration in consecutive hours, for which the Breede sharks were experiencing temperatures below 19°C for each zone was also calculated. Similarly, we binned temperature and depth data recorded from acoustic detections of bull sharks tagged in Sydney Harbour to determine percent of time at depth/temperature. Acoustic detections at depth/temperature were also plotted over time for sharks tagged in Sydney Harbour and indicated in which zone they occurred. Lastly, data from temperature loggers deployed outside the Breede estuary at 20 m and off Port Alfred at 30 m, as well as the loggers used for Theil-Sen estimation (see previous section) were used to spatio-temporally match temperature and depth recorded by PSAT-tags of the Breede River sharks with upwelling events at nearby loggers based on shark acoustic detections/PSAT-geolocations. This was plotted in combination with a detection timeline from combined acoustic and PSAT-tag locations to indicate when and where PSAT-tagged sharks encountered active upwelling cells. Temperature data for outside Sydney Harbour was recorded by a mooring located 14 km offshore, between 12-20 m depth, which was used to plot upwelling events with acoustic shark detections. Method References 56. Archer MR, Roughan M, Keating SR, Schaeffer A. On the variability of the East Australian Current: Jet structure, meandering, and influence on shelf circulation. Journal of Geophysical Research: Oceans 2017, 122(11): 8464-8481. 57. Abrahams A, Schlegel RW, Smit AJ. A novel approach to quantify metrics of upwelling intensity, frequency, and duration. PLoS One 2021, 16(7): e0254026. 58. Good S, Fiedler E, Mao C, Martin MJ, Maycock A, Reid R, et al. The current configuration of the OSTIA system for operational production of foundation sea surface temperature and ice concentration analyses. Remote Sensing 2020, 12(4): 720. 59. Yang C, Leonelli FE, Marullo S, Artale V, Beggs H, Nardelli BB, et al. Sea surface temperature intercomparison in the framework of the Copernicus Climate Change Service (C3S). Journal of Climate 2021, 34(13): 5257-5283. 60. Schlegel RW, Smit AJ. heatwaveR: A central algorithm for the detection of heatwaves and cold-spells. Journal of Open Source Software 2018, 3(27): 821. 61. Schlegel RW, Oliver EC, Hobday AJ, Smit AJ. Detecting marine heatwaves with sub-optimal data. Frontiers in Marine Science 2019, 6: 737. 62. Daly R, Smale MJ, Cowley PD, Froneman PW. Residency patterns and migration dynamics of adult bull sharks (Carcharhinus leucas) on the east coast of southern Africa. PloS one 2014, 9(10): e109357. 63. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ 64. Brunnschweiler JM. Sharksucker–shark interaction in two carcharhinid species. Marine Ecology 2006, 27(1): 89-94. 65. Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J., & Bates, A. E. (2015). Thermal biases and vulnerability to warming in the world’s marine fauna. Nature, 528(7580), 88-92. # Climate-change-driven cooling can kill marine megafauna at their distributional limits Bull Shark PSAT Data and example code for modeling of upwelling variables: ## Description of the Data and file structure These data include two different data sets Data set 1 contains 5 csv. files with raw depth and temperature data collected by PSAT-tags externally attached to bull sharks tagged in the Breede River in South Africa (n=5, 1 per shark). For this study, recorded depth (in m) and temperature (in °C) were the main variables used. For Shark ID1, ID2, ID4 and ID5 PSAT-tag series data was available. This includes the columns DeployID and PTT which are each sharks individual identifier number, DepthSensor which is the depth sensor resolution in m, a Source column which describes the source of the data, in the case of our PSAT-tags this is by Transmission, an Instrument column which describes the tag type, in our case MiniPAT, Day and Time columns provided in UTC. The Depth column provides Depth in m, while the DepthRange column provides the depth range experienced by each individual shark during each time bin (in our case 10 min interval). Similarly, the Temperature column provides temperature values in °C with the TemperatureRange column indicating the range of temperatures are shark experienced in a binned time interval of 10 mins. Activity and ARange columns were not used for this study. LocationQuality, Latitude and Longitude are not given in Series data and will populated with NA. Lastly, for SharkID3 full Archival Data was available, thus the data structure for this individual differs slightly. Here, the Time column provides a combined date and time value in UTC, with Depth and Temperature given in meters and °C respectively. Lightlevel data is used to determine geolocation for an animal at a given timestamp by measuring light intensity in W cm^-2. Columns Ax, Ay, Az, Dry, Aux, One Minute Light Level, Smoothed Light Level and Events are not applicable to this study and can be ignored. Combined tracks for each shark tagged in the Breede River are given in supplemental figures 18-22 to match with depth/temperature values. All rows containing NA means that tags were unable to measure and/or transmit to a satellite the given value at that time stamp. Data set 2 includes example code for how we modelled number of upwelling events, mean intensity of upwelling events and mean rate of onset of upwelling events over time (see methods). For this, we also included a Net_CDF file for an example location which includes a column for sea surface temperature and Date. All other data used in the MS is publicly available at: Both the OSTIA and the CCI satellite SST product were accessed at [https://marine.copernicus.eu/](https://marine.copernicus.eu/). The AVHRR Night satellite SST product was accessed at [https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstnmday_Lon0360.html](https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstnmday_Lon0360.html), while AVHRR Day satellite SST product was accessed at [https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstdmday_Lon0360.html](https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdPH53sstdmday_Lon0360.html). The L3 Aqua-Modis satellite SST product was accessed at [https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1sstd1dayR20190SQ.html](https://coastwatch.pfeg.noaa.gov/erddap/griddap/erdMH1sstd1dayR20190SQ.html). These data sets provide temperature in °C, a date and latitude and longitude in decimal degrees. NetCDF-files used to map the progression of the lethal upwelling event were downloaded from the OceanColor webserver ([https://oceancolor.gsfc.nasa.gov/l3/](https://oceancolor.gsfc.nasa.gov/l3/). Long-term wind data for South Africa can be requested from the South African Weather Service [https://www.weathersa.co.za/](https://www.weathersa.co.za/). Wind data for key dates in main manuscript is supplied in the supplemental material, including a column for wind speed in m/s and wind direction in degrees for a given date. Data associated with bull sharks tagged in Sydney Harbour (temperature in °C, depth in m and acoustic detections at a given receiver) is publicly available from the animal tracking facility at IMOS [https://animaltracking.aodn.org.au/](https://animaltracking.aodn.org.au/) (see supplemental material for tagging dates of sharks). Similarly, acoustic tracking data for bull sharks tagged in Southern Africa is available from the South African Acoustic Tracking Array Platform [https://www.saiab.ac.za/atap.htm](https://www.saiab.ac.za/atap.htm). Mooring temperature data for Sydney in °C is publicly available at the Australian Ocean Data Network on [https://portal.aodn.org.au/](https://portal.aodn.org.au/) (Mooring code: SYD100). In-situ temperature data collected by a logger at Port Alfred in in °C is supplied in the supplemental material. The impacts on marine species from secular warming and heatwaves are well demonstrated, however the impacts of extreme cold events are poorly understood. Here, we link the death of organisms from 81 species to an intense cold upwelling event in the Agulhas Current, and show trends of increasing frequency and intensification of upwelling in the Agulhas Current and East Australian Current. Using electronic tagging, we illustrate potential impacts of upwelling events on the movement behaviour of bull sharks, Carcharhinus leucas including alterations of migratory patterns and maintenance of shallower dive profiles when transiting through upwelling cells. Increasing upwelling could result in “bait-and-switch” situations, where climate change expands subtropical species’ distribution, while simultaneously exposing climate-migrants to increased risk of cold-stun/mortality events at poleward distributional limits. This shows the potential impacts of increased cold events, an understudied aspect of climate-change research, and highlights the complexities of climate change effects on marine ecosystems.

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