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Change","sub_category":"Climate Downscaling","monthly_downloads":201,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"[//]: # (![]\u0026#40;figs/DeepSensorLogo.png\u0026#41;)\n\u003cul style=\"text-align: center;\"\u003e\n\u003cimg src=\"https://raw.githubusercontent.com/alan-turing-institute/deepsensor/main/figs/DeepSensorLogo2.png\" width=\"700\"/\u003e\n\u003c/ul\u003e\n\n\u003cul style=\"margin-top:0px;\"\u003e\n\n\n\u003cp style=\"text-align: center; font-size: 15px\"\u003eA Python package and open-source project for modelling environmental\ndata with neural processes\u003c/p\u003e\n\n-----------\n\n[![release](https://img.shields.io/badge/release-v0.4.2-green?logo=github)](https://github.com/alan-turing-institute/deepsensor/releases)\n[![Latest Docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://alan-turing-institute.github.io/deepsensor/)\n![Tests](https://github.com/alan-turing-institute/deepsensor/actions/workflows/tests.yml/badge.svg)\n[![Coverage Status](https://coveralls.io/repos/github/alan-turing-institute/deepsensor/badge.svg?branch=main)](https://coveralls.io/github/alan-turing-institute/deepsensor?branch=main)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![slack](https://img.shields.io/badge/slack-deepsensor-purple.svg?logo=slack)](https://ai4environment.slack.com/archives/C05NQ76L87R)\n[![All Contributors](https://img.shields.io/github/all-contributors/alan-turing-institute/deepsensor?color=ee8449\u0026style=flat-square)](#contributors)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/alan-turing-institute/deepsensor/blob/main/LICENSE)\n\nDeepSensor streamlines the application of neural processes (NPs) to environmental sciences by\nproviding a simple interface for building, training, and evaluating NPs using `xarray` and `pandas`\ndata. Our developers and users form an open-source community whose vision is to accelerate the next\ngeneration of environmental ML research. The DeepSensor Python package facilitates this by\ndrastically reducing the time and effort required to apply NPs to environmental prediction tasks.\nThis allows DeepSensor users to focus on the science and rapidly iterate on ideas.\n\nDeepSensor is an experimental package, and we\nwelcome [contributions from the community](https://github.com/alan-turing-institute/deepsensor/blob/main/CONTRIBUTING.md).\nWe have an active Slack channel for code and research discussions; you can join by [signing up for the Turing Environment \u0026 Sustainability stakeholder community](https://forms.office.com/pages/responsepage.aspx?id=p_SVQ1XklU-Knx-672OE-ZmEJNLHTHVFkqQ97AaCfn9UMTZKT1IwTVhJRE82UjUzMVE2MThSOU5RMC4u). The form includes a question on signing up for the Slack team, where you can find DeepSensor's channel.\n\n![DeepSensor example application figures](https://raw.githubusercontent.com/alan-turing-institute/deepsensor/main/figs/deepsensor_application_examples.png)\n\nWhy neural processes?\n-----------\nNPs are a highly flexible class of probabilistic models that offer unique opportunities to model\nsatellite observations, climate model output, and in-situ measurements.\nTheir key features are the ability to:\n\n- ingest multiple data streams of pointwise or gridded modalities\n- handle missing data and varying resolutions\n- predict at arbitrary target locations\n- quantify prediction uncertainty\n\nThese capabilities make NPs well suited to a range of\nspatio-temporal data fusion tasks such as downscaling, sensor placement, gap-filling, and forecasting.\n\nWhy DeepSensor?\n-----------\nThis package aims to faithfully match the flexibility of NPs with a simple and intuitive interface.\nUnder the hood, DeepSensor wraps around the\npowerful [neuralprocessess](https://github.com/wesselb/neuralprocesses) package for core modelling\nfunctionality, while allowing users to stay in the familiar [xarray](https://xarray.pydata.org)\nand [pandas](https://pandas.pydata.org) world from end-to-end.\nDeepSensor also provides convenient plotting tools and active learning functionality for finding\noptimal [sensor placements](https://doi.org/10.1017/eds.2023.22).\n\nDocumentation\n-----------\nWe have an extensive documentation page [here](https://alan-turing-institute.github.io/deepsensor/),\ncontaining steps for getting started, a user guide built from reproducible Jupyter notebooks,\nlearning resources, research ideas, community information, an API reference, and more!\n\nDeepSensor Gallery\n-----------\nFor real-world DeepSensor research demonstrators, check out the\n[DeepSensor Gallery](https://github.com/tom-andersson/deepsensor_gallery).\nConsider submitting a notebook showcasing your research!\n\nDeep learning library agnosticism\n-----------\nDeepSensor leverages the [backends](https://github.com/wesselb/lab) package to be compatible with\neither [PyTorch](https://pytorch.org/) or [TensorFlow](https://www.tensorflow.org/).\nSimply `import deepsensor.torch` or `import deepsensor.tensorflow` to choose between them!\n\nQuick start\n----------\n\nHere we will demonstrate a simple example of training a convolutional conditional neural process\n(ConvCNP) to spatially interpolate random grid cells of NCEP reanalysis air temperature data\nover the US. First, pip install the package. In this case we will use the PyTorch backend\n(note: follow the [PyTorch installation instructions](https://pytorch.org/) if you\nwant GPU support).\n\n```bash\npip install deepsensor[torch]\n```\n\nWe can go from imports to predictions with a trained model in less than 30 lines of code!\n\n```python\nimport deepsensor.torch\nfrom deepsensor.data import DataProcessor, TaskLoader\nfrom deepsensor.model import ConvNP\nfrom deepsensor.train import Trainer\n\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\n\n# Load raw data\nds_raw = xr.tutorial.open_dataset(\"air_temperature\")\n\n# Normalise data\ndata_processor = DataProcessor(x1_name=\"lat\", x2_name=\"lon\")\nds = data_processor(ds_raw)\n\n# Set up task loader\ntask_loader = TaskLoader(context=ds, target=ds)\n\n# Set up ConvNP, which by default instantiates a ConvCNP with Gaussian marginals\nmodel = ConvNP(data_processor, task_loader)\n\n# Generate training tasks with up 100 grid cells as context and all grid cells\n#   as targets\ntrain_tasks = []\nfor date in pd.date_range(\"2013-01-01\", \"2014-11-30\")[::7]:\n    N_context = np.random.randint(0, 100)\n    task = task_loader(date, context_sampling=N_context, target_sampling=\"all\")\n    train_tasks.append(task)\n\n# Train model\ntrainer = Trainer(model, lr=5e-5)\nfor epoch in tqdm(range(10)):\n    batch_losses = trainer(train_tasks)\n\n# Predict on new task with 50 context points and a dense grid of target points\ntest_task = task_loader(\"2014-12-31\", context_sampling=50)\npred = model.predict(test_task, X_t=ds_raw)\n```\n\nAfter training, the model can predict directly to `xarray` in your data's original units and\ncoordinate system:\n\n```python\n\u003e\u003e\u003e pred[\"air\"]\n\u003cxarray.Dataset\u003e\nDimensions:  (time: 1, lat: 25, lon: 53)\nCoordinates:\n  * time     (time) datetime64[ns] 2014-12-31\n  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\nData variables:\n    mean     (time, lat, lon) float32 267.7 267.2 266.4 ... 297.5 297.8 297.9\n    std      (time, lat, lon) float32 9.855 9.845 9.848 ... 1.356 1.36 1.487\n```\n\nWe can also predict directly to `pandas` containing a timeseries of predictions at off-grid\nlocations\nby passing a `numpy` array of target locations to the `X_t` argument of `.predict`:\n\n```python\n# Predict at two off-grid locations over December 2014 with 50 random, fixed context points\ntest_tasks = task_loader(pd.date_range(\"2014-12-01\", \"2014-12-31\"), 50, seed_override=42)\npred = model.predict(test_tasks, X_t=np.array([[50, 280], [40, 250]]).T)\n```\n\n```python\n\u003e\u003e\u003e pred[\"air\"]\n                          mean       std\ntime       lat lon                      \n2014-12-01 50  280  260.282562  5.743976\n           40  250  270.770111  4.271546\n2014-12-02 50  280  255.572098  6.165956\n           40  250  277.588745  3.727404\n2014-12-03 50  280  260.894196   6.02924\n...                        ...       ...\n2014-12-29 40  250  266.594421  4.268469\n2014-12-30 50  280  250.936386  7.048379\n           40  250  262.225464  4.662592\n2014-12-31 50  280  249.397919  7.167142\n           40  250  257.955505  4.697775\n\n[62 rows x 2 columns]\n```\n\nDeepSensor offers far more functionality than this simple example demonstrates.\nFor more information on the package's capabilities, check out the\n[User Guide](https://alan-turing-institute.github.io/deepsensor/user-guide/index.html)\nin the documentation.\n\n## Citing DeepSensor\n\nIf you use DeepSensor in your research, please consider citing this repository.\nYou can generate a BiBTeX entry by clicking the 'Cite this repository' button\non the top right of this page.\n\n## Funding\n\nDeepSensor has received funding from [The Alan Turing Institute](https://www.turing.ac.uk/) under the [Environmental monitoring: blending satellite and surface data](https://www.turing.ac.uk/research/research-projects/environmental-monitoring-blending-satellite-and-surface-data) project, and the ARIA [Forecasting Tipping Points programme](https://www.aria.org.uk/opportunity-spaces/scoping-our-planet/forecasting-tipping-points/) specifically as part of the [GIANT](https://www.bas.ac.uk/project/giant/) project. The PI for DeepSensor development is [Dr Scott Hosking](https://www.turing.ac.uk/people/researchers/scott-hosking).\n\n## Contributors\n\nWe appreciate all contributions to DeepSensor, big or small, code-related or not, and we thank all\ncontributors below for supporting open-source software and research.\nFor code-specific contributions, check out our graph of [code contributions](https://github.com/alan-turing-institute/deepsensor/graphs/contributors).\nSee our [contribution guidelines](https://github.com/alan-turing-institute/deepsensor/blob/main/CONTRIBUTING.md)\nif you would like to join this list!\n\n\u003c!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --\u003e\n\u003c!-- prettier-ignore-start --\u003e\n\u003c!-- markdownlint-disable --\u003e\n\u003ctable\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/acocac\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/13321552?v=4?s=100\" width=\"100px;\" alt=\"Alejandro ©\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eAlejandro ©\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#userTesting-acocac\" title=\"User Testing\"\u003e📓\u003c/a\u003e \u003ca href=\"#bug-acocac\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e \u003ca href=\"#mentoring-acocac\" title=\"Mentoring\"\u003e🧑‍🏫\u003c/a\u003e \u003ca href=\"#ideas-acocac\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#research-acocac\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"#code-acocac\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"#test-acocac\" title=\"Tests\"\u003e⚠️\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/annavaughan\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/45528489?v=4?s=100\" width=\"100px;\" alt=\"Anna Vaughan\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eAnna Vaughan\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#research-annavaughan\" title=\"Research\"\u003e🔬\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/DaniJonesOcean\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/11757453?v=4?s=100\" width=\"100px;\" alt=\"Dani Jones\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eDani Jones\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#bug-DaniJonesOcean\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"http://davidwyld.uk\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/24752124?v=4?s=100\" width=\"100px;\" alt=\"David Wyld\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eDavid Wyld\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#doc-davidwyld\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#test-davidwyld\" title=\"Tests\"\u003e⚠️\u003c/a\u003e \u003ca href=\"#maintenance-davidwyld\" title=\"Maintenance\"\u003e🚧\u003c/a\u003e \u003ca href=\"#bug-davidwyld\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"http://inconsistentrecords.co.uk\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/731727?v=4?s=100\" width=\"100px;\" alt=\"Jim Circadian\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJim Circadian\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-JimCircadian\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#projectManagement-JimCircadian\" title=\"Project Management\"\u003e📆\u003c/a\u003e \u003ca href=\"#maintenance-JimCircadian\" title=\"Maintenance\"\u003e🚧\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/jonas-scholz123\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/37850411?v=4?s=100\" width=\"100px;\" alt=\"Jonas Scholz\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eJonas Scholz\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#userTesting-jonas-scholz123\" title=\"User Testing\"\u003e📓\u003c/a\u003e \u003ca href=\"#research-jonas-scholz123\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"#code-jonas-scholz123\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"#bug-jonas-scholz123\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e \u003ca href=\"#ideas-jonas-scholz123\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"http://www.westerling.nu\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/7298727?v=4?s=100\" width=\"100px;\" alt=\"Kalle Westerling\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eKalle Westerling\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#doc-kallewesterling\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#infra-kallewesterling\" title=\"Infrastructure (Hosting, Build-Tools, etc)\"\u003e🚇\u003c/a\u003e \u003ca href=\"#ideas-kallewesterling\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#projectManagement-kallewesterling\" title=\"Project Management\"\u003e📆\u003c/a\u003e \u003ca href=\"#promotion-kallewesterling\" title=\"Promotion\"\u003e📣\u003c/a\u003e \u003ca href=\"#question-kallewesterling\" title=\"Answering Questions\"\u003e💬\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"http://kenzaxtazi.github.io\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/43008274?v=4?s=100\" width=\"100px;\" alt=\"Kenza Tazi\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eKenza Tazi\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-kenzaxtazi\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"http://magnusross.github.io/about\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/51709759?v=4?s=100\" width=\"100px;\" alt=\"Magnus Ross\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eMagnus Ross\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#tutorial-magnusross\" title=\"Tutorials\"\u003e✅\u003c/a\u003e \u003ca href=\"#data-magnusross\" title=\"Data\"\u003e🔣\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://nilsleh.info/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/35272119?v=4?s=100\" width=\"100px;\" alt=\"Nils Lehmann\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eNils Lehmann\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-nilsleh\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#userTesting-nilsleh\" title=\"User Testing\"\u003e📓\u003c/a\u003e \u003ca href=\"#bug-nilsleh\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/polpel\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/56694450?v=4?s=100\" width=\"100px;\" alt=\"Paolo Pelucchi\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003ePaolo Pelucchi\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#userTesting-polpel\" title=\"User Testing\"\u003e📓\u003c/a\u003e \u003ca href=\"#bug-polpel\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://rohitrathore.netlify.app/\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/42641738?v=4?s=100\" width=\"100px;\" alt=\"Rohit Singh Rathaur\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eRohit Singh Rathaur\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#code-RohitRathore1\" title=\"Code\"\u003e💻\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://scotthosking.com\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/10783052?v=4?s=100\" width=\"100px;\" alt=\"Scott Hosking\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eScott Hosking\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#fundingFinding-scotthosking\" title=\"Funding Finding\"\u003e🔍\u003c/a\u003e \u003ca href=\"#ideas-scotthosking\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e \u003ca href=\"#projectManagement-scotthosking\" title=\"Project Management\"\u003e📆\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://www.bas.ac.uk/profile/tomand\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/26459412?v=4?s=100\" width=\"100px;\" alt=\"Tom Andersson\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eTom Andersson\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#code-tom-andersson\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"#research-tom-andersson\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"#maintenance-tom-andersson\" title=\"Maintenance\"\u003e🚧\u003c/a\u003e \u003ca href=\"#bug-tom-andersson\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e \u003ca href=\"#test-tom-andersson\" title=\"Tests\"\u003e⚠️\u003c/a\u003e \u003ca href=\"#tutorial-tom-andersson\" title=\"Tutorials\"\u003e✅\u003c/a\u003e \u003ca href=\"#doc-tom-andersson\" title=\"Documentation\"\u003e📖\u003c/a\u003e \u003ca href=\"#review-tom-andersson\" title=\"Reviewed Pull Requests\"\u003e👀\u003c/a\u003e \u003ca href=\"#talk-tom-andersson\" title=\"Talks\"\u003e📢\u003c/a\u003e \u003ca href=\"#question-tom-andersson\" title=\"Answering Questions\"\u003e💬\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"http://wessel.ai\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/1444448?v=4?s=100\" width=\"100px;\" alt=\"Wessel\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eWessel\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#research-wesselb\" title=\"Research\"\u003e🔬\u003c/a\u003e \u003ca href=\"#code-wesselb\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"#ideas-wesselb\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"http://patel-zeel.github.io\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/59758528?v=4?s=100\" width=\"100px;\" alt=\"Zeel B Patel\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eZeel B Patel\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#bug-patel-zeel\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e \u003ca href=\"#code-patel-zeel\" title=\"Code\"\u003e💻\u003c/a\u003e \u003ca href=\"#userTesting-patel-zeel\" title=\"User Testing\"\u003e📓\u003c/a\u003e \u003ca href=\"#ideas-patel-zeel\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/holzwolf\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/135216528?v=4?s=100\" width=\"100px;\" alt=\"holzwolf\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eholzwolf\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#bug-holzwolf\" title=\"Bug reports\"\u003e🐛\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/ots22\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/5434836?v=4?s=100\" width=\"100px;\" alt=\"ots22\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003eots22\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#ideas-ots22\" title=\"Ideas, Planning, \u0026 Feedback\"\u003e🤔\u003c/a\u003e\u003c/td\u003e\n      \u003ctd align=\"center\" valign=\"top\" width=\"14.28%\"\u003e\u003ca href=\"https://github.com/vinayakrana\"\u003e\u003cimg src=\"https://avatars.githubusercontent.com/u/95575600?v=4?s=100\" width=\"100px;\" alt=\"vinayakrana\"/\u003e\u003cbr /\u003e\u003csub\u003e\u003cb\u003evinayakrana\u003c/b\u003e\u003c/sub\u003e\u003c/a\u003e\u003cbr /\u003e\u003ca href=\"#doc-vinayakrana\" title=\"Documentation\"\u003e📖\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003c!-- markdownlint-restore --\u003e\n\u003c!-- prettier-ignore-end --\u003e\n\n\u003c!-- ALL-CONTRIBUTORS-LIST:END --\u003e\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1017/eds.2023.22"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["hut23"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/183659","html_url":"https://ost.ecosyste.ms/projects/183659"}