Session Contribution Rest List
A contribution rest export view for sessions.
GET /events/data-science-symposium-7/programme/1/export/?format=api
[
{
"title": "Unlocking the potential of ML for Earth and Environment researchers",
"url": "/events/data-science-symposium-7/submissions/14",
"abstract": "<p dir=\"ltr\">\n This presentation reports on support done under the aegis of Helmholtz AI for a wide range of machine learning based solutions for research questions related to Earth and Environmental sciences. We will give insight into typical problem statements from Earth observation and Earth system modeling that are good candidates for experimentation with ML methods and report on our accumulated experience tackling such challenges with individual support projects. We address these projects in an agile, iterative manner and during the definition phase, we direct special attention towards assembling practically meaningful demonstrators within a couple of months. A recent focus of our work lies on tackling software engineering concerns for building ML-ESM hybrids.\n</p>\n<p>\n</p>\n<p dir=\"ltr\">\n Our implementation workflow covers stages from data exploration to model tuning. A project may often start with evaluating available data and deciding on basic feasibility, apparent limitations such as biases or a lack of labels, and splitting into training and test data. Setting up a data processing workflow to subselect and compile training data is often the next step, followed by setting up a model architecture. We have made good experience with automatic tooling to tune hyperparameters and test and optimize network architectures. In typical implementation projects, these stages may repeat many times to improve results and cover aspects such as errors due to confusing samples, incorporating domain model knowledge, testing alternative architectures and ML approaches, and dealing with memory limitations and performance optimization.\n</p>\n<p>\n</p>\n<p dir=\"ltr\">\n Over the past two years, we have supported Helmholtz-based researchers from many subdisciplines on making the best use of ML methods along with these steps. Example projects include wind speed regression on GNSS-R data, emulation of atmospheric chemistry modeling, Earth System model parameterizations with ML, marine litter detection, and rogue waves prediction. The presentation will highlight selected best practices across these projects. We are happy to share our experience as it may prove useful to applications in wider Earth System modeling. If you are interested in discussing your challenge with us, please feel free to chat with us.\n</p>",
"presentation_type": "Presentation",
"session": "Artificial Intelligence / Machine Learning in Earth System Sciences Part 2",
"start": "2022-06-28T09:45:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Frauke",
"last_name": "Albrecht",
"orcid": null
},
"affiliation": [
"Deutsches Klimarechenzentrum (DKRZ)"
],
"is_presenter": true
},
{
"author": {
"first_name": "Caroline",
"last_name": "Arnold",
"orcid": null
},
"affiliation": [
"Deutsches Klimarechenzentrum (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Danu",
"last_name": "Caus",
"orcid": null
},
"affiliation": [
"Deutsches Klimarechenzentrum (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Harsh",
"last_name": "Grover",
"orcid": null
},
"affiliation": [
"Deutsches Klimarechenzentrum (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Andrey",
"last_name": "Vlasenko",
"orcid": null
},
"affiliation": [
"Deutsches Klimarechenzentrum (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Tobias",
"last_name": "Weigel",
"orcid": null
},
"affiliation": [
"Deutsches Klimarechenzentrum (DKRZ)"
],
"is_presenter": false
}
],
"submitter": "Frauke Albrecht",
"event": "Data Science Symposium No. 7",
"activity": null,
"accepted": false,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"Deutsches Klimarechenzentrum (DKRZ)"
]
},
{
"title": "Making marine image data FAIR with iFDOs",
"url": "/events/data-science-symposium-7/submissions/45",
"abstract": "<p>\n <span>\n <span>\n Underwater images are used to explore and monitor ocean habitats, generating huge datasets with unusual data characteristics that preclude traditional data management strategies. Due to the lack of universally adopted data standards, image data collected from the marine environment are increasing in heterogeneity, preventing objective comparison. The extraction of actionable information thus remains challenging, particularly for researchers not directly involved with the image data collection. Standardized formats and procedures are needed to enable sustainable image analysis and processing tools, as are solutions for image publication in long-term repositories to ascertain reuse of data. The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a framework for such data management goals. We propose the use of image FAIR Digital Objects (iFDOs) and present an infrastructure environment to create and exploit such FAIR digital objects. We show how these iFDOs can be created, validated, managed, and stored, and which data associated with imagery should be curated. The goal is to reduce image management overheads while simultaneously creating visibility for image acquisition and publication efforts and to provide a standardised interface to image (meta) data for data science applications such as annotation, visualization, digital twins or machine learning.\n </span>\n </span>\n</p>",
"presentation_type": "Presentation",
"session": "Artificial Intelligence / Machine Learning in Earth System Sciences Part 2",
"start": "2022-06-28T10:00:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Timm",
"last_name": "Schoening",
"orcid": null
},
"affiliation": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
],
"is_presenter": true
}
],
"submitter": "Timm Schoening",
"event": "Data Science Symposium No. 7",
"activity": null,
"accepted": false,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
]
},
{
"title": "ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models",
"url": "/events/data-science-symposium-7/submissions/53",
"abstract": "<p>\n Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmospheric radiative transfer (RT) calculations are especially expensive. This has made them a popular target for neural network-based emulators. However, prior work is hard to compare due to the lack of a comprehensive dataset and standardized best practices for ML benchmarking.\n <br/>\n To fill this gap, we introduce the ClimART dataset, which is based on the Canadian Earth System Model, and comes with more than 10 million samples from present, pre-industrial, and future climate conditions.\n</p>\n<p>\n ClimART poses several methodological challenges for the ML community, such as multiple out-of-distribution test sets, underlying domain physics, and a trade-off between accuracy and inference speed. We also present several novel baselines that indicate shortcomings of the datasets and network architectures used in prior work.\n</p>",
"presentation_type": "Presentation",
"session": "Artificial Intelligence / Machine Learning in Earth System Sciences Part 2",
"start": "2022-06-28T10:15:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Salva",
"last_name": "Rühling Cachay",
"orcid": null
},
"affiliation": [
"Mila - Quebec AI Institute",
"TU Darmstadt"
],
"is_presenter": true
},
{
"author": {
"first_name": "Venkatesh",
"last_name": "Ramesh",
"orcid": null
},
"affiliation": [
"Mila - Quebec AI Institute"
],
"is_presenter": false
},
{
"author": {
"first_name": "Jason",
"last_name": "Cole",
"orcid": null
},
"affiliation": [
"Environment and Climate Change Canada"
],
"is_presenter": false
},
{
"author": {
"first_name": "Howard",
"last_name": "Barker",
"orcid": null
},
"affiliation": [
"Environment and Climate Change Canada"
],
"is_presenter": false
},
{
"author": {
"first_name": "David",
"last_name": "Rolnick",
"orcid": "0000-0002-2855-393X"
},
"affiliation": [
"Mila - Quebec AI Institute"
],
"is_presenter": false
}
],
"submitter": "Salva Rühling Cachay",
"event": "Data Science Symposium No. 7",
"activity": null,
"accepted": false,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"Mila - Quebec AI Institute",
"TU Darmstadt",
"Environment and Climate Change Canada"
]
},
{
"title": "Machine Learning applications for impact modelling of climate extremes",
"url": "/events/data-science-symposium-7/submissions/50",
"abstract": "<p>\n <span>\n <span>\n The impacts from anthropogenic climate change are directly felt through extremes. The existing research skills in assessing future changes in impacts from climate extremes is however still limited. Despite the fact that a multitude of climate simulations is now available that allows the analysis of such climatic events, available approaches have not yet sufficiently analysed the complex and dynamic aspects that are relevant to estimate what climate extremes mean for society in terms of impacts and damages. Machine Learning (ML) algorithms have the ability to model multivariate and nonlinear relationships, with possibilities for non-parametric regression and classification, and are therefore well-suited to model highly complex relations between climate extremes and their impacts.\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n In this presentation, I will highlight some recent ML applications, focussing on monetary damages from floods and windstorms. For these extremes, ML models are built using observational datasets of extremes and their impacts. Here I will also address the sample selection bias, which occurs between observed moderate impact events, and more extreme events sampled in current observed and projected future data. This can be addressed by adjusting weighting for such variable values, as is demonstrated for extreme windstorm events.\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n Another application focusses on health outcomes, in this case the occurrence of myocardial infarctions (MI). Several ML algorithms are tested to better predict MI events under changing environmental and demographic conditions, using data from the city of Augsburg (Germany) between 1998 and 2015. Multivariable predictors include weather (air temperature, relative humidity), air pollution (particulate matter, nitrogen oxide, nitrogen dioxide, sulphur dioxide, and ozone), surrounding vegetation, as well as demographic data.\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n Finally, I will suggest some further applications that could be developed for predicting climate impacts as well as impacts from policy planning and adaptation.\n </span>\n </span>\n</p>",
"presentation_type": "Presentation",
"session": "Artificial Intelligence / Machine Learning in Earth System Sciences Part 2",
"start": "2022-06-28T10:30:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Laurens Menno",
"last_name": "Bouwer",
"orcid": "0000-0003-3498-2586"
},
"affiliation": [
"Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany"
],
"is_presenter": true
}
],
"submitter": "Laurens Menno Bouwer",
"event": "Data Science Symposium No. 7",
"activity": null,
"accepted": false,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany"
]
}
]