cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/9hr6mj
title: A07_16644.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/187f7fd9-b463-1f95-84af-acafa0446944'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/187f7fd9-b463-1f95-84af-acafa0446944
DOI: 10.7910/dvn/qtzkeb
title: A07_16645.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/7c4633d8-b454-a6c0-14fc-970a7984c332'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/7c4633d8-b454-a6c0-14fc-970a7984c332
DOI: 10.7910/dvn/ef5xhg
title: A07_16646.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/e3b214e4-2f9a-5bc8-6d30-32674743b8ee'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/e3b214e4-2f9a-5bc8-6d30-32674743b8ee
DOI: 10.21227/pq3k-2782
title: GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors

description: Multimodal sensor fusion has been widely adopted in constructing scene understanding, perception, and planning for intelligent robotic systems. One of the critical tasks in this field is geospatial tracking, i.e., constantly detecting and locating objects moving across a scene. Successful development of multimodal sensor fusion tracking algorithms relies on large multimodal datasets where common modalities exist and are time-aligned, and such datasets are not readily available. Existing multimodal tracking datasets focus mainly on cameras and LiDARs in outdoor environments, while the rich set of indoor sensing modalities is largely ignored. Nevertheless, investigating this tracking problem indoors is non-trivial as it can benefit many applications such as intelligent building infrastructures. Some other datasets either employ a single centralized sensor node or a set of sensors whose positions and orientations are fixed. Models developed on such datasets have difficulties generalizing to different sensor placements. To fill these gaps, we propose GDTM, a nine-hour dataset for multi-modal object tracking with distributed multimodal sensors and reconfigurable sensor node placements. We demonstrate that our dataset enables the exploration of several research problems, including creating multimodal sensor fusion architectures robust to adverse sensing conditions and creating distributed object tracking systems robust to sensor placement variances. A GitHub repository containing the code, data, and checkpoints of this work is available at https://github.com/nesl/GDTM.
DOI: 10.7910/dvn/9nfhxm
title: A07_16647.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/73303353-6849-3fcd-8989-8cac1fc88626'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/73303353-6849-3fcd-8989-8cac1fc88626
DOI: 10.7910/dvn/axivbk
title: A07_16649.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/5d0416d2-b995-e711-875e-bc726afd4020'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/5d0416d2-b995-e711-875e-bc726afd4020
DOI: 10.6084/m9.figshare.27282837
title: The treatment of physiological jaundice by therapeutic massage in healthy term infants: a scoping review synoptic table

description: This is the Synoptic Table of a Scoping Review about the treatment of physiological jaundice by therapeutic massage in healthy term infants, performed for a bachelor thesis in nursing science.
DOI: 10.7910/dvn/vj0jqg
title: A07_16650.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/b4f55986-dbc0-fdf3-74e0-89aa74ab889f'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/b4f55986-dbc0-fdf3-74e0-89aa74ab889f
DOI: 10.7910/dvn/s18toy
title: A07_16653.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/8b06a040-bf54-da9c-5a8b-3400a5d56aa8'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/8b06a040-bf54-da9c-5a8b-3400a5d56aa8
DOI: 10.7910/dvn/vzkj0w
title: A07_16654.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/6cc9d232-e24c-ac4b-67f1-90675bc1856c'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/6cc9d232-e24c-ac4b-67f1-90675bc1856c
DOI: 10.7910/dvn/kaleic
title: A07_16655.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/f1908b0b-fec8-acc4-0869-f4c5499177ae'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/f1908b0b-fec8-acc4-0869-f4c5499177ae
DOI: 10.7910/dvn/nzkoxn
title: A07_16656.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/091c5c0d-46c3-3958-0764-837dd3ddc630'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/091c5c0d-46c3-3958-0764-837dd3ddc630
DOI: 10.7910/dvn/5lm5rk
title: A07_16657.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/dabf3e72-0274-d615-8cb9-7f96755ef810'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/dabf3e72-0274-d615-8cb9-7f96755ef810
DOI: 10.7910/dvn/pmeifh
title: A07_16659.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/259d5951-5eef-43e8-f862-ebf9d1dadd82'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/259d5951-5eef-43e8-f862-ebf9d1dadd82
DOI: 10.7910/dvn/fnrb1c
title: A07_16660.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/dc21dc00-97fd-cd52-153a-f6268e90f6c8'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/dc21dc00-97fd-cd52-153a-f6268e90f6c8
DOI: 10.7910/dvn/4xgqyx
title: A07_16661.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/d5b9903d-5300-56a3-31d3-b25202d5ca83'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/d5b9903d-5300-56a3-31d3-b25202d5ca83
DOI: 10.7910/dvn/q6ql29
title: A07_16664.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/9ff6e7d1-a34b-9574-1af9-dfa96800ccd4'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/9ff6e7d1-a34b-9574-1af9-dfa96800ccd4
DOI: 10.7910/dvn/5j7fxg
title: A07_16666.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/7125fb45-eaf7-904b-3339-8720e5298790'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/7125fb45-eaf7-904b-3339-8720e5298790
DOI: 10.7910/dvn/xr9voy
title: A07_16667.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/3414e6a1-2266-aa51-d150-db1c49637326'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/3414e6a1-2266-aa51-d150-db1c49637326
DOI: 10.7910/dvn/dk5kn7
title: A07_16668.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/da206b24-143c-38b7-ddcc-67fa91f493aa'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/da206b24-143c-38b7-ddcc-67fa91f493aa
DOI: 10.7910/dvn/aupirl
title: A07_16670.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/6ade242d-a25a-3938-2e39-ed2791f7a0b5'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/6ade242d-a25a-3938-2e39-ed2791f7a0b5
DOI: 10.7910/dvn/inxnho
title: A07_16671.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/d6693496-204b-68e9-6253-ea96431dd85f'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/d6693496-204b-68e9-6253-ea96431dd85f
DOI: 10.7910/dvn/nj4vtu
title: A07_16672.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/68c5755e-383a-e648-6ec0-5ac6d023d98d'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/68c5755e-383a-e648-6ec0-5ac6d023d98d
DOI: 10.7910/dvn/whqzgd
title: A07_16674.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/474ee20e-87a2-8cd6-154b-cf4a6f672c68'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/474ee20e-87a2-8cd6-154b-cf4a6f672c68
DOI: 10.15468/dl.whm9br
title: Occurrence Download

description: A dataset containing 5187792 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-123.0096 45.35765, -122.3604 45.35765, -122.3604 45.71841, -123.0096 45.71841, -123.0096 45.35765))",
 "Year is greater than or equal to 2013",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 5187792 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031880-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/s4nu6j
title: A07_16678.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/7f6a2fa2-d0d2-0f91-58f0-17ddcaae3701'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/7f6a2fa2-d0d2-0f91-58f0-17ddcaae3701
DOI: 10.7910/dvn/rc1nhv
title: A07_16682.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/d22182d7-9a05-27dd-68a0-645b346bd231'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/d22182d7-9a05-27dd-68a0-645b346bd231
DOI: 10.7910/dvn/cdscdq
title: A07_16683.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/ed9a4d6d-12a7-0a90-df75-1fb79f22fd30'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/ed9a4d6d-12a7-0a90-df75-1fb79f22fd30
DOI: 10.7910/dvn/vd2rce
title: A07_16684.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/ea63fcb8-e3d4-8b09-9fd6-1319e1886696'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/ea63fcb8-e3d4-8b09-9fd6-1319e1886696
DOI: 10.7910/dvn/dvqmbf
title: A07_16686.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/566587e0-35da-1e61-4ee4-87ad746aed43'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/566587e0-35da-1e61-4ee4-87ad746aed43
DOI: 10.7910/dvn/oj2dta
title: A07_16687.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/6d866a3a-33ec-0e09-5739-42eaeb4c8f52'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/6d866a3a-33ec-0e09-5739-42eaeb4c8f52
DOI: 10.7910/dvn/drhknp
title: A07_16688.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/009fe0e8-1388-7e47-9032-f9fcd6d4ca3c'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/009fe0e8-1388-7e47-9032-f9fcd6d4ca3c
DOI: 10.7910/dvn/we5vdo
title: A07_16689.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/c5b9225b-655f-9e09-3226-d05c5fc6e417'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/c5b9225b-655f-9e09-3226-d05c5fc6e417
DOI: 10.7910/dvn/ae3ddv
title: A07_16690.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/9179a4a0-f1f5-6636-04f6-3046ded5f33d'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/9179a4a0-f1f5-6636-04f6-3046ded5f33d
DOI: 10.7910/dvn/zzaxih
title: A07_16692.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/e24baa3f-aab9-9ec0-fb31-a98c17d7861a'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/e24baa3f-aab9-9ec0-fb31-a98c17d7861a
DOI: 10.7910/dvn/xjrz8a
title: A07_16696.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/91c71a09-fa10-1f8a-2bc5-0bfff2008143'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/91c71a09-fa10-1f8a-2bc5-0bfff2008143
DOI: 10.7910/dvn/vwf4ec
title: A07_16697.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/bbd2b174-554a-65bf-0f80-616f5d350e57'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/bbd2b174-554a-65bf-0f80-616f5d350e57
DOI: 10.7910/dvn/5hyxnz
title: A07_16699.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/7d96d1f4-184e-17f0-e17a-adea5ab2970d'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/7d96d1f4-184e-17f0-e17a-adea5ab2970d
DOI: 10.7910/dvn/9qryeq
title: A07_16700.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/67c73f81-f17f-5acc-f2d6-715006da61f5'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/67c73f81-f17f-5acc-f2d6-715006da61f5
DOI: 10.7910/dvn/bvoimt
title: A07_16702.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/ca3cb878-899b-2b39-ddeb-d3502a5fda75'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/ca3cb878-899b-2b39-ddeb-d3502a5fda75
DOI: 10.7910/dvn/e2hus5
title: A07_16704.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/4dde4263-fdb8-e6ac-eabe-1559381be6f5'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/4dde4263-fdb8-e6ac-eabe-1559381be6f5
DOI: 10.7910/dvn/b8mx03
title: A07_16705.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/6a958968-a50e-0012-6b40-d3ade8ee53a3'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/6a958968-a50e-0012-6b40-d3ade8ee53a3
DOI: 10.57745/5n7ftq
title: CuAg_b110_theta5_stress_fields.atom

description: :unav
DOI: 10.57745/nt7soo
title: AgNi_b110_theta10_stress_fields.atom

description: :unav
DOI: 10.57745/sfqtxp
title: CuAg_b110_theta5_segregation-energy.atom

description: :unav
DOI: 10.57745/ujupdn
title: CuAg_b100_theta5_segregation-energy.atom

description: :unav
DOI: 10.57745/ybugui
title: CuAg_b100_theta5_stress_fields.atom

description: :unav
DOI: 10.57745/c0w7lo
title: AgNi_b100_theta5_stress_fields.atom

description: :unav
DOI: 10.57745/cmikpx
title: CuAg_b110_theta10_segregation-energy.atom

description: :unav
DOI: 10.57745/gz9nja
title: AgNi_b100_theta5_segregation-energy.atom

description: :unav
DOI: 10.57745/kwjlh9
title: AgNi_b110_theta5_segregation-energy.atom

description: :unav
DOI: 10.57745/pudxof
title: AgNi_b110_theta5_stress_fields.atom

description: :unav
DOI: 10.57745/u3ezbr
title: AgNi_b110_theta10_segregation-energy.atom

description: :unav
DOI: 10.57745/dimqit
title: README.md

description: :unav
DOI: 10.57745/wp76fj
title: CuAg_b110_theta10_stress_fields.atom

description: :unav
DOI: 10.15468/dl.92j2fs
title: Occurrence Download

description: A dataset containing 773 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is MEX.25_1",
 "TaxonKey is Felidae"
 ]
}

 The dataset includes 773 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031884-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/cqzrik
title: A07_16709.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/830120a4-c570-9234-ba07-e9a4b7b0d943'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/830120a4-c570-9234-ba07-e9a4b7b0d943
DOI: 10.7910/dvn/uimudi
title: Estudos sobre a Matriz SWOT aplicados ao ecoturismo

description: Lista de referência de artigos utilizando a Matriz SWOT para fins de aplicação no planejamento e gestão em ecoturismo.
DOI: 10.15468/dl.g4vcws
title: Occurrence Download

description: A dataset containing 86440 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is MEX",
 "OccurrenceStatus is Present",
 "TaxonKey is Colubridae"
 ]
}

 The dataset includes 86440 records from 143 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031883-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/io2wwc
title: Replication Data for: COVID-19 and Asian Americans: How Elite Messaging and Social Exclusion Shape Partisan Attitudes (Perspectives on Politics)

description: Data and Replication Code
Codebook: https://www.voterstudygroup.org/publication/nationscape-data-set

Data used through May 7, 2020.
DOI: 10.7910/dvn/uk5bff
title: A08_16713.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/9a7ef409-4019-ca93-1d4f-ae074df6d409'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/9a7ef409-4019-ca93-1d4f-ae074df6d409
DOI: 10.7910/dvn/fgyuui
title: A08_16714.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/95ff8da9-f23f-aa96-2d25-5349eb924f1c'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/95ff8da9-f23f-aa96-2d25-5349eb924f1c
DOI: 10.21227/y57r-mb60
title: chicago traffic crash data 2020-2022

description: In the realm of traffic safety analysis, the inherent imbalance in crash datasets, particularly in terms of injury severity, poses a significant challenge for machine learning-based classification models. This study delves into the efficacy of Generative Adversarial Networks (GANs), with a specific focus on Conditional Tabular GAN (CTGAN), for synthesizing minority crash data to address this imbalance. Utilizing traffic crash data from Chicago spanning 2020 to 2022, the research evaluates the capabilities of CTGAN against three traditional data resampling methods, as well as an additional cost-sensitive learning approach. These methods are evaluated across various injury severity classification scenarios (2-class, 3-class, and 4-class) using five commonly applied injury severity classification models. The study's dual evaluation approach encompasses both the quality of synthetic data and the enhancement of classification model performance. The pivotal findings reveal that: 1) CTGAN markedly outperforms other data resampling techniques in generating superior quality synthetic data, particularly for the least represented injury severity category; 2) While CTGAN demonstrates substantial improvements over traditional data resampling methods in classification model performance, this advantage diminishes as the number of injury categories increases; 3) Surprisingly, CTGAN's superior data quality does not result in better classification performance compared to cost-sensitive learning, especially in more complex classification scenarios. Cost-sensitive learning combined with LightGBM achieves the best classification performance across all scenarios. Given the significantly lower computational resources required by cost-sensitive learning, this approach is recommended for handling imbalanced injury severity data.
DOI: 10.7910/dvn/eqlbe0
title: A08_16715.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/9a1dfeb9-0c0c-ca8f-aa34-c23c46cf105d'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/9a1dfeb9-0c0c-ca8f-aa34-c23c46cf105d
DOI: 10.15468/dl.ur562h
title: Occurrence Download

description: A dataset containing 4386 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-10000.0",
 "Geometry POLYGON((-125.07985 26.60335,-66.83277 26.60335,-66.83277 49.09625,-125.07985 49.09625,-125.07985 26.60335))",
 "TaxonKey is Bromus tectorum L."
 ]
}

 The dataset includes 4386 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031885-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/aaqlxx
title: A08_16718.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/871c0e4e-3110-5000-e077-f6ab153c12c4'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/871c0e4e-3110-5000-e077-f6ab153c12c4
DOI: 10.7910/dvn/mrncc0
title: A08_16719.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/8b3c9838-a900-818b-7431-7c0fa6c5415e'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/8b3c9838-a900-818b-7431-7c0fa6c5415e
DOI: 10.7910/dvn/nr5hz3
title: A08_16725.JPG

description: Link to OCHRE database: &lt;a href = 'http://pi.lib.uchicago.edu/1001/org/ochre/e2d6a2d4-0a97-45ba-1144-5caa3234b971'&gt;http://pi.lib.uchicago.edu/1001/org/ochre/e2d6a2d4-0a97-45ba-1144-5caa3234b971
DOI: 10.5281/zenodo.11846591
title: ESA StormSurgeCastNet Dataset

description: This is the dataset for the storm surge forecasting work of Ebel et al (2024), featuring the curation of a global and multi-decadal dataset of extreme weather induced storm surges as well as the implementation of neural networks for addressing the associated forecasting task. The provided dataset is multi-modal and features preprocessed in-situ tide gauge records as well as atmospheric reanalysis products and ocean state simulations. The models applied in this work learn to fuse the sparse yet accurate in-situ measurements with the global ocean and atmosphere state products. This way, more accurate storm surge forecasts are achieved, with predictions broadcasted to sites missing well-maintained tidal gauge infrastructure.


 



The publication is available in the proceedings https://openaccess.thecvf.com/content/CVPR2024W/EarthVision/html/Ebel_Implicit_Assimilation_of_Sparse_In_Situ_Data_for_Dense__CVPRW_2024_paper.html

For the associated code, please see https://github.com/PatrickESA/StormSurgeCastNet

For any further questions, please reach out to me here or via the credentials on my website.


Reference:

P. Ebel, B. Victor, P. Naylor, G. Meoni, F. Serva, R. Schneider Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2024.
DOI: 10.5281/zenodo.11846592
title: ESA StormSurgeCastNet Dataset

description: This is the dataset for the storm surge forecasting work of Ebel et al (2024), featuring the curation of a global and multi-decadal dataset of extreme weather induced storm surges as well as the implementation of neural networks for addressing the associated forecasting task. The provided dataset is multi-modal and features preprocessed in-situ tide gauge records as well as atmospheric reanalysis products and ocean state simulations. The models applied in this work learn to fuse the sparse yet accurate in-situ measurements with the global ocean and atmosphere state products. This way, more accurate storm surge forecasts are achieved, with predictions broadcasted to sites missing well-maintained tidal gauge infrastructure.


 



The publication is available in the proceedings https://openaccess.thecvf.com/content/CVPR2024W/EarthVision/html/Ebel_Implicit_Assimilation_of_Sparse_In_Situ_Data_for_Dense__CVPRW_2024_paper.html

For the associated code, please see https://github.com/PatrickESA/StormSurgeCastNet

For any further questions, please reach out to me here or via the credentials on my website.


Reference:

P. Ebel, B. Victor, P. Naylor, G. Meoni, F. Serva, R. Schneider Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2024.
DOI: 10.5281/zenodo.12067776
title: ESA StormSurgeCastNet Dataset

description: This is the dataset for the storm surge forecasting work of Ebel et al (2024), featuring the curation of a global and multi-decadal dataset of extreme weather induced storm surges as well as the implementation of neural networks for addressing the associated forecasting task. The provided dataset is multi-modal and features preprocessed in-situ tide gauge records as well as atmospheric reanalysis products and ocean state simulations. The models applied in this work learn to fuse the sparse yet accurate in-situ measurements with the global ocean and atmosphere state products. This way, more accurate storm surge forecasts are achieved, with predictions broadcasted to sites missing well-maintained tidal gauge infrastructure.


 



The publication is available in the proceedings https://openaccess.thecvf.com/content/CVPR2024W/EarthVision/html/Ebel_Implicit_Assimilation_of_Sparse_In_Situ_Data_for_Dense__CVPRW_2024_paper.html

For the associated code, please see https://github.com/PatrickESA/StormSurgeCastNet

For any further questions, please reach out to me here or via the credentials on my website.


Reference:

P. Ebel, B. Victor, P. Naylor, G. Meoni, F. Serva, R. Schneider Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2024.