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Plain Text - 1.3 KB -
MD5: 3b8d0982b71637e6d37673727d3b2eb9
README explaining properties measured |
Tabular Data - 11.7 KB - 8 Variables, 338 Observations - UNF:6:tcayMkVXt5zR4FzbzvKNsg==
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Comma Separated Values - 4.6 KB -
MD5: 121975d4952e761e760b21f886ce3502
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Comma Separated Values - 7.5 KB -
MD5: 7b104a971676676bae855293540c523c
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Tabular Data - 7.4 KB - 8 Variables, 338 Observations - UNF:6:/b9dHzCsvpR7oQFtJXmlgg==
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Comma Separated Values - 7.7 KB -
MD5: 836b1181805b46f6122dab5e48d6d6c7
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Comma Separated Values - 7.2 KB -
MD5: 3942c22b2efeed7fd0ad4ba702e61eff
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Comma Separated Values - 29.1 KB -
MD5: d6dc48e47740fb6bc1988e001e25df84
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Jan 6, 2023 - Study around COVID-19 lockdown
Luo, Yizhou, 2023, "SuPreMeChiF – a New Approach to Detect Subtle Changes in Continuous Monitoring Data, with a case study of COVID-19 impact in Singapore through seismic and infrasound recordings", https://doi.org/10.21979/N9/1ZRELD, DR-NTU (Data), V1
This dataset contains infrasound recordings used for analysis for the paper "SuPreMeChiF – a New Approach to Detect Subtle Changes in Continuous Monitoring Data, with a case study of COVID-19 impact in Singapore through seismic and infrasound recordings" |
ZIP Archive - 948.7 MB -
MD5: 6ae55f1b05585df5d4f5171e0acd73d3
Singapore infrasound recordings (SG01) used for analysis for the paper "SuPreMeChiF – a New Approach to Detect Subtle Changes in Continuous Monitoring Data, with a case study of COVID-19 impact in Singapore through seismic and infrasound recordings" |
