cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/d5yq-6p54
title: Metro vehicle vibration energy harvesting dataset

description: We collect 200 hours of metro vehicle vibration energy harvesting data in total at intervals of 2 min.
DOI: 10.21227/pq6h-sf82
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that contained the exact location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 160+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-april29.* will contain tweet ID and sentiment data of the tweets that were tweeted between April 28, 2020, and April 29, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.5519/qd.w68fc7wa
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2113 records)
DOI: 10.7910/dvn/9argzn/06dgs4
title: Model_ic_3_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/1yuhd3
title: Model_oc_195_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/3fkght
title: Model_oc_27_V3 (v0=1e14).tab

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title: Model_oc_256_V3 (v0=1e14).tab

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title: Model_oc_68_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/5zwavr
title: Model_ic_15_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/7m12sl
title: Model_oc_27_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/7xvfpt
title: Model_ic_47_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/7z6mue
title: Model_oc_75_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/801pni
title: Model_oc_116_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/8bqztz
title: Model_ic_44_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/8sxqmh
title: Model_oc_262_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/9io7ih
title: Model_ic_36_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/bjaaki
title: Model_oc_61_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/f1k1x3
title: Model_ic_3_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/fkheho
title: Model_oc_240_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/fpnhf2
title: Model_oc_116_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/frqgvl
title: Model_ic_42_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/fs6ufb
title: Model_oc_184_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/fsdh2k
title: Model_ic_7_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/h7mypw
title: Model_oc_154_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/hj96rp
title: Model_oc_56_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/htqsvx
title: Model_ic_21_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/i2cut3
title: Model_oc_110_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/jzzh25
title: Model_oc_38_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/k2vr5i
title: Model_ic_4_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ltkyhc
title: Model_ic_47_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/m3wyaz
title: Model_oc_286_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/orwsq6
title: Model_oc_145_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/piollp
title: Model_oc_41_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/qcpfpe
title: Model_oc_154_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/qwdyim
title: Model_oc_266_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/qxclrk
title: Model_ic_26_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/tqdbgm
title: Model_oc_40_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/ufiw7x
title: Model_ic_20_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/wbp2k8
title: Model_ic_64_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/xitv8v
title: Model_ic_1_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/yqno1f
title: Model_oc_196_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/zt2fem
title: Model_oc_174_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/082wrr
title: Model_ic_1_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/12woiz
title: Model_oc_71_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/1oqmkg
title: Model_oc_181_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/2ep4c9
title: Model_oc_160_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/3gycxc
title: Model_ic_16_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/4dtkyc
title: Model_oc_94_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/68wsii
title: Model_oc_23_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/732h2m
title: Model_oc_2_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/74zfke
title: Model_ic_33_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/8x1z7i
title: Model_oc_140_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/azraia
title: Model_ic_62_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/bmhicx
title: Model_oc_18_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/c0jvm6
title: Model_oc_55_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/cukwyo
title: Model_oc_144_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/dnnjxw
title: Model_ic_56_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/dxhwrt
title: Model_ic_28_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/eawaap
title: Model_oc_21_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/ejvgjo
title: Model_oc_240_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/erbij7
title: Model_oc_46_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/heo6l9
title: Model_oc_2_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/hpsrhv
title: Model_ic_4_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/iwxt0s
title: Model_oc_84_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/jbouer
title: Model_ic_51_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/lhihky
title: Model_oc_294_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/lxqyxc
title: Model_ic_45_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/nbmlpv
title: Model_ic_30_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/o0z545
title: Model_ic_62_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/ol4qhq
title: Model_oc_237_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/po2cd9
title: Model_oc_188_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/py3ggy
title: Model_oc_271_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/qcdiyy
title: Model_oc_206_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/tkbcep
title: Model_oc_2_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/uditf7
title: Model_oc_298_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/umz75q
title: Model_oc_164_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/utwdiy
title: Model_ic_49_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/vklztp
title: Model_oc_174_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/wfx8vs
title: Model_oc_163_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ydhyqa
title: Model_oc_123_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ygmnxf
title: Model_ic_17_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/yuhrgf
title: Model_ic_56_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/yxilzn
title: Model_oc_164_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/1skbsc
title: Model_oc_265_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/1sxl5q
title: Model_oc_166_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/8wsnvp
title: Model_ic_10_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/c07fyx
title: Model_oc_75_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/d9ysab
title: Model_oc_208_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/dv1kry
title: Model_oc_93_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/dxsz9v
title: Model_ic_34_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/esoaf2
title: Model_oc_6_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/fgwwmc
title: Model_oc_55_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/gfbbaj
title: Model_oc_217_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ghjfis
title: Model_oc_59_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/gsgm9y
title: Model_oc_88_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/hl5hey
title: Model_oc_46_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/hzfgzh
title: Model_ic_56_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/i6o8wh
title: Model_oc_193_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/j1fud7
title: Model_ic_23_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/j7f0tr
title: Model_oc_291_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/jawzuw
title: Model_oc_106_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/jndkon
title: Model_oc_86_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/kwnluo
title: Model_ic_8_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/llncqy
title: Model_oc_277_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/llnjiq
title: Model_ic_52_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/mj4xxp
title: Model_oc_77_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/mz1d5z
title: Model_oc_27_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/nbeyf5
title: Model_oc_95_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/oldjro
title: Model_ic_19_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/oolwtz
title: Model_oc_61_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/pgknyo
title: Model_oc_242_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ph3ul9
title: Model_oc_262_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/pim2c3
title: Model_oc_101_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/pxw6p3
title: Model_ic_59_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/q7vet0
title: Model_oc_273_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/qf2m4k
title: Model_oc_287_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/rl582n
title: Model_oc_37_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/splajb
title: Model_oc_272_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/tibm7b
title: Model_ic_10_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/tx9gch
title: Model_oc_15_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/uvhkgt
title: Model_oc_26_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/uw7dgh
title: Model_oc_43_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/vnptcf
title: Model_oc_119_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/w9uvu5
title: Model_oc_30_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/zojhdu
title: Model_oc_9_V2 (v0=1e14).tab

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DOI: 10.21227/7scn-f734
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 160+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? It is Twitter's content redistribution policy that restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers to always pull fresh data using their API. Why? Here's my opinion. Maybe, some user might want to delete a particular tweet after a couple of minute(s)/hour(s)/day(s), and if the same tweet has already been pulled and is shared on a public domain, then it might make the user/community vulnerable to many inferences coming out of the shared data.
DOI: 10.5281/zenodo.3883416
title: Earthquake Data around the Palu-Koro fault

description: This earthquake data is used in my paper about stress on the Palu-Koro fault.
DOI: 10.7910/dvn/9argzn/4oy7ly
title: Model_oc_10_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/59qitn
title: Model_oc_198_M&A (v0=1e13).tab

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title: Model_oc_243_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ajuazl
title: Model_oc_121_V1 (v0=1e14).tab

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title: Model_oc_112_M&A (v0=1e12).tab

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title: Model_oc_34_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/cmaink
title: Model_oc_183_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/dzzwyy
title: Model_oc_84_V2 (v0=1e14).tab

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title: Model_oc_69_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/fhgy2q
title: Model_oc_20_M&A (v0=1e13).tab

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title: Model_oc_156_V2 (v0=1e14).tab

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title: Model_oc_11_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/jdgjee
title: Model_ic_32_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/jzlgxn
title: Model_oc_7_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/k0b5im
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DOI: 10.3886/e112378v1-24807
title: Replication data for: Sudden Stops, Financial Crises, and Leverage

description: Financial crashes were followed by deep recessions in the Sudden Stops of emerging economies. An equilibrium business cycle model with a collateral constraint explains this phenomenon as a result of the amplification and asymmetry that the constraint induces in the responses of macro-aggregates to shocks. Leverage rises during expansions, and when it rises enough it triggers the constraint, causing a Fisherian deflation that reduces credit and the price and quantity of collateral assets. Output and factor allocations fall because access to working capital financing is also reduced. Precautionary saving makes Sudden Stops low probability events nested within normal cycles, as observed in the data. (JEL E21, E23, E32, E44, G01, O11, O16)
DOI: 10.5281/zenodo.3813574
title: Data supplement for "Thin-Film Modeling of Resting and Moving Active Droplets"

description: This dataset contains the data and source files for figures 4-10, 12, and 14-20 in the following publication: S. Trinschek, F. Stegemerten, K. John and U. Thiele <em>"Thin-Film Modeling of Resting and Moving Active Droplets"</em> published in 2020 in Physical Review E. Please follow the instructions given in 'Readme.txt'.