cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3885347
title: Annual and seasonal carbon gas dynamics in the land–aquatic–atmosphere continuum of a boreal landscape

description: Dataset from the submitted manuscript Miettinen H, Pumpanen J, Rantakari M, Heiskanen J and Ojala A: Annual and seasonal carbon gas dynamics in the land–aquatic–atmosphere continuum of a boreal landscape.
DOI: 10.7910/dvn/9argzn/0zby24
title: Model_oc_1_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/3crelb
title: Model_oc_94_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/3zbdob
title: Model_oc_63_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/79vzv3
title: Model_oc_40_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ba5nvf
title: Model_ic_7_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ce9uz0
title: Model_oc_13_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/cpyc5v
title: Model_oc_27_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/dr2tvx
title: Model_oc_231_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/drjma2
title: Model_oc_100_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/egikuk
title: Model_oc_98_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/elmrsd
title: Model_ic_50_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/elpkf4
title: Model_oc_135_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/emyrce
title: Model_oc_107_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/etgn5n
title: Model_ic_14_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/frpaxn
title: Model_ic_51_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/gfyp0t
title: Model_ic_45_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/hidmpf
title: Model_oc_77_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ksekh4
title: Model_oc_40_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/lr4hph
title: Model_oc_277_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/msd35y
title: Model_oc_143_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/mtb1nv
title: Model_oc_199_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/nzemxn
title: Model_oc_17_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/o7zz65
title: Model_ic_31_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/pixiwj
title: Model_oc_75_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ptrvck
title: Model_ic_24_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/t6pwuy
title: Model_oc_14_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ua4mtb
title: Model_oc_6_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/uqetty
title: Model_oc_72_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vak2nd
title: Model_oc_98_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vrpyzp
title: Model_ic_5_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/w4fssz
title: Model_oc_83_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/xpv3ps
title: Model_oc_88_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/xv3ykg
title: Model_oc_45_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/yceha6
title: Model_oc_19_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/yjnbgf
title: Model_oc_210_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/zpnfzx
title: Model_oc_52_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/zs3ap9
title: Model_oc_224_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/zwc5yg
title: Model_oc_25_V1 (v0=1e14).tab

description: :unav
DOI: 10.15468/j8zuk2
title: Gastrotermes spinatus gen. n. sp. n., an African soil-feeding termite described from the worker caste (Isoptera, Termitidae, Apicotermitidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Scheffrahn, Rudolf H. (2020): Gastrotermes spinatus gen. n. sp. n., an African soil-feeding termite described from the worker caste (Isoptera, Termitidae, Apicotermitidae). Zootaxa 4789 (1): 291-296, DOI: https://doi.org/10.11646/zootaxa.4789.1.12
DOI: 10.21227/h424-b957
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: 10.7910/dvn/9argzn/18jlix
title: Model_oc_118_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/1fks2d
title: Model_oc_82_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/2nwtyr
title: Model_oc_89_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/7fiaws
title: Model_oc_269_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/7glinj
title: Model_oc_65_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/8czyio
title: Model_oc_88_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/accik4
title: Model_oc_225_M&A (v0=1e13).tab

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

description: :unav
DOI: 10.7910/dvn/9argzn/ci85ih
title: Model_oc_44_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/dsxj41
title: Model_oc_63_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/iw8pnb
title: Model_oc_47_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/jfssum
title: Model_oc_243_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/keppik
title: Model_ic_30_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/kofwnk
title: Model_ic_43_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/l3jt4y
title: Model_oc_23_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/llf9vw
title: Model_oc_36_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/msbqr4
title: Model_oc_11_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/odhtlm
title: Model_oc_22_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ppd42o
title: Model_oc_213_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/pqefds
title: Model_oc_45_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/pqelge
title: Model_oc_25_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/qmaagn
title: Model_oc_41_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/qsdpmr
title: Model_oc_268_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/rpe2pz
title: Model_ic_58_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/t4x6u2
title: Model_ic_47_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/thw4ve
title: Model_oc_32_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/u3bcpa
title: Model_oc_119_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vogtl6
title: Model_oc_212_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vwm3r5
title: Model_oc_1_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/x5ej1t
title: Model_oc_38_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/xdr58t
title: Model_oc_72_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/zf0ik5
title: Model_oc_56_V2 (v0=1e14).tab

description: :unav
DOI: 10.5281/zenodo.3885370
title: Some Notable Records of Mayflies (Insecta: Ephemeroptera) from Big Rivers in Indiana: Supporting Data

description: Significant records from July & August 2019 fieldwork on big rivers in Indiana, United States.
DOI: 10.7910/dvn/9argzn/0rhgxo
title: Model_ic_54_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/1hfylz
title: Model_oc_241_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/1sa7ow
title: Model_oc_56_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/4eesxn
title: Model_oc_51_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/4yvmsu
title: Model_oc_16_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/9pqxzx
title: Model_oc_12_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/afv3he
title: Model_oc_72_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/cpdboc
title: Model_oc_8_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/faqsy8
title: Model_oc_60_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/fv3v9u
title: Model_oc_76_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/g7joeo
title: Model_ic_64_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/gdy6ke
title: Model_oc_5_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ghdwzk
title: Model_oc_176_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/gzabbd
title: Model_ic_30_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/inizgv
title: Model_ic_35_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/kd9apg
title: Model_ic_7_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/lu6dox
title: Model_oc_3_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/muanin
title: Model_oc_214_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/nkvnzs
title: Model_oc_1_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/nxl86t
title: Model_oc_254_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/pt4pbj
title: Model_ic_42_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/puuqgo
title: Model_ic_58_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/qxomfz
title: Model_oc_98_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/rmmubk
title: Model_oc_280_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/rtsykl
title: Model_oc_227_M&A (v0=1e12).tab

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

description: :unav
DOI: 10.7910/dvn/9argzn/sj4wov
title: Model_ic_12_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/tjgcnt
title: Model_ic_19_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/tnqrlz
title: Model_ic_32_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/tuug5e
title: Model_oc_237_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ubybl9
title: Model_oc_192_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vdc301
title: Model_oc_86_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vlycxw
title: Model_ic_3_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vsakuk
title: Model_oc_62_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ww5zog
title: Model_ic_20_M&A (v0=1e13).tab

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

description: :unav
DOI: 10.7910/dvn/9argzn/ybecnw
title: Model_oc_27_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/yp33jx
title: Model_oc_8_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/zr4deg
title: Model_oc_110_V1 (v0=1e14).tab

description: :unav
DOI: 10.15468/gdef69
title: Newly reported genus Batracomorphus Lewis (Hemiptera: Cicadellidae: Iassinae) from Pakistan with description of a new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Naveed, Hassan, Sohail, Kamran, Islam, Waqar, Zhang, Yalin (2020): Newly reported genus Batracomorphus Lewis (Hemiptera: Cicadellidae: Iassinae) from Pakistan with description of a new species. Zootaxa 4789 (1): 297-300, DOI: https://doi.org/10.11646/zootaxa.4789.1.13
DOI: 10.21227/zb75-xm11
title: Intel Open Wi-Fi RTT Dataset

description: Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range. Please see the README file for more details.
DOI: 10.7910/dvn/9argzn/2xnivm
title: Model_oc_84_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/37vcpy
title: Model_oc_290_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/6pbup6
title: Model_ic_17_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/aqwam1
title: Model_oc_148_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/bayjkp
title: Model_oc_20_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/bw2ymm
title: Model_ic_43_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/dvtsys
title: Model_oc_33_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ecvtsi
title: Model_ic_6_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/escp8a
title: Model_ic_34_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/fbokch
title: Model_oc_218_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/hf3oed
title: Model_ic_26_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/hsur6i
title: Model_ic_62_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/icabip
title: Model_oc_130_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/if0dii
title: Model_oc_264_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/iw2ywh
title: Model_ic_32_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/kkoynj
title: Model_ic_38_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/mqbhrd
title: Model_oc_3_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/ncyp2x
title: Model_oc_57_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/p8wqst
title: Model_ic_33_V3 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/pyxvic
title: Model_oc_25_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/sqqa4e
title: Model_ic_14_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/stf88x
title: Model_oc_1_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/t5hurk
title: Model_ic_1_V2 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/vqgbfn
title: Model_oc_88_M&A (v0=1e12).tab

description: :unav
DOI: 10.7910/dvn/9argzn/wedli5
title: Model_oc_210_M&A (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/xmzxey
title: Model_ic_62_V1 (v0=1e14).tab

description: :unav
DOI: 10.7910/dvn/9argzn/yeqyo8
title: Model_oc_267_M&A (v0=1e13).tab

description: :unav
DOI: 10.7910/dvn/9argzn/z9xr98
title: Model_oc_157_V1 (v0=1e14).tab

description: :unav
DOI: 10.21335/nmdc-1539462099
title: Hydrography in Kaldfjorden, Troms, Norway, Sep 2016 - Sep 2018

description: The dataset consists of processed CTD data collected along three transects across Kaldfjorden, Troms, between September 2016 and September 2018. CTD profiles were taken from small boat using a handheld SAIV a/s CTD sensor, lowered by hand, and pulled up by winch or by hand, or from a research vessel using a Seabird Electronics SBE911plus CTD package. Information on which sensor was used is included in the dataset. Calibration of the conductivity sensors was done against salinity samples collected throughout the sampling period, as well as by sensor intercalibration. The SBE sensors were factory calibrated annually. Raw temperature and salinity profiles were processed using Matlab to control for and remove outliers, calibrate salinity, convert in situ temperature to potential temperature, bin data to regular 2db bins. Positions of the stations are nominal positions.
DOI: 10.7910/dvn/9argzn/7gocri
title: Model_ic_3_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/80duoq
title: Model_ic_24_V2 (v0=1e14).tab

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

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DOI: 10.7910/dvn/9argzn/9lmeq7
title: Model_oc_54_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/akdw9e
title: Model_oc_241_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/aynept
title: Model_oc_63_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/blhnlw
title: Model_oc_131_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/cs5owb
title: Model_oc_7_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/d3g1ag
title: Model_ic_10_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/daxzpx
title: Model_oc_74_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/fjbu4f
title: Model_ic_50_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ksvbfz
title: Model_oc_91_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/l3muuo
title: Model_oc_47_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/myxnax
title: Model_ic_44_M&A (v0=1e13).tab

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

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DOI: 10.7910/dvn/9argzn/p69kui
title: Model_ic_15_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/q17j7o
title: Model_oc_39_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/q3pnfk
title: Model_oc_171_V2 (v0=1e14).tab

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

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DOI: 10.7910/dvn/9argzn/rdhtk0
title: Model_oc_200_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/s81pgl
title: Model_ic_13_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/tmlo39
title: Model_oc_182_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/wau4qr
title: Model_oc_189_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/ymx3fl
title: Model_oc_39_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/znscgh
title: Model_ic_33_V2 (v0=1e14).tab

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DOI: 10.25919/5ede1e1ed5ac9
title: Parkes pulsar observations with undefined project IDs for 2020_07

description: Parkes pulsar observations with undefined project IDs for 2020. This collection contains observations from project PX060.
DOI: 10.5281/zenodo.3556612
title: Subfossil oribatid mite communities indicate Holocene permafrost dynamics in Canadian mires

description: Original data for article "Subfossil oribatid mite communities indicate Holocene permafrost dynamics in Canadian mires". Oribatid subfossil data from two permafrost mires in Hudson Bay Lowlands, Canada.
DOI: 10.5281/zenodo.3556613
title: Subfossil oribatid mite communities indicate Holocene permafrost dynamics in Canadian mires

description: Original data for article "Subfossil oribatid mite communities indicate Holocene permafrost dynamics in Canadian mires". Oribatid subfossil data from two permafrost mires in Hudson Bay Lowlands, Canada.
DOI: 10.7910/dvn/9argzn/3ye5nn
title: Model_oc_9_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/4a2ebe
title: Model_ic_38_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/4scvvm
title: Model_oc_48_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/7tot3r
title: Model_oc_166_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/8qunw7
title: Model_oc_130_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/aibvyo
title: Model_oc_82_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/b1i7sb
title: Model_oc_6_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/cgwn6k
title: Model_ic_12_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ffblal
title: Model_ic_55_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/fjjtyo
title: Model_oc_268_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/gcbnao
title: Model_oc_4_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ghc9we
title: Model_oc_54_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/hxgsrb
title: Model_oc_258_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/ijee6i
title: Model_oc_70_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/jv5haj
title: Model_oc_147_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/l2md09
title: Model_ic_31_V3 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ljbwox
title: Model_ic_24_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/mjbt9o
title: Model_oc_5_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/nhewew
title: Model_ic_41_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/olsijb
title: Model_ic_5_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ov9igc
title: Model_oc_32_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/pbfnnl
title: Model_oc_244_M&A (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/pk6ctm
title: Model_oc_42_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/pzmzmp
title: Model_oc_296_M&A (v0=1e13).tab

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DOI: 10.7910/dvn/9argzn/qkmzls
title: Model_oc_140_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/qxmmww
title: Model_oc_69_M&A (v0=1e12).tab

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

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DOI: 10.7910/dvn/9argzn/tiny6t
title: Model_oc_13_V2 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/ulbhqz
title: Model_oc_26_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/urkl8n
title: Model_oc_284_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/vc9igq
title: Model_ic_5_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/xxbecb
title: Model_ic_17_V1 (v0=1e14).tab

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DOI: 10.7910/dvn/9argzn/yydgnp
title: Model_oc_32_M&A (v0=1e12).tab

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DOI: 10.7910/dvn/9argzn/z9xlgu
title: Model_oc_6_M&A (v0=1e13).tab

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DOI: 10.21227/qbyn-jn54
title: Intel Open Wi-Fi RTT Dataset

description: Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range. Please see the README file for more details.