cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/ffryuavnpbueic-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (1H-quinoxalin-2-one)
title: InChI=1S/C8H6N2O/c11-8-5-9-6-3-1-2-4-7(6)10-8/h1-5H,(H,10,11)

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, 110 °C): 147 (10) [M+H]+, 146 (100) [M], 118 (88), 91 (30), 90 (14), 64 (13), 63 (16). HRMS–EI (m/z): [M]+ Calcd for C8H6O1N2, 146.0480; Found, 146.0480.
DOI: 10.14272/ffryuavnpbueic-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (1H-quinoxalin-2-one)
title: InChI=1S/C8H6N2O/c11-8-5-9-6-3-1-2-4-7(6)10-8/h1-5H,(H,10,11)

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, DMSO-d6, ppm) δ = 7.22–7.36 (m, 2H), 7.55 (td, J = 7.6, 1.4 Hz, 1H), 7.78 (dd, J = 8.3, 1.5 Hz, 1H), 8.17 (s, 1H), 12.42 (s, 1H, -NH).
DOI: 10.14272/ffryuavnpbueic-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (1H-quinoxalin-2-one)
title: InChI=1S/C8H6N2O/c11-8-5-9-6-3-1-2-4-7(6)10-8/h1-5H,(H,10,11)

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, DMSO-d6, ppm) δ = 115.7, 123.2, 128.8, 130.7, 131.8, 132.0, 151.6, 154.9.
DOI: 10.14272/ffryuavnpbueic-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (1H-quinoxalin-2-one)
title: InChI=1S/C8H6N2O/c11-8-5-9-6-3-1-2-4-7(6)10-8/h1-5H,(H,10,11)

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3301, 3145, 3077, 2975, 2944, 2870, 2809, 2745, 2680, 2619, 1959, 1924, 1867, 1836, 1807, 1696, 1673, 1635, 1612, 1537, 1494, 1472, 1424, 1373, 1354, 1332, 1322, 1262, 1254, 1238, 1200, 1142, 1125, 1021, 963, 950, 924, 890, 779, 751, 724, 683, 605, 554, 531, 510, 493, 469, 399 cm–1.
DOI: 10.21227/q73p-mb90
title: Noise Data Base (iNoise)

description: A collection of India specific real-world noise collected over a period of couple of months. The data was collected using a Dialogic CTI card. Most of the data was collected using a variety of mobile phone on a variety of mobile networks. There are 130 outdoor noise files and 190 indoor noise files each of duration approximately between 50 and 60 seconds. A total of 15113 seconds of noise data (9114 seconds of indoor and 5998 seconds of outdoor noise data). Noise types [0?] outdoor 01 (Travel in) Auto-rickshaw 02 (Travel in) Bus 03 (Besides) Highway 04 Railway station 05 (Busy) Street [5?] indoor 51 Airport 52 Cafeteria 53 Car 54 Home 55 Mall 56 (Travel in) Train 57 Workplace 99 Ambiguous (when they were not very sure of the environment that they were in)
DOI: 10.21227/khvd-st78
title: Noise Data Base (iNoise)

description: A collection of India specific real-world noise collected over a period of couple of months. The data was collected using a Dialogic CTI card. Most of the data was collected using a variety of mobile phone on a variety of mobile networks. There are 130 outdoor noise files and 190 indoor noise files each of duration approximately between 50 and 60 seconds. A total of 15113 seconds of noise data (9114 seconds of indoor and 5998 seconds of outdoor noise data). Noise types [0?] outdoor 01 (Travel in) Auto-rickshaw 02 (Travel in) Bus 03 (Besides) Highway 04 Railway station 05 (Busy) Street [5?] indoor 51 Airport 52 Cafeteria 53 Car 54 Home 55 Mall 56 (Travel in) Train 57 Workplace 99 Ambiguous (when they were not very sure of the environment that they were in)
DOI: 10.7910/dvn/ydbmni/6aouo0
title: house_countycorr19751976.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/8fdl6y
title: house_countycorr19671968.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/bjylap
title: house_countycorr19992000.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/na6u66
title: .Rapp.history

description: :unav
DOI: 10.7910/dvn/ydbmni/xjgdph
title: mayors_elecs.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/xso21s
title: big_cities.csv

description: :unav
DOI: 10.7910/dvn/ydbmni/1imgqj
title: federal_partisan_control.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/d3vohl
title: senate_public.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/fa6w8i
title: potus_public.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/hl8mpt
title: unemployment.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/hode0r
title: va_city_names_icma.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/kz1llf
title: upper_county_corr_2014.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/2lw219
title: econvoting_counties_maps.R

description: :unav
DOI: 10.7910/dvn/ydbmni/5h7neu
title: councilcomp.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/9yg6i1
title: lower_county_corr_2014.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/bjqhce
title: house_countycorr20052006.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/hsntye
title: cpi.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/na2iwz
title: sld_1968_1989.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/v9rgwg
title: house_countycorr19591960.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/xzywxl
title: election_returns_2016.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/adbann
title: house_countycorr19571958.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/c4yyl5
title: county_TW_ideology_estimates.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/cw29bc
title: house_countycorr19811982.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/gkgn4a
title: house_countycorr20032004.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/nkkcm2
title: VA_cities_counties_link2.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/oxzvty
title: house_countycorr19651966.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/uyyjyy
title: downballot_elecs_oc_processed.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/xpj4sx
title: house_countycorr19791980.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/lnrxno
title: upper_county_corr_2000.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/n7o11r
title: VA_cities_counties_link.csv

description: :unav
DOI: 10.7910/dvn/ydbmni/niu92b
title: .Rhistory

description: :unav
DOI: 10.7910/dvn/ydbmni/p5tjbr
title: qcew_income.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/r0djfs
title: counties_papers_panel.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/s8gn2z
title: mayoral_election_check_all75k.csv

description: :unav
DOI: 10.7910/dvn/ydbmni/ulekpy
title: states_icpsr.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/weucgs
title: house_countycorr19711972.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/z5cgjg
title: house_county_crosswalk.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/9xmasw
title: house_countycorr19731974.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/cezia4
title: election_types.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/fxk0me
title: governor_public.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/hvnr51
title: upper_county_corr_2002_old.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/jua6wp
title: house_elections_1932_2016.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/nvfuq6
title: lower_county_corr_1968.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/xsjxt8
title: econvoting_counties_figures.R

description: :unav
DOI: 10.7910/dvn/ydbmni/y70hwe
title: election_returns_1964_1990.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/zivxml
title: lower_county_corr_2000.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/2pffcb
title: house_countycorr19631964.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/4zpor1
title: house_countycorr20132014.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/9i4yhj
title: house_countycorr20152016.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/9qzb8a
title: election_returns_2018.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/ahlwjn
title: upper_county_corr_2002.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/jepz4i
title: lower_county_corr_2002_old.tab

description: :unav
DOI: 10.7910/dvn/ydbmni/mpf0cg
title: counties_list.tab

description: :unav
DOI: 10.21227/byha-ac60
title: General group-based epidemic model for spreading processes on networks: GgroupEM (datasets)

description: We develop a general group-based continuous-time Markov epidemic model (GgroupEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic epidemic spreading over large complex networks while being informative about the state of groups. Aggregating nodes by groups, the state-space becomes smaller than the one of individual-based approach at the cost of an aggregation error, which is bounded by the well-known isoperimetric inequality. We also develop a mean-field approximation of this framework to reduce the state-space size further. Finally, we extend the GgroupEM to multilayer networks. Individual-based frameworks are in general not computationally efficient. However, the individual-based approach is essential when the objective is to study the local dynamics at the individual level. Therefore, we propose a group-based framework to reduce the computational time of the Individual-based generalized epidemic model framework (GEMF) but retain its advantages.
DOI: 10.21227/ekbm-2p40
title: General group-based epidemic model for spreading processes on networks: GgroupEM (datasets)

description: We develop a general group-based continuous-time Markov epidemic model (GgroupEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic epidemic spreading over large complex networks while being informative about the state of groups. Aggregating nodes by groups, the state-space becomes smaller than the one of individual-based approach at the cost of an aggregation error, which is bounded by the well-known isoperimetric inequality. We also develop a mean-field approximation of this framework to reduce the state-space size further. Finally, we extend the GgroupEM to multilayer networks. Individual-based frameworks are in general not computationally efficient. However, the individual-based approach is essential when the objective is to study the local dynamics at the individual level. Therefore, we propose a group-based framework to reduce the computational time of the Individual-based generalized epidemic model framework (GEMF) but retain its advantages.
DOI: 10.17632/gvvzf7yrdg.1
title: Data for: An analysis of the downward-flushing flow on the crest of a levee under combined wave and surge overtopping

description: Here I just uploaded the used data in this manuscript. The measured time series is a little messy and I give up to upload them. But you are welcome to ask me for it if you're interested.
DOI: 10.7910/dvn/5e6gbn/u4pnal
title: edu_schools_primary_EST.tab

description: Tabular data (tab delimited) with metadata before the header (first 6 lines identified with #). Source and notes columns included beside data columns.
DOI: 10.17632/zts3zb53cx.1
title: Data for: The role of dyadic social capital in enhancing collaborative knowledge creation
DOI: 10.5519/qd.gbh3y0qa
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (11146 records)
DOI: 10.7910/dvn/4wk146/piidxt
title: edu_schools_primary_LAT.tab

description: Tabular data (tab delimited) with metadata before the header (first 6 lines identified with #). Source and notes columns included beside data columns.
DOI: 10.7910/dvn/rtazzg/ccicz0
title: Covid19_BR_mun

description: 2020_02_26
DOI: 10.17632/42dgdr7dmw.3
title: Data for: Radiolarian assemblage as an indicator of environmental conditions in the marginal seas of the Western North Pacific

description: Supplementary material Supplementary material Table 1, Taxonomic information of total radiolarian taxa encountered in study. Supplementary material Table 2, Basic environmental informations with annual, summer, and winter timescales of the 45 surface sediment samples. Supplementary material Table 3, Raw counting data of the total radiolarian assemblages in the 45 surface sediment samples.
DOI: 10.5519/qd.xsz8dntq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (0 records)
DOI: 10.17632/gvvzf7yrdg.2
title: Data for: An analysis of the downward-flushing flow on the crest of a levee under combined wave and surge overtopping

description: Here I just uploaded the used data in this manuscript. The measured time series is a little messy and I give up to upload them. But you are welcome to ask me for it if you're interested.
DOI: 10.5281/zenodo.3740366
title: Making Nd3+ a Sensitive Luminescent Thermometer for Physiological Temperatures — An Account of Pitfalls in Boltzmann Thermometry

description: Dataset accompanying figures published in the publication DOI: 10.3390/nano10030543
description: This project has received funding from the European Union's Horizon 2020 FET Open programme under grant agreement No 801305 (NanoTBTech).
DOI: 10.5281/zenodo.3740367
title: Making Nd3+ a Sensitive Luminescent Thermometer for Physiological Temperatures — An Account of Pitfalls in Boltzmann Thermometry

description: Dataset accompanying figures published in the publication DOI: 10.3390/nano10030543
description: This project has received funding from the European Union's Horizon 2020 FET Open programme under grant agreement No 801305 (NanoTBTech).
DOI: 10.7910/dvn/l9ynv6/egubns
title: edu_schools_primary_LIT.tab

description: Tabular data (tab delimited) with metadata before the header (first 6 lines identified with #). Source and notes columns included beside data columns.
DOI: 10.15468/at2srp
title: Framily Emballonuridae (Sheath-tailed Bats)

description: This dataset contains the digitized treatments in Plazi based on the original book chapter Bonaccorso, Frank (2019): Framily Emballonuridae (Sheath-tailed Bats). In: Wilson, Don E., Mittermeier, Russel A. (Eds): Handbook of the Mammals of the World. Volume 9. Lyny Edicions: 350-373, ISBN: 978-84-16728-19-0, DOI: http://doi.org/10.5281/zenodo.3740269
DOI: 10.7910/dvn/4wk146/g8g5vg
title: edu_schools_primary_LAT.tab

description: Tabular data (tab delimited) with metadata before the header (first 6 lines identified with #). Source and notes columns included beside data columns.
DOI: 10.7910/dvn/5e6gbn/pdfbic
title: edu_schools_primary_EST.tab

description: Tabular data (tab delimited) with metadata before the header (first 6 lines identified with #). Source and notes columns included beside data columns.
DOI: 10.21227/d1av-cq50
title: Corona Virus (COVID-19) Tweets Dataset (en)

description: Tweets Counter: 17,713,153This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets, using filters: language “en” and keyword “corona”. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose (ignore every other LICENSE category given in this page).Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details (Tweets collected in GMT+0; Local time mentioned below: GMT+5:45):corona_tweets_01.csv: 831,327 tweets (March 20, 2020 01:37 AM - March 20, 2020 10:28 AM)corona_tweets_02.csv: 870,924 tweets (March 20, 2020 10:31 AM - March 20, 2020 09:43 PM)corona_tweets_03.csv: 773,729 tweets (March 20, 2020 09:49 PM - March 21, 2020 09:25 AM)corona_tweets_04.csv: 1,233,340 tweets (March 21, 2020 09:27 AM - March 22, 2020 07:46 AM)corona_tweets_05.csv: 1,782,157 tweets (March 22, 2020 07:50 AM - March 23, 2020 09:08 AM)corona_tweets_06.csv: 1,771,295 tweets (March 23, 2020 09:11 AM - March 24, 2020 11:35 AM)corona_tweets_07.csv: 1,479,651 tweets (March 24, 2020 11:42 AM - March 25, 2020 11:43 AM)corona_tweets_08.csv: 1,272,592 tweets (March 25, 2020 11:47 AM - March 26, 2020 12:46 PM)corona_tweets_09.csv: 1,091,429 tweets (March 26, 2020 12:51 PM - March 27, 2020 11:53 AM)corona_tweets_10.csv: 1,172,013 tweets (March 27, 2020 11:56 AM - March 28, 2020 01:59 PM)corona_tweets_11.csv: 1,141,210 tweets (March 28, 2020 02:03 PM - March 29, 2020 04:01 PM)----- March 29, 2020 04:05 PM - March 30, 2020 12:30 PM -- Some folk(s) messed around with the server. Tweets for this period won't be available. However, I'll be continuing adding the new Tweet IDs. Some preventive measures have been taken. Sorry for the inconvenience. -----corona_tweets_12.csv: 793,417 tweets. (March 30, 2020 02:01 PM - March 31, 2020 10:16 AM)corona_tweets_13.csv: 1,029,294 tweets (March 31, 2020 10:20 AM - April 01, 2020 10:59 AM)corona_tweets_14.csv: 920,076 tweets (April 01, 2020 11:02 AM - April 02, 2020 12:19 PM)corona_tweets_15.csv: 826,271 tweets (April 02, 2020 12:21 PM - April 03, 2020 02:38 PM)corona_tweets_16.csv: 612,512 tweets (April 03, 2020 02:40 PM - April 04, 2020 11:54 AM)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/7k00-jp41
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 17,713,153This dataset includes CSV files which contain the tweet IDs. The tweets have been collected by the LSTM model deployed here at sentiment.live. The model monitors the real-time Twitter feed for corona virus-related tweets, using filters: language “en” and keyword “corona”. As per the Twitter Developer Policy, it is not possible for me to provide information other than the Tweet IDs (this dataset has been completely re-designed on March 20, 2020, to comply with data sharing policies set by Twitter). Note: This dataset should be solely used for non-commercial research purpose (ignore every other LICENSE category given in this page).Schema of the CSV files: First column: tweet ID, Second column: Sentiment score for the particular tweet.Files details (Tweets collected in GMT+0; Local time mentioned below: GMT+5:45):corona_tweets_01.csv: 831,327 tweets (March 20, 2020 01:37 AM - March 20, 2020 10:28 AM)corona_tweets_02.csv: 870,924 tweets (March 20, 2020 10:31 AM - March 20, 2020 09:43 PM)corona_tweets_03.csv: 773,729 tweets (March 20, 2020 09:49 PM - March 21, 2020 09:25 AM)corona_tweets_04.csv: 1,233,340 tweets (March 21, 2020 09:27 AM - March 22, 2020 07:46 AM)corona_tweets_05.csv: 1,782,157 tweets (March 22, 2020 07:50 AM - March 23, 2020 09:08 AM)corona_tweets_06.csv: 1,771,295 tweets (March 23, 2020 09:11 AM - March 24, 2020 11:35 AM)corona_tweets_07.csv: 1,479,651 tweets (March 24, 2020 11:42 AM - March 25, 2020 11:43 AM)corona_tweets_08.csv: 1,272,592 tweets (March 25, 2020 11:47 AM - March 26, 2020 12:46 PM)corona_tweets_09.csv: 1,091,429 tweets (March 26, 2020 12:51 PM - March 27, 2020 11:53 AM)corona_tweets_10.csv: 1,172,013 tweets (March 27, 2020 11:56 AM - March 28, 2020 01:59 PM)corona_tweets_11.csv: 1,141,210 tweets (March 28, 2020 02:03 PM - March 29, 2020 04:01 PM)----- March 29, 2020 04:05 PM - March 30, 2020 12:30 PM -- Some folk(s) messed around with the server. Tweets for this period won't be available. However, I'll be continuing adding the new Tweet IDs. Some preventive measures have been taken. Sorry for the inconvenience. -----corona_tweets_12.csv: 793,417 tweets. (March 30, 2020 02:01 PM - March 31, 2020 10:16 AM)corona_tweets_13.csv: 1,029,294 tweets (March 31, 2020 10:20 AM - April 01, 2020 10:59 AM)corona_tweets_14.csv: 920,076 tweets (April 01, 2020 11:02 AM - April 02, 2020 12:19 PM)corona_tweets_15.csv: 826,271 tweets (April 02, 2020 12:21 PM - April 03, 2020 02:38 PM)corona_tweets_16.csv: 612,512 tweets (April 03, 2020 02:40 PM - April 04, 2020 11:54 AM)To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [1] has been appended as the second column. New databases will be added to this dataset every day. Bookmark this page for further updates. [1] https://textblob.readthedocs.io/en/dev/
DOI: 10.3886/e113065v1-23521
title: Replication data for: Inspecting the Mechanism: Leverage and the Great Recession in the Eurozone

description: We provide a comprehensive account of the dynamics of eurozone countries from 2000 to 2012. We analyze private leverage, fiscal policy, labor costs, and spreads, and we propose a model and an identification strategy to separate the impact of credit cycles, excessive government spending, and sudden stops. We then ask how periphery countries would have fared with different policies. We find that countries could have stabilized their employment if they had followed more conservative fiscal policies during the boom. Macroprudential policies and an early intervention by the central bank to prevent market segmentation and reduce fiscal austerity would also have significantly reduced the recession.
DOI: 10.5281/zenodo.3740441
title: GWAS output for early salt stress responses in Arabidopsis thaliana accessions

description: The output of the Genome Wide Association Study for early responses to salt stress in Arabidopsis accessions. The "_gwas2029.rda" files contain associations found for each trait using single models, where the first part of file name describes the phenotype (e.g. ROUNDNESS), the first number describes the day after stress application, the subsequent C/S indicated whether the association was performed with the trait scored at Control / Salt Stress conditions respectively. The "mtmm_final.rda" files contain the associations found between two traits, indicated as above in the first part of the file name.
DOI: 10.5281/zenodo.3740442
title: GWAS output for early salt stress responses in Arabidopsis thaliana accessions

description: The output of the Genome Wide Association Study for early responses to salt stress in Arabidopsis accessions. The "_gwas2029.rda" files contain associations found for each trait using single models, where the first part of file name describes the phenotype (e.g. ROUNDNESS), the first number describes the day after stress application, the subsequent C/S indicated whether the association was performed with the trait scored at Control / Salt Stress conditions respectively. The "mtmm_final.rda" files contain the associations found between two traits, indicated as above in the first part of the file name.
DOI: 10.7910/dvn/l9ynv6/grj4za
title: edu_schools_primary_LIT.tab

description: Tabular data (tab delimited) with metadata before the header (first 6 lines identified with #). Source and notes columns included beside data columns.
DOI: 10.17632/hj6bn5dc87.2
title: Data for: New hydroperoxylated and 20,24-epoxylated dammarane triterpenes from the rot roots of Panax notoginseng

description: The raw 1D and 2D NMR data of new compounds 1-7
DOI: 10.7910/dvn/fh8pew/1fxpmz
title: OnlineAppendices.pdf

description: :unav
DOI: 10.7910/dvn/hxn9aw
title: Increased COVID-19 Mortality in geographies with higher prevalence of the Pi*SZ Alpha 1 Antitrypsin Genotype

description: As of the time of this paper, more than 800,000 people have been confirmed to be infected by the COVID-19 virus and over 40,000 people have died. In this paper we have identified a correlation between the geographic prevalence of the Pi*SZ genotype of the alpha 1 antitrypsin gene (A1AT) and COVID-19 mortality. This observation if confirmed and extended, would suggest that patients carrying the Pi*SZ A1AT genotype may be at increased mortality risk due to COVID-19 infection. Additionally, this observation may offer new therapeutic opportunities for COVID-19 induced respiratory distress through the development and use of elastase inhibitors or A1AT enhancers.