cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3753978
title: Transcriptions of interviews with older adults on the use of WhatsApp

description: The file contains German transcriptions of semi-structured qualitative interviews conducted with older adults 65+ in Switzerland in 2019. The topic of the interviews was the older adults' use of the instant-messaging service WhatsApp and its perceived effects on their social relationships.
DOI: 10.5281/zenodo.3786587
title: MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis

description: <strong>MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis</strong> The file Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.dump contains a dump of the protbuff messages recorded by the Met4FoF dataaqusition unit during the calibration measurement. Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.xlsx contains the accelerations recorded by the PTB refferenzsystem for each measurement run. The phase is referred to the analog reference values in the channel Data_11 Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.csv contains the values from the excel table in panda readable form.
description: This project has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme.
DOI: 10.5519/qd.ko8e7ef1
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (183 records)
DOI: 10.25387/g3.12233630
title: Supplemental Material for Booker, 2020

description: This file contains all the supplementary figures and the associated legends for my study on inferring positive selection parameters from the uSFS.
DOI: 10.25387/g3.12233630.v1
title: Supplemental Material for Booker, 2020

description: This file contains all the supplementary figures and the associated legends for my study on inferring positive selection parameters from the uSFS.
DOI: 10.25625/ko7yte/dlhsgg
title: BE-Vie.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/i5e8lt
title: 25_TSplots.Annual.html

description: :unav
DOI: 10.25625/ko7yte/krsr4b
title: CZ-BK1_2014_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/0ldrof
title: CZ-BK1_2011_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/7dtxi1
title: Figure_1_MapOfsites.jpeg

description: :unav
DOI: 10.25625/ko7yte/gplvd2
title: IT-Ro2_2006_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/u73n2x
title: CH-Dav.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/ut38vd
title: US-Ha1_2003_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/4zyjhc
title: US-Syv.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/djiz6f
title: Figure_4_Example_TrendGPP1000.jpeg

description: :unav
DOI: 10.25625/ko7yte/rgmpre
title: Figure_4_Example_GPP1000mp.jpeg

description: :unav
DOI: 10.25625/ko7yte/sfhqmn
title: 12_Structure.data.Analysis.R

description: :unav
DOI: 10.25625/ko7yte/ywmffn
title: 60_Annual.stability.R

description: :unav
DOI: 10.25625/ko7yte/m8gujp
title: 77_Annual.GPPandReco.Anomalies.Different.Cases.Correlation.Results.Rdata

description: :unav
DOI: 10.25625/ko7yte/qmvlwq
title: RU-Fyo.Diameter.Distribution.tab

description: :unav
DOI: 10.25625/ko7yte/srkmqn
title: 23_FluxNetData_Daily.Rdata

description: :unav
DOI: 10.21227/0wnx-6s35
title: Corona Virus (COVID-19) Geolocation-based Sentiment Data

description: This dataset contains the geolocation-based sentiment analysis data. The data is generated 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", "covid-19", "coronavirus" or the variants of "sars-cov-2". Complying with Twitter's data sharing 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 with an exact location that contained any of the previously mentioned keywords.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 50 million tweets) shared in the Corona Virus (COVID-19) Tweets dataset.If you need the geolocation-based data starting March 20, 2020, then use the Corona Virus (COVID-19) Tweets dataset and hydrate the IDs while adding the following condition:if data["coordinates"] is true; use 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.
DOI: 10.25625/ko7yte/9inf8z
title: Figure_3_Speiexample_defense_SPEIexample.jpeg

description: :unav
DOI: 10.25625/ko7yte/lsaybs
title: CZ-BK1_2010_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/mtkpdv
title: US-Ha1_2002_Weibull.jpeg

description: :unav
DOI: 10.3886/e109149
title: Women at Work: Sexual Harassment

description: Over the last two years, awareness about the sexual mistreatment of women has stunned the world. According to analysis by the New York Times, the defeat of Hilary Clinton and election of Donald Trump spurred a women’s movement in the US that began in November of 2016 and resulted in protests across the country, including the largest single-day protest in history on January 21, 2017. Later that year, the #MeToo movement (starting in October 2017) and subsequent #TimesUp movement (starting in January 2018) galvanized women to unite against sexual assault and sexual harassment, which has become the hallmark of the current women’s movement. But has anything changed over this time period in regard to the sexual harassment of women? We examine data from over 500 women at two points in time (September 2016 and September 2018) and found reduced levels of the most egregious forms of sexual harassment (unwanted sexual attention and sexual coercion) but increased levels of gender harassment in 2018 compared to data collected in 2016. More importantly, sexual harassment had a weaker relationship with women’s negative self-views (lower self-esteem, higher self-doubt) in 2018 compared to 2016. Qualitative interviews collected from women in the fall of 2016 and in the fall of 2018 from the same women, support the quantitative data. They suggest that the decrease in the more egregious forms of sexual harassment is due to the increased scrutiny on the topic and the increase in gender harassment is the result of backlash against women. The interviewees also suggest that the diminished relationship between sexual harassment and negative self-views was the result of reduced shame and increased support and empowerment.
DOI: 10.3886/e109149v2-24110
title: Women at Work: Sexual Harassment

description: Over the last two years, awareness about the sexual mistreatment of women has stunned the world. According to analysis by the New York Times, the defeat of Hilary Clinton and election of Donald Trump spurred a women’s movement in the US that began in November of 2016 and resulted in protests across the country, including the largest single-day protest in history on January 21, 2017. Later that year, the #MeToo movement (starting in October 2017) and subsequent #TimesUp movement (starting in January 2018) galvanized women to unite against sexual assault and sexual harassment, which has become the hallmark of the current women’s movement. But has anything changed over this time period in regard to the sexual harassment of women? We examine data from over 500 women at two points in time (September 2016 and September 2018) and found reduced levels of the most egregious forms of sexual harassment (unwanted sexual attention and sexual coercion) but increased levels of gender harassment in 2018 compared to data collected in 2016. More importantly, sexual harassment had a weaker relationship with women’s negative self-views (lower self-esteem, higher self-doubt) in 2018 compared to 2016. Qualitative interviews collected from women in the fall of 2016 and in the fall of 2018 from the same women, support the quantitative data. They suggest that the decrease in the more egregious forms of sexual harassment is due to the increased scrutiny on the topic and the increase in gender harassment is the result of backlash against women. The interviewees also suggest that the diminished relationship between sexual harassment and negative self-views was the result of reduced shame and increased support and empowerment.
DOI: 10.3886/e117331v1
title: Replication Data for: A Century of the American Woman Voter: Sex Gaps in Political Participation, Preferences, and Partisanship Since Women’s Enfranchisement

description: This year marks the centennial of the Nineteenth Amendment, which provided American women a constitutional guarantee to the franchise. We assemble data from a variety of sources to document and explore trends in women’s political participation, issue preferences, and partisanship since that time. We show that in the early years following enfranchisement, women voted at much lower rates than men and held distinct issue preferences, despite splitting their votes across parties similarly to men. But by the dawn of the 21st century, women not only voted more than men, but also voted differently, systematically favoring the Democratic party. We find that the rise in women’s relative voter turnout largely reflects cross-cohort changes in voter participation and coincided with increasing rates of high school completion. By contrast, women’s relative shift toward the Democratic party permeates all cohorts and appears to owe more to changes in how parties have defined themselves than to changes in issue preferences. The findings suggest that a confluence of factors have led to the unique place women currently occupy in the American electorate, one where they are arguably capable of exerting more political influence than ever before.
description: United States voting-age population.
DOI: 10.5281/zenodo.3786130
title: EDI_OpenCalls_RP2Export

description: Export of EDI_OPENCALLS_DB at the end of the second reporting period (M27), i.e. March 2020, including aggregated and anonymized results from 2nd Open Call. This dataset corresponds to the one generated with information about those applying to EDI’s three Open Calls. The contents of such dataset will be the result of aggregating (taking measures to prevent identification of personal data) participants’ attributes, e.g. country of origin, challenge targeted and so on, for statistical purposes and analysis of the performance of the Open Call
DOI: 10.5281/zenodo.3786131
title: EDI_OpenCalls_RP2Export

description: Export of EDI_OPENCALLS_DB at the end of the second reporting period (M27), i.e. March 2020, including aggregated and anonymized results from 2nd Open Call. This dataset corresponds to the one generated with information about those applying to EDI’s three Open Calls. The contents of such dataset will be the result of aggregating (taking measures to prevent identification of personal data) participants’ attributes, e.g. country of origin, challenge targeted and so on, for statistical purposes and analysis of the performance of the Open Call
DOI: 10.25625/ko7yte/g4ncjm
title: US-Ha1_2001_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/gipzks
title: 75_Annual.Anomalies.Different.Cases.Correlation.Results.Rdata

description: :unav
DOI: 10.25625/ko7yte/kjhzhy
title: 11_structural.index.one.value.tab

description: :unav
DOI: 10.25625/ko7yte/vueqbe
title: US-Ha1_2007_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/d0wvws
title: Figure_2_Example_GPP1000mp.jpeg

description: :unav
DOI: 10.25625/ko7yte/efc8mn
title: US-Dk3.Diameter.Distribution.tab

description: :unav
DOI: 10.25625/ko7yte/fyg08k
title: 02_CalcFxns.R

description: :unav
DOI: 10.25625/ko7yte/gb4yph
title: 73_Annual.Anomalies.Wide.Format.anomalyCase.DroughtCase.Rdata

description: :unav
DOI: 10.25625/ko7yte/i9syn1
title: 72_Annual.Anomalies.Wide.Format.Rdata

description: :unav
DOI: 10.25625/ko7yte/tre45f
title: CZ-BK1.Inventory.Data.tab

description: :unav
DOI: 10.5281/zenodo.3785393
title: Table S1. Supplement of https://doi.org/10.5194/gmd-2019-286

description: Model parameter values and composite parameter sensitivities related to the simulated tracer breakthrough curves in Fig. 4 of the article <em>Bodin, J.: MFIT 1.0.0: Multiflow inversion of tracer breakthrough curves in fractured and karst aquifers, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-286, in review, 2020.</em> The beta parameter values at 1.0 × 10<sup>-3</sup> h<sup>-1/2</sup> correspond to the upper bound of the optimization range set for this parameter. Larger values for beta would be physically unrealistic given the mathematical definition of this parameter (Eq. 12).
DOI: 10.25625/ko7yte/0lyksg
title: CZ-BK1_2012_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/cselqb
title: BarPlotCorrelationWithCVGPPSat.jpeg

description: :unav
DOI: 10.25625/ko7yte/hoccv6
title: US-Ha1_2000_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/hqkvcp
title: 01_Variables.tab

description: :unav
DOI: 10.25625/ko7yte/oqh74f
title: 00_xtables.Rmd

description: :unav
DOI: 10.25625/ko7yte/pyhldh
title: DE-Hai_2005_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/rx3zze
title: 04_ManagementDetails.tab

description: :unav
DOI: 10.25625/ko7yte/ylbcaz
title: 25_TSplots.Annual.Rmd

description: :unav
DOI: 10.25625/ko7yte/zkvwas
title: 50_GPP100Calc.R

description: :unav
DOI: 10.7910/dvn/ce2530/4jgvlq
title: coronavirus-tweet-id-2020-01-22-04.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/bxacpt
title: coronavirus-tweet-id-2020-01-28-22.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/frxvng
title: coronavirus-tweet-id-2020-01-25-11.jsonl.gz

description: :unav
DOI: 10.25625/ko7yte/3aauru
title: RU-Fyo.2011.Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/6qlkpv
title: FI-Hyy_2015_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/b7xme5
title: IT-Ro2.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/dpw7hl
title: CZ-BK1_2000_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/e1ma5r
title: 31_SPEIdataAll.RData

description: :unav
DOI: 10.25625/ko7yte/ruyquq
title: 92_Musavi_2017_annual_cvGPPsat.csv

description: :unav
DOI: 10.25625/ko7yte/tpecs7
title: DE-Hai.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/wt9thu
title: US-Prr.Inventory.Data.tab

description: :unav
DOI: 10.7910/dvn/ce2530/bei2ka
title: coronavirus-tweet-id-2020-01-23-04.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/myjvsq
title: coronavirus-tweet-id-2020-01-27-01.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/paiite
title: coronavirus-tweet-id-2020-01-29-22.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/pzlj3h
title: coronavirus-tweet-id-2020-01-30-22.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/rxbige
title: coronavirus-tweet-id-2020-01-26-15.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/satixe
title: coronavirus-tweet-id-2020-01-25-12.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/tt3vlj
title: coronavirus-tweet-id-2020-01-25-15.jsonl.gz

description: :unav
DOI: 10.18112/openneuro.ds002742.v1.0.1
title: P3 study
DOI: 10.25625/ko7yte/cejbic
title: BarPlotCorrelationWithCVGPPSat.jpeg

description: :unav
DOI: 10.25625/ko7yte/g7dbwz
title: 26_TSplots.Daily.html

description: :unav
DOI: 10.25625/ko7yte/os72nr
title: CZ-BK1_2013_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/qqbdje
title: CH-Lae_2005_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/txncfn
title: 74_Annual.Anomalies.Correlation.Results.tab

description: :unav
DOI: 10.25625/ko7yte/udz06x
title: 70_Annual.RRS.R

description: :unav
DOI: 10.25625/ko7yte/xg1iil
title: US-Ha1_2010_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/ytpq13
title: US-WCr_2000_Weibull.jpeg

description: :unav
DOI: 10.7910/dvn/ce2530/7kypsf
title: coronavirus-tweet-id-2020-01-28-15.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/ga0b1i
title: coronavirus-tweet-id-2020-01-25-10.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/hd9wva
title: coronavirus-tweet-id-2020-01-23-06.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/ihz9vs
title: coronavirus-tweet-id-2020-01-29-21.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/lxcer0
title: coronavirus-tweet-id-2020-01-28-18.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/swiulb
title: coronavirus-tweet-id-2020-01-27-03.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/tlkbxe
title: coronavirus-tweet-id-2020-01-23-17.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/yotxqh
title: coronavirus-tweet-id-2020-01-22-18.jsonl.gz

description: :unav
DOI: 10.14469/hpc/7163
title: Michael 1,4 addition, imino replacing carbonyl, S-H-O PT, TS1, G = -994.529986

description: Gaussian Calculation
DOI: 10.25625/ko7yte/4aoqgt
title: IT-Ro1.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/a72wts
title: .Rhistory

description: :unav
DOI: 10.25625/ko7yte/dad8ex
title: US-Ha1_2014_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/fvltdw
title: US-PFa_2006_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/jtvutl
title: CH-Dav_2005_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/nh5hbu
title: FR-Fon.2014.Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/uebgd0
title: US-MMS.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/vbpnag
title: 93_Musavi_2018_annual_cvGPPsat.tab

description: :unav
DOI: 10.5281/zenodo.3786145
title: EDI_Subgrantees_RP2Export

description: Export of EDI_SubGrantees_DB at the end of the second reporting period (M27). It includes the data available at M27, since the 2<sup>nd</sup> round of incubation actually finishes in M28. Only non-privacy sensitive fields from EDI_SubGrantees_DB will be dumped in this export. This dataset corresponds to the following statement encountered it the Grant Agreement: “<em>EDI will publish an Open Dataset with the beneficiaries of our three open calls including their data and funding received. This dataset will be made available at FIWARE Lab. This has been proven as a beneficial measure for transparency but also discoverability of the companies and their investment received in EDI. At the same time, we will populate the wiki-sourced database of Crunchbase to facilitate access to the data of our start-ups and the incubator as such in one of the reference portals in the start-up ecosystem</em>”.
DOI: 10.7910/dvn/ce2530/d18bq8
title: coronavirus-tweet-id-2020-01-26-04.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/ee2zdm
title: coronavirus-tweet-id-2020-01-29-04.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/itz8ur
title: coronavirus-tweet-id-2020-01-28-23.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/jtfoxa
title: coronavirus-tweet-id-2020-01-26-10.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/kdqfln
title: coronavirus-tweet-id-2020-01-31-00.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/kwic6z
title: coronavirus-tweet-id-2020-01-26-13.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/znlpzh
title: coronavirus-tweet-id-2020-01-27-09.jsonl.gz

description: :unav
DOI: 10.21227/pz5g-7q04
title: Evaluation dataset per criteria

description: The plotted graph shows the mean values of the framework evaluation data provided by 10 participants over 8 criteria.
DOI: 10.25625/ko7yte/0ocl1a
title: DE-Lnf.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/7sqipe
title: IT-Ren_2010_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/bgmpuy
title: 21_FluxNetData_Annual.RData

description: :unav
DOI: 10.25625/ko7yte/bwiv6n
title: CZ-BK1_2002_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/g5i0lw
title: 70_Annual.Resilience.Resistance.Rdata

description: :unav
DOI: 10.25625/ko7yte/iznatg
title: FR-Fon.2007.Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/lfp62m
title: CZ-BK1_2006_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/qjsvnw
title: US-MMS_2018_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/vfrort
title: US-MMS_2015_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/yotr4q
title: US-Ha1_2006_Weibull.jpeg

description: :unav
DOI: 10.5281/zenodo.3785362
title: Datasets from Approximate equality of character strings and its application to record linkage in metadata of scientific publications thesis

description: The datasets were produced in my thesis project. The thesis (in Czech language) explores the application of approximate string matching in scientific publication record linkage process. An introduction to record matching along with five commonly used metrics for string distance (Levenshtein, Jaro, Jaro-Winkler, Cosine distances and Jaccard coefficient) are provided. These metrics are applied on publication metadata from V3S current research information system of the Czech Technical University in Prague. Based on the findings, optimal thresholds in the F1, F2 and F3-measures are determined for each metric. Thesis citation:<br> DOBIÁŠOVSKÝ, Jan. <em>Approximate equality of character strings and its application to record linkage in metadata of scientific publications</em> [online]. Praha, 2020 [cit. 2020-05-04]. Masters thesis. Charles University. Faculty of Arts. Institute of Information Studies and Librarianship.
DOI: 10.5281/zenodo.3785363
title: Datasets from Approximate equality of character strings and its application to record linkage in metadata of scientific publications thesis

description: The datasets were produced in my thesis project. The thesis (in Czech language) explores the application of approximate string matching in scientific publication record linkage process. An introduction to record matching along with five commonly used metrics for string distance (Levenshtein, Jaro, Jaro-Winkler, Cosine distances and Jaccard coefficient) are provided. These metrics are applied on publication metadata from V3S current research information system of the Czech Technical University in Prague. Based on the findings, optimal thresholds in the F1, F2 and F3-measures are determined for each metric. Thesis citation:<br> DOBIÁŠOVSKÝ, Jan. <em>Approximate equality of character strings and its application to record linkage in metadata of scientific publications</em> [online]. Praha, 2020 [cit. 2020-05-04]. Masters thesis. Charles University. Faculty of Arts. Institute of Information Studies and Librarianship.
DOI: 10.7910/dvn/ce2530/d6nwnd
title: coronavirus-tweet-id-2020-01-29-11.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/fkacfo
title: coronavirus-tweet-id-2020-01-21-22.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/lawqli
title: coronavirus-tweet-id-2020-01-30-23.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/r0qdbs
title: coronavirus-tweet-id-2020-01-25-06.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/sftmng
title: coronavirus-tweet-id-2020-01-31-11.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/xberq6
title: coronavirus-tweet-id-2020-01-26-16.jsonl.gz

description: :unav
DOI: 10.7910/dvn/ce2530/zqgmdh
title: coronavirus-tweet-id-2020-01-23-10.jsonl.gz

description: :unav
DOI: 10.25625/ko7yte/cywjeb
title: US-WCr.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/djubaw
title: US-UMB_2010_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/hejgoz
title: CZ-BK1_2001_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/ldhslm
title: US-WCr_2010_Weibull.jpeg

description: :unav
DOI: 10.25625/ko7yte/mmvmv2
title: US-Dk2.Inventory.Data.tab

description: :unav
DOI: 10.25625/ko7yte/ohxmdq
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