cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc27wb4m
title: CCDC 2081840: Experimental Crystal Structure Determination

description: Related Article: Nilamuni H. de Silva, Suneela Pyreddy, Ewan W. Blanch, Helmut M. Hügel, Subashani Maniam|2021|Bioorg.Chem.|114|105128|doi:10.1016/j.bioorg.2021.105128
DOI: 10.5517/ccdc.csd.cc27wmzr
title: CCDC 2082145: Experimental Crystal Structure Determination

description: Related Article: Souad Dgachi, Fatma Rahmouni, Albert Soran, Mongi Saoudi, Gabriela Nemes, Houcine Naïli|2021|J.Mol.Struct.|1244|130996|doi:10.1016/j.molstruc.2021.130996
DOI: 10.5517/ccdc.csd.cc27s529
title: CCDC 2078800: Experimental Crystal Structure Determination

description: Related Article: Julia A. Eremina, Ekaterina A. Ermakova, Ksenia S. Smirnova, Lyubov S. Klyushova, Alexey S. Berezin, Taisiya S. Sukhikh, Alexander A. Zubenko, Leonid N. Fetisov, Kristina N. Kononenko, Elizaveta V. Lider|2021|Polyhedron|206|115352|doi:10.1016/j.poly.2021.115352
DOI: 10.5517/ccdc.csd.cc27s518
title: CCDC 2078799: Experimental Crystal Structure Determination

description: Related Article: Julia A. Eremina, Ekaterina A. Ermakova, Ksenia S. Smirnova, Lyubov S. Klyushova, Alexey S. Berezin, Taisiya S. Sukhikh, Alexander A. Zubenko, Leonid N. Fetisov, Kristina N. Kononenko, Elizaveta V. Lider|2021|Polyhedron|206|115352|doi:10.1016/j.poly.2021.115352
DOI: 10.5517/ccdc.csd.cc27s507
title: CCDC 2078798: Experimental Crystal Structure Determination

description: Related Article: Julia A. Eremina, Ekaterina A. Ermakova, Ksenia S. Smirnova, Lyubov S. Klyushova, Alexey S. Berezin, Taisiya S. Sukhikh, Alexander A. Zubenko, Leonid N. Fetisov, Kristina N. Kononenko, Elizaveta V. Lider|2021|Polyhedron|206|115352|doi:10.1016/j.poly.2021.115352
DOI: 10.5517/ccdc.csd.cc27s4z5
title: CCDC 2078797: Experimental Crystal Structure Determination

description: Related Article: Julia A. Eremina, Ekaterina A. Ermakova, Ksenia S. Smirnova, Lyubov S. Klyushova, Alexey S. Berezin, Taisiya S. Sukhikh, Alexander A. Zubenko, Leonid N. Fetisov, Kristina N. Kononenko, Elizaveta V. Lider|2021|Polyhedron|206|115352|doi:10.1016/j.poly.2021.115352
DOI: 10.5281/zenodo.5111112
title: MAD (MAlicious Traffic Dataset) in home and commercial environments - Internal environment

description: In this environment we have: 01 Wifi Router, 01 Smartphone, 01 server and 01 desktop with virtual machines. This environment, called Internal, is a local network. One of the servers has the Security and Performance Monitoring Environment installed. In addition, 05 virtual machines were instantiated via QEMU on the same network. In this server, a network card provides connectivity to the environment and the other network card receives all network traffic for analysis by the Monitoring Environment. Getting traffic to Suricata is done by Ettercap. The desktop has two virtual machines instantiated via Oracle VirtualBox, on the same network and acts on the network as a client as well. The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases. events.csv.gz - Suricata / Evebox collections net.csv.gz - Telegraf collections from the TICK stack netstat.csv.gz - Telegraf collections from the TICK stack For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files. The interval between collections, non-consecutive, was from 2018-06-06 to 2019-01-31
DOI: 10.5281/zenodo.5112290
title: MAD (MAlicious Traffic Dataset) in home and commercial environments - Environment with scalability

description: We have used the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server. We added 08 virtual machines and performed the following test with a denial of service DoS attack: 01 virtual machine from 04:00 pm to 23:55 pm on 2019-12-04 with an interval every 01 hour;<br> 02 virtual machines from 23:55 am on 2019-12-04 to 08:50 am on 2019-12-05 with an interval every 01 hour;<br> 04 virtual machines as of 08:55 am on 2019-12-05 to 05:25 pm on 2019-12-06 with an interval every 5 minutes;<br> 08 virtual machines from 05:30 pm on 2019-12-06 to 23:59 on 2019-12-06 with an interval every 5 minutes;<br> End of tests with shutdown of virtual machines at 23:59 on 2019-12-06. The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases. events.csv.gz - Suricata / Evebox collections net.csv.gz - Telegraf collections from the TICK stack netstat.csv.gz - Telegraf collections from the TICK stack For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files. The interval between collections, non-consecutive, was from 2019-12-04 to 2019-12-06
DOI: 10.5281/zenodo.5094055
title: MAD (MAlicious Traffic Dataset) in home and commercial environments - Home environment

description: For the home environment we have: 01 Wifi Modem Router, 03 Smartphones, 01 server, 01 desktop, 01 Multifunction Printer, 01 network extender, 01 SmartTV, 01 Cable TV decoder and 01 firewall. This environment is a local network. The server has the Monitoring Environment and a network card, which provides connectivity and receives all network traffic for analysis. The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases. events.csv.gz - Suricata / Evebox collections net.csv.gz - Telegraf collections from the TICK stack netstat.csv.gz - Telegraf collections from the TICK stack For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files. The interval between collections, non-consecutive, was from 2018-09-15 to 2019-02-04
description: -
DOI: 10.5281/zenodo.5094054
title: MAD (MAlicious Traffic Dataset) in home and commercial environments - Home environment

description: For the home environment we have: 01 Wifi Modem Router, 03 Smartphones, 01 server, 01 desktop, 01 Multifunction Printer, 01 network extender, 01 SmartTV, 01 Cable TV decoder and 01 firewall. This environment is a local network. The server has the Monitoring Environment and a network card, which provides connectivity and receives all network traffic for analysis. The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases. events.csv.gz - Suricata / Evebox collections net.csv.gz - Telegraf collections from the TICK stack netstat.csv.gz - Telegraf collections from the TICK stack For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files. The interval between collections, non-consecutive, was from 2018-09-15 to 2019-02-04
description: -
DOI: 10.5281/zenodo.5112289
title: MAD (MAlicious Traffic Dataset) in home and commercial environments - Environment with scalability

description: We have used the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server. We added 08 virtual machines and performed the following test with a denial of service DoS attack: 01 virtual machine from 04:00 pm to 23:55 pm on 2019-12-04 with an interval every 01 hour;<br> 02 virtual machines from 23:55 am on 2019-12-04 to 08:50 am on 2019-12-05 with an interval every 01 hour;<br> 04 virtual machines as of 08:55 am on 2019-12-05 to 05:25 pm on 2019-12-06 with an interval every 5 minutes;<br> 08 virtual machines from 05:30 pm on 2019-12-06 to 23:59 on 2019-12-06 with an interval every 5 minutes;<br> End of tests with shutdown of virtual machines at 23:59 on 2019-12-06. The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases. events.csv.gz - Suricata / Evebox collections net.csv.gz - Telegraf collections from the TICK stack netstat.csv.gz - Telegraf collections from the TICK stack For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files. The interval between collections, non-consecutive, was from 2019-12-04 to 2019-12-06
DOI: 10.5281/zenodo.5111959
title: MAD (MAlicious Traffic Dataset) in home and commercial environments - Internet environment

description: We have for the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server. The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases. events.csv.gz - Suricata / Evebox collections net.csv.gz - Telegraf collections from the TICK stack netstat.csv.gz - Telegraf collections from the TICK stack For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files. The interval between collections, non-consecutive, was from 2018-08-28 to 2019-11-14
DOI: 10.5281/zenodo.5111958
title: MAD (MAlicious Traffic Dataset) in home and commercial environments - Internet environment

description: We have for the Internet environment: 01 Switch, 01 IP camera, 01 server for monitoring, 01 server for honeypot and no firewall. This environment is directly connected to the Internet. We installed a server, functioning as a Monitoring Environment. The network traffic was obtained via Port Mirroring on the switch to the Monitoring Environment server. The results were obtained from Suricata and Telegraf collections from the TICK stack. All evidence was performed by queries via EveBox, which received data from Suricata, Grafana or graphics with information extracted from the InfluxDB (Grafana) and PostgreSQL (EveBox) databases. events.csv.gz - Suricata / Evebox collections net.csv.gz - Telegraf collections from the TICK stack netstat.csv.gz - Telegraf collections from the TICK stack For correlation purposes, use the events.csv.gz file as a basis. The key to correlation is the 'timestamp' column events.csv.gz with the 'time' column in the net.csv.gz and netstat.csv.gz files. The interval between collections, non-consecutive, was from 2018-08-28 to 2019-11-14
DOI: 10.6084/m9.figshare.14032742
title: Text classification to streamline online wildlife trade analyses

description: Code and data for text classification models association with "Text classification to streamline online wildlife trade analyses". by Stringham OC, Moncayo S, Hill KGW, Toomes A, Mitchell L, Ross JV, Cassey P. PLOS ONE https://doi.org/10.1371/journal.pone.0254007
DOI: 10.6084/m9.figshare.14032742.v3
title: Text classification to streamline online wildlife trade analyses

description: Code and data for text classification models association with "Text classification to streamline online wildlife trade analyses". by Stringham OC, Moncayo S, Hill KGW, Toomes A, Mitchell L, Ross JV, Cassey P. PLOS ONE https://doi.org/10.1371/journal.pone.0254007
DOI: 10.6084/m9.figshare.14032742.v2
title: Text classification to streamline online wildlife trade analyses

description: Code and data for text classification models association with "Text classification to streamline online wildlife trade analyses". by Stringham OC, Moncayo S, Hill KGW, Toomes A, Mitchell L, Ross JV, Cassey P. Submitted to peer review, PDF for pre print forthcoming.
DOI: 10.6084/m9.figshare.13340414
title: Propagule Pressure Sensitivity and Uncertainty Analysis

description: Code and data associated with "Managing propagule pressure to prevent invasive species establishments: propagule size, number, and risk-release curve" by Oliver C. Stringham & Julie L. Lockwood. Ecological Applications (2021) https://doi.org/10.1002/eap.2314.<br>
DOI: 10.6084/m9.figshare.13340414.v1
title: Propagule Pressure Sensitivity and Uncertainty Analysis

description: Code and data associated with "Managing propagule pressure to prevent invasive species establishments: propagule size, number, and risk-release curve" by Oliver C. Stringham & Julie L. Lockwood. Ecological Applications (2021) https://doi.org/10.1002/eap.2314.<br>
DOI: 10.5281/zenodo.5112632
title: Compressed Datasets for GNUVID

description: Compressed dataset for GNUVID
DOI: 10.6084/m9.figshare.15001869
title: Two Filial Piety Dimensions Play Different Roles in Facilitating Moral Concern for In~ and Out~ groups: A Cross-cultural Study Comparing Chinese and Indonesian College Students

description: 在这项研究中，429 名中国大学生（171 名男性，18-20 岁）和 363 名印度尼西亚大学生（174 名男性，23-29 岁）完成了双重孝道量表（DFPS），将他人纳入自我量表 (IOS) 和道德关注圈 (CMR)。
DOI: 10.6084/m9.figshare.15001869.v1
title: Two Filial Piety Dimensions Play Different Roles in Facilitating Moral Concern for In~ and Out~ groups: A Cross-cultural Study Comparing Chinese and Indonesian College Students

description: 在这项研究中，429 名中国大学生（171 名男性，18-20 岁）和 363 名印度尼西亚大学生（174 名男性，23-29 岁）完成了双重孝道量表（DFPS），将他人纳入自我量表 (IOS) 和道德关注圈 (CMR)。
DOI: 10.6084/m9.figshare.14122800
title: How Vulnerable Are U.S. Crop Workers?: Evidence from Representative Worker Data and Implications for COVID-19

description: <b>Objectives</b> This paper examines health profiles and work environments of hired U.S. farmworkers to understand the risk to essential workers and their employers, to the food supply, and to rural health systems such as what is possible with the COVID-19 pandemic. <b>Methods</b> Large-sample statistical methods and proprietary data from the National Agricultural Workers Survey from 2000 to 2018 were used to assess factors associated with exposure to COVID-19 and vulnerabilities associated with medical complications. <b>Results</b> An aging workforce and increased access to health care within the crop worker population has been associated with a higher reported incidence of diabetes, asthma, and heart disease among workers over time. These trends confirm a vulnerable, but essential, workforce with higher risks for COVID-19 complications than would have been true of U.S. farmworkers as a group in earlier years. <b>Conclusions</b> Increasing age and disease burden in the U.S. agricultural labor force puts workers at increased risk for developing COVID-19 complications. Limits to field sanitation and housing quality inflate the probability of the development of COVID-19 hotbeds in rural communities that could further compromise the physical health of workers, the economic health of farm establishments, the agricultural supply-chain, and rural health capacities. Additional and more targeted worker protections may minimize public health and economic costs in the long run.
DOI: 10.6084/m9.figshare.14122800.v1
title: How Vulnerable Are U.S. Crop Workers?: Evidence from Representative Worker Data and Implications for COVID-19

description: <b>Objectives</b> This paper examines health profiles and work environments of hired U.S. farmworkers to understand the risk to essential workers and their employers, to the food supply, and to rural health systems such as what is possible with the COVID-19 pandemic. <b>Methods</b> Large-sample statistical methods and proprietary data from the National Agricultural Workers Survey from 2000 to 2018 were used to assess factors associated with exposure to COVID-19 and vulnerabilities associated with medical complications. <b>Results</b> An aging workforce and increased access to health care within the crop worker population has been associated with a higher reported incidence of diabetes, asthma, and heart disease among workers over time. These trends confirm a vulnerable, but essential, workforce with higher risks for COVID-19 complications than would have been true of U.S. farmworkers as a group in earlier years. <b>Conclusions</b> Increasing age and disease burden in the U.S. agricultural labor force puts workers at increased risk for developing COVID-19 complications. Limits to field sanitation and housing quality inflate the probability of the development of COVID-19 hotbeds in rural communities that could further compromise the physical health of workers, the economic health of farm establishments, the agricultural supply-chain, and rural health capacities. Additional and more targeted worker protections may minimize public health and economic costs in the long run.
DOI: 10.6084/m9.figshare.11996226.v2
title: Gender differences in respiratory health outcomes among farming cohorts around the globe: findings from the AGRICOH consortium

description: <b>Purpose</b>: Respiratory hazards of farming have been identified for centuries, with little focus on gender differences. We used data from the AGRICOH consortium, a collective of prospective cohorts of agricultural workers, to assess respiratory disease prevalence among adults in 18 cohorts representing over 200,000 farmers, farm workers, and their spouses from six continents. <b>Methods</b>: Cohorts collected data between 1992 and 2016 and ranged in size from 200 to &gt;128,000 individuals; 44% of participants were female. Farming practices varied from subsistence farming to large-scale industrial agriculture. All cohorts provided respiratory outcome information for their cohort based on their study definitions. The majority of outcomes were based on self-report using standard respiratory questionnaires; the greatest variability in assessment methods was associated with chronic obstructive pulmonary disease (COPD). <b>Results</b>: For all three respiratory symptoms (cough, phlegm, and wheeze), the median prevalence in men was higher than in women, with the greatest difference for phlegm (17% vs. 10%). For asthma, women had a higher prevalence (7.8% vs 6.5%), with the difference associated with allergic asthma. The relative proportion of allergic asthma varied among cohorts. In two of eight cohorts for women and two of seven cohorts for men, allergic asthma was more common than non-allergic asthma. <b>Conclusions</b>: These findings indicate that respiratory outcomes are common among farmers around the world despite differences in agricultural production. As women in the general population are at higher risk of asthma, exploring gender differences in occupational studies is critical for a deeper understanding of respiratory disease among agricultural workers.
DOI: 10.6084/m9.figshare.11996226
title: Gender differences in respiratory health outcomes among farming cohorts around the globe: findings from the AGRICOH consortium

description: <b>Purpose</b>: Respiratory hazards of farming have been identified for centuries, with little focus on gender differences. We used data from the AGRICOH consortium, a collective of prospective cohorts of agricultural workers, to assess respiratory disease prevalence among adults in 18 cohorts representing over 200,000 farmers, farm workers, and their spouses from six continents. <b>Methods</b>: Cohorts collected data between 1992 and 2016 and ranged in size from 200 to &gt;128,000 individuals; 44% of participants were female. Farming practices varied from subsistence farming to large-scale industrial agriculture. All cohorts provided respiratory outcome information for their cohort based on their study definitions. The majority of outcomes were based on self-report using standard respiratory questionnaires; the greatest variability in assessment methods was associated with chronic obstructive pulmonary disease (COPD). <b>Results</b>: For all three respiratory symptoms (cough, phlegm, and wheeze), the median prevalence in men was higher than in women, with the greatest difference for phlegm (17% vs. 10%). For asthma, women had a higher prevalence (7.8% vs 6.5%), with the difference associated with allergic asthma. The relative proportion of allergic asthma varied among cohorts. In two of eight cohorts for women and two of seven cohorts for men, allergic asthma was more common than non-allergic asthma. <b>Conclusions</b>: These findings indicate that respiratory outcomes are common among farmers around the world despite differences in agricultural production. As women in the general population are at higher risk of asthma, exploring gender differences in occupational studies is critical for a deeper understanding of respiratory disease among agricultural workers.
DOI: 10.6084/m9.figshare.14962356
title: PDBBind in Atoms Format

description: http://www.pdbbind.org.cn/
DOI: 10.6084/m9.figshare.14962356.v2
title: PDBBind in Atoms Format

description: http://www.pdbbind.org.cn/
DOI: 10.6084/m9.figshare.14962356.v1
title: PDBBind in Atoms Format

description: http://www.pdbbind.org.cn/