cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21223/p3/cblrdj
title: Dataset for: Modeling of virus transmission for PYVV by Trialeurodes vaporariorum

description: The effect of temperature on the behavior of the Potato yellow vein virus (PYVV), based on the interaction with its vector Trialeurodes vaporariorum (Westwood, 1856) depending on the temperature at which the virus can be acquired and transmitted efficiently through a viruliferous adult, generating a tool to predict its potential transmission in areas where it is not present yet. The efficiency of PYVV transmission was determined through five controlled laboratory experiments at different temperatures (12, 15, 18, 20 y 25 ºC). Whitefly adults were exposed for a period of 48 hours for the period of access to acquisition (PAA), then the viruliferous insects were transferred to free of virus plants (one insect per plant) for a period of access to inoculation (PAI) of 72 hours. At 30 days after the PAI, the presence of the virus was confirmed by RT-PCR. The highest transmission rates of the virus were obtained at 12 and 15ºC with 14 and 64 % respectively. While at 18, 20, 25 ºC, a percentage of 4, 4 and 3% of virus transmission were obtained respectively. Additionally, a serial transmission was made at natural fluctuating temperature during 3 seasons of the year (Summer – Autumn – Winter) to corroborate the period of infectivity retention by the vector. During the first 2 seasons of the year, no success was found in the transmission, while in winter time, transmission efficiency was reached until day 10 of inoculation and with a minimum of 5 days that PYVV can be inoculated by its vector.
DOI: 10.21223/p3/clypci
title: Dataset for: Life-table of Prodiplosis longifila

description: The effect of temperature on the development of immature stages and survival, as well as adult longevity and reproduction of Prodiplosis longifila was studied in controlled incubator chambers at three constant temperatures from 20º to 30ºC on its host potato. Data collected in the life-table studies under constant temperature conditions were arranged in incomplete life-table formats as required by ILCYM’s ‘model builder’ to process, analyze and develop the phenology model (development time and its variation, development rate, senescence, mortality, total oviposition and relative oviposition frequency). ILCYM’s ‘validation and simulation’ module was applied for simulating life-table parameters and for model validation. The best fit model was selected based on Akaike’s Information Criterion, a well-known goodness of fit indicator or other built in statistics (R2, Adjusted R2, MSE). The development of the Prodiplosis longifila phenology model and its life-table parameter simulation were conducted using the Insect Life Cycle Modeling (ILCYM) software version 3.0 developed by CIP. It is freely available at CIP’s website: https://research.cip.cgiar.org/confluence/display/ilcym/
DOI: 10.21223/p3/dxbpkz
title: Dataset for: Life-table of Diglyphus begini

description: The effect of temperature on the development of immature stages and survival, as well as adult longevity and reproduction of Diglyphus begini was studied in controlled incubator chambers at six constant temperatures from 10º to 35ºC on its host bean. Data collected in the life-table studies under constant temperature conditions were arranged in incomplete life-table formats as required by ILCYM’s ‘model builder’ to process, analyze and develop the phenology model (development time and its variation, development rate, senescence, mortality, total oviposition and relative oviposition frequency). ILCYM’s ‘validation and simulation’ module was applied for simulating life-table parameters and for model validation. The best fit model was selected based on Akaike’s Information Criterion, a well-known goodness of fit indicator or other built in statistics (R2, Adjusted R2, MSE). The development of the Diglyphus begini phenology model and its life-table parameter simulation were conducted using the Insect Life Cycle Modeling (ILCYM) software version 3.0 developed by CIP. It is freely available at CIP’s website: https://research.cip.cgiar.org/confluence/display/ilcym/
DOI: 10.21223/p3/j7bjvj
title: Dataset for: Life-table of Diglyphus websteri

description: The effect of temperature on the development of immature stages and survival, as well as adult longevity and reproduction of Diglyphus websteri was studied in controlled incubator chambers at five constant temperatures from 15º to 35ºC on its host bean. Data collected in the life-table studies under constant temperature conditions were arranged in incomplete life-table formats as required by ILCYM’s ‘model builder’ to process, analyze and develop the phenology model (development time and its variation, development rate, senescence, mortality, total oviposition and relative oviposition frequency). ILCYM’s ‘validation and simulation’ module was applied for simulating life-table parameters and for model validation. The best fit model was selected based on Akaike’s Information Criterion, a well-known goodness of fit indicator or other built in statistics (R2, Adjusted R2, MSE). The development of the Diglyphus websteri phenology model and its life-table parameter simulation were conducted using the Insect Life Cycle Modeling (ILCYM) software version 3.0 developed by CIP. It is freely available at CIP’s website: https://research.cip.cgiar.org/confluence/display/ilcym/
DOI: 10.21223/p3/p9kkcu
title: Dataset for: Life-table of Delphastus quinculus

description: The effect of temperature on the development of immature stages and survival, as well as adult longevity and reproduction of Delphastus quinculus was studied in controlled incubator chambers at five constant temperatures from 18º to 32ºC on its host potato. Data collected in the life-table studies under constant temperature conditions were arranged in incomplete life-table formats as required by ILCYM’s ‘model builder’ to process, analyze and develop the phenology model (development time and its variation, development rate, senescence, mortality, total oviposition and relative oviposition frequency). ILCYM’s ‘validation and simulation’ module was applied for simulating life-table parameters and for model validation. The best fit model was selected based on Akaike’s Information Criterion, a well-known goodness of fit indicator or other built in statistics (R2, Adjusted R2, MSE). The development of the Delphastus quinculus phenology model and its life-table parameter simulation were conducted using the Insect Life Cycle Modeling (ILCYM) software version 3.0 developed by CIP. It is freely available at CIP’s website: https://research.cip.cgiar.org/confluence/display/ilcym/
DOI: 10.21223/p3/tcblpt
title: Dataset for: Baseline Survey for Scaling Up Sweetpotato Through Agriculture and Nutrition (SUSTAIN) Project, Kenya

description: Scaling Up Sweetpotato Through Agriculture and Nutrition (SUSTAIN) was a 5-year project of the International Potato Center (CIP). The project aimed at improving production and consumption of vitamin A-rich orange-fleshed sweetpotato (OFSP) by households with children under five years of age in Kenya. A study was conducted to provide baseline information about the status of the households in terms of sweetpotato production, consumption and sale of OFSP. It was also intended to obtain information on dietary diversity, feeding practices, household food security status, vitamin A and acceptance of OFSP among the households. Data was collected from 600 households randomly selected from five counties where SUSTAIN project was implemented, namely, Siaya, Kisumu, Homa bay, Nyamira and Migori. In each household, the head responded to the agriculture modules of the tool while the reference woman (defined as a caregiver of child less than 5 years of age or lactating/pregnant woman) responded to the questions on knowledge, household nutrition, food security and feeding practices. Household’s diet quality was assessed using the standard child dietary diversity (CDDS) and minimum dietary diversity for women of reproductive age (MDD-W) as well as frequency of consumption of different foods based on 24-hour recall. The vitamin consumption frequency was assessed using scores based on the Helen Keller International (HKI) approach based on 7-day recall.
DOI: 10.5066/p9rlncae
title: Hatchery Trout Predation Data, Arizona

description: These data were compiled to evaluate the potential adverse impacts that stocked trout may have on native fish populaitons through predation. Hatchery-reared rainbow and brown trout are typically fed exclusively on commercially prepared pelleted feeds and have no experience catching or consuming life fish at the time of stocking and yet stocked fish are percieved to present a high predation risk for juvenile native fishes. These data were compiled to allow for a relative comparison of predation effectiveness between hatchery-reared fish and wild fish. Mean numbers of prey fish surviving in replicated overnight laboratory predation trials were quantified.
DOI: 10.5066/p9rxs3hu
title: Molokai (Kawela) USGS ridge-to-reef vegetation monitoring study 2009-2014

description: This data release contains two files: 1) the raw data collected as part of a vegetation monitoring study that was conducted to assess the changes in plant species frequency inside and outside a fenced exclosure, constructed to eliminate browsing and grazing by feral goats, from 2009-2014 in the US Geological Survey's Kawela research site on the island of Molokai, Hawaii. 2) A list of species that were recorded during the vegetation monitoring or species that were found in the general area but not recorded during monitoring.
DOI: 10.5066/p9s3cvni
title: Data release for mean random reflectance for products of hydrous pyrolysis experiments on artificial rock mixtures of humic Wyodak-Anderson coal (2018)

description: Mean random vitrinite reflectance (Ro) is the most widely accepted method to determine thermal maturity of coal and other sedimentary rocks. However, oil-immersion Ro of polished rock or kerogen samples is commonly lower than Ro values measured in samples from adjacent vitrinite-rich coals that have undergone the same level of thermal stress. So-called suppressed Ro values have also been observed in hydrous pyrolysis experiments designed to simulate petroleum formation. Various hypotheses to explain Ro suppression, such as sorption of products generated from liptinite during maturation, diagenetic formation of perhydrous vitrinite or overpressure, remain controversial. To experimentally test for suppression of vitrinite reflectance, artificial rock was prepared using silica and a calcined blend of limestone and clay with various proportions of thermally immature vitrinite-rich Wyodak-Anderson coal and liptinite-rich kerogen isolated from the oil-prone Parachute Creek Member of the Green River Formation. The samples were subjected to hydrous pyrolysis for 72 hr. at isothermal temperatures of 300 C, 330 C, and 350 C to simulate burial maturation. Compared to artificial rock that contains only coal, samples with different proportions of oil-prone kerogen show distinct suppression of calibrated Ro at 300 C and 330 C. The reflectance of solid bitumen generated during heating of the samples is lower than that of the associated vitrinite and does not interfere with the Ro measurements. These results provide the first experimental evidence that Ro suppression occurs in vitrinite mixed with liptinite-rich kerogen in a rock matrix. Although the precise chemical mechanism for Ro suppression by liptinite remains unclear, free radicals generated from solid bitumen and associated volatile products during maturation of liptinite may contribute to termination reactions that slow the aromatization and rearrangement of polyaromatic sheets in vitrinite, thus suppressing Ro. This mechanism does not preclude Ro suppression that might result from overpressure or differences in redox conditions during diagenesis.
DOI: 10.5281/zenodo.1316988
title: Accurately Inferring Personality Traits from the Use of Mobile Technology

description: This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents. The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation. <em><strong>Feature extraction pipeline</strong></em> For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point. First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on. Next, we process both the refined and the raw time series to extract the features, as follows: <strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here. <strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: The frequency of highest energy (we prefix its name with <em>top_frequency</em>); The <em>periodicity</em> of the series in the frequency domain; The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>); The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em> The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>). The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends). The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets. <em><strong>Source code</strong></em> The Python source code developed to engineer and evaluate the embeddings is available here.
DOI: 10.5281/zenodo.1316989
title: Accurately Inferring Personality Traits from the Use of Mobile Technology

description: This dataset contains the features extracted from Spatio-Temporal Mobility and Context of Use and the Big5 scores from the 50-item IPIP survey of 55 volunteers from 6 countries located in 2 continents. The authors predict the Big5 traits by fitting 5 regularized linear regression models, one per trait, and select the regularization parameter and evaluate the prediction performance through nested leave-one-out cross validation. <em><strong>Feature extraction pipeline</strong></em> For each volunteer, we start the pipeline with 5 time series encoding, in time, her WGS84 coordinates (latitude and longitude), measurements related to her smartphone's battery (charging status and level), surrounding WiFi APs and BT devices, and whether her phone was connected to a WiFi access point. First, we refine the 5 raw time series to accurately describe the spatio-temporal mobility and the context of our volunteers. For example, we create a binary time series that peaks when the user is at home, or when the user is at work, and so on. Next, we process both the refined and the raw time series to extract the features, as follows: <strong>Statistical Features</strong>: We divide the raw time series in intervals of one day. We aggregate the different values within each day into a single numerical measurement (e.g., by computing the average, the count of unique values, the information entropy, or the repetitiveness). Finally, we aggregate the measurements obtained across all days into a single value --- the value of that feature for the selected user --- by measuring the mean (<em>avg</em>), the standard deviation (<em>std</em>), and the coefficient of variation (<em>cov</em>). Features prefixed with <em>avg</em>, <em>std</em>, or <em>cov, </em>have been extracted as described here. <strong>Spectral Analysis Features</strong>: We first apply the DFT to the raw time series. Then, we measure: The frequency of highest energy (we prefix its name with <em>top_frequency</em>); The <em>periodicity</em> of the series in the frequency domain; The energy at the daily and weekly frequencies (<em>daily_energy </em>and <em>weekly_energy</em>); The frequency, the periodicity, and the daily and weekly energy obtained after processing the time series with Welch's method and a two weeks window (<em>w_top_frequency</em>, <em>w_periodicity</em>, <em>w_daily_energy, w_weekly_energy);</em> The euclidean distance between the DFT and a pure sine wave with period equivalent to the top frequency of the series (<em>distance_from_sine</em>). The string <em>b_day </em>in each name specifies that the features only consider business days (i.e. they exclude holidays and weekends). The 5 columns named O, C, E, A, and N, score the users on the Big5 and represent the prediction targets. <em><strong>Source code</strong></em> The Python source code developed to engineer and evaluate the embeddings is available here.
DOI: 10.5281/zenodo.2255286
title: Mitigating Traffic Congestion on I-10 in Baton Rouge, LA: Supply- and Demand-Oriented Strategies & Treatments

description: Corresponding data set for Tran-SET Project No. 17ITSLSU09. Abstract of the final report is stated below for reference: "The aim of this study is to develop a better understanding of the causes of traffic congestion on I-10 in the Baton Rouge, LA area, particularly at the I-10 Mississippi River Bridge, and to identify treatments and strategies to mitigate congestion at the bridge site. This study developed and calibrated a microsimulation model of I-10 (from Lobdell Highway in Port Allen to Highland Road, I-110 to Florida Street, and I-12 to Walker Road) and investigated several supply- and demand-oriented strategies. This includes: rehabilitation and utilization of the old Mississippi River Bridge on US-190 and the existing US-190/US-61 corridor, overall demand management of I-10 EB traffic, reduction in percent trucks traveling eastbound on I-10 during the A.M. peak period, and ramp metering at the on-ramp west of the I-10 Mississippi River Bridge. The majority of the tested strategies appear to be feasible and effective solutions; however, a combination of supply- and demand-oriented treatments must be implemented to fully relieve congestion on I-10 in Baton Rouge."
description: Tran-SET Project No. 17ITSLSU09
DOI: 10.5281/zenodo.2255287
title: Mitigating Traffic Congestion on I-10 in Baton Rouge, LA: Supply- and Demand-Oriented Strategies & Treatments

description: Corresponding data set for Tran-SET Project No. 17ITSLSU09. Abstract of the final report is stated below for reference: "The aim of this study is to develop a better understanding of the causes of traffic congestion on I-10 in the Baton Rouge, LA area, particularly at the I-10 Mississippi River Bridge, and to identify treatments and strategies to mitigate congestion at the bridge site. This study developed and calibrated a microsimulation model of I-10 (from Lobdell Highway in Port Allen to Highland Road, I-110 to Florida Street, and I-12 to Walker Road) and investigated several supply- and demand-oriented strategies. This includes: rehabilitation and utilization of the old Mississippi River Bridge on US-190 and the existing US-190/US-61 corridor, overall demand management of I-10 EB traffic, reduction in percent trucks traveling eastbound on I-10 during the A.M. peak period, and ramp metering at the on-ramp west of the I-10 Mississippi River Bridge. The majority of the tested strategies appear to be feasible and effective solutions; however, a combination of supply- and demand-oriented treatments must be implemented to fully relieve congestion on I-10 in Baton Rouge."
description: Tran-SET Project No. 17ITSLSU09
DOI: 10.5281/zenodo.2254715
title: Development, Training, Education, and Implementation of Low-Cost Sensing Technologies for Bridge Structural Health Monitoring (SHM)

description: Corresponding data set for Tran-SET Project No. 17STUNM02. Abstract of the final report is stated below for reference: "Transportation infrastructure needs continuous monitoring. However, traditional inspections cost money and are conducted visually. New technologies for bridge monitoring are expensive and complex. This project involved developing cost-effective sensor technologies that can be applied towards the maintenance of railroad bridges by recording reference-free transverse displacement. More specifically, this project developed new applications of new technologies (Arduino, wireless smart sensors, drones, Hololens) and promoted workforce development with an emphasis on outreach of high-school students. This project was carried out in three main phases: (1) development and validation of technologies, (2) education and outreach to students, and (3) outreach to industry consisting in one professional workshop. The findings from the first phase showed that the data gathered by these new low-cost sensing systems were comparable to the data collected using traditional sensors. Researchers collected the findings of the second phase of the project through surveys conducted from Middle school, High school and college students during and after outreach activities. these surveys showed that many of the participant students got more interested in the use of new technologies after getting familiar with them. Finally, researchers collected the findings of the third phase of the project through a workshop collecting the interest and challenges of the owners of railroad infrastructure. The top interest of railroad owners is to explore the use of new technologies to increase safety in the field. The conclusions of this research include prioritization on developing low-cost technologies that can measure simple parameters in the field of interest to existing inspectors."
description: Tran-SET Project No. 17STUNM02