cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7303/syn9727752
title: Dataset: Epigenomic, genomic, and transcriptomic landscape of schwannomatosis
DOI: 10.6084/m9.figshare.16649167
title: Data to build temporal subnetworks

description: Raw data
DOI: 10.25810/k55w-7a04.41
title: World Radio Coverage of Climate Change or Global Warming, 2000-2021 - August 2021

description:         The Media and Climate Change Observatory Data monitors 127 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.6084/m9.figshare.12854147
title: The evolution of the southern Namibian Karoo-aged basins: implications from detrital zircon geochronologic and geochemistry data

description: Detrital U-Pb zircon age dating and grain morphometrics were used for provenance and maximum deposition age determination for the Lower Permian Dwyka and Middle to Upper Permian Ecca groups of southern Namibia’s Aranos and Karasburg basins. Thirteen sedimentary samples and one ash fall sample were analysed using laser ablation – single collector – inductively coupled plasma – mass spectrometry (LA-ICP-MS). The results reveal prominent Cambrian to Neoproterozoic (500–750 Ma), a Lower Neoproterozoic to Mesoproterozoic (950–1300 Ma), and Upper Palaeoproterozoic (1775–1950 Ma) detrital zircon age populations. Multiple evidence (e.g. roundness of investigated zircon grains and <sub>Ɛ</sub>Hf values) suggest the zircon populations are part of a Gondwana-wide recycling system, where basins act as intermediate reservoirs and eventual dispersers. Classic source to sink dynamics do not apply. A Permian age population (ca. 260–296 Ma) consisting of typically euhedral zircon grains is assumed to be derived directly from protolith rocks. Because no felsic Permian rocks are exposed in the southern African region, these findings suggest a significant input by ash fall from an unknown source, which may be linked to the southern Gondwanan arc volcanism during Permian times. The results reveal a ca. 36 Ma duration of the evolution of the Karasburg Basin and a shorter ca. 30 Ma lasting evolution of the Aranos Basin. The stratigraphic oldest datable unit is an ash bed of the Ganigobis Shale Member of the Zwartbas Fm (Dwyka Group) which gave a maximum deposition age of ca. 296 Ma, whereas the Aussenkjer Fm represents the uppermost Namibian Karoo-aged strata with a maximum age of deposition of ca. 260 Ma.
DOI: 10.5281/zenodo.5123100
title: Weakly hydrated anions bind to polymers but not monomers in aqueous solutions - Source data for Figure 3

description: Source data that was used to create Figure 3.
DOI: 10.5281/zenodo.5517864
title: Micro-blocks in NE Asia amalgamated into the unified Amuria block by ~300 Ma: first paleomagnetic evidence from the Songliao block, NE China

description: These documents are supplementary tables for a manuscript submitted to Journal of Geophysical Research: Solid Earth entitled "Micro-blocks in NE Asia amalgamated into the unified Amuria block by ~300 Ma: first paleomagnetic evidence from the Songliao block, NE China" by Zhang et al.
DOI: 10.5281/zenodo.5123099
title: Weakly hydrated anions bind to polymers but not monomers in aqueous solutions - Source data for Figure 3

description: Source data that was used to create Figure 3.
DOI: 10.6084/m9.figshare.12854147.v2
title: The evolution of the southern Namibian Karoo-aged basins: implications from detrital zircon geochronologic and geochemistry data

description: Detrital U-Pb zircon age dating and grain morphometrics were used for provenance and maximum deposition age determination for the Lower Permian Dwyka and Middle to Upper Permian Ecca groups of southern Namibia’s Aranos and Karasburg basins. Thirteen sedimentary samples and one ash fall sample were analysed using laser ablation – single collector – inductively coupled plasma – mass spectrometry (LA-ICP-MS). The results reveal prominent Cambrian to Neoproterozoic (500–750 Ma), a Lower Neoproterozoic to Mesoproterozoic (950–1300 Ma), and Upper Palaeoproterozoic (1775–1950 Ma) detrital zircon age populations. Multiple evidence (e.g. roundness of investigated zircon grains and <sub>Ɛ</sub>Hf values) suggest the zircon populations are part of a Gondwana-wide recycling system, where basins act as intermediate reservoirs and eventual dispersers. Classic source to sink dynamics do not apply. A Permian age population (ca. 260–296 Ma) consisting of typically euhedral zircon grains is assumed to be derived directly from protolith rocks. Because no felsic Permian rocks are exposed in the southern African region, these findings suggest a significant input by ash fall from an unknown source, which may be linked to the southern Gondwanan arc volcanism during Permian times. The results reveal a ca. 36 Ma duration of the evolution of the Karasburg Basin and a shorter ca. 30 Ma lasting evolution of the Aranos Basin. The stratigraphic oldest datable unit is an ash bed of the Ganigobis Shale Member of the Zwartbas Fm (Dwyka Group) which gave a maximum deposition age of ca. 296 Ma, whereas the Aussenkjer Fm represents the uppermost Namibian Karoo-aged strata with a maximum age of deposition of ca. 260 Ma.
DOI: 10.5281/zenodo.4937283
title: Micro-blocks in NE Asia amalgamated into the unified Amuria block by ~300 Ma: first paleomagnetic evidence from the Songliao block, NE China

description: These documents are supplementary tables for a manuscript submitted to Journal of Geophysical Research: Solid Earth entitled "Micro-blocks in NE Asia amalgamated into the unified Amuria block by ~300 Ma: first paleomagnetic evidence from the Songliao block, NE China" by Zhang et al.
DOI: 10.5281/zenodo.5517359
title: Does Native Multilingualism Lead to Enhanced Executive Functioning in Adulthood? - A Study Examining Inhibitory Control (Stroop Effect) in University Students

description: The SPSS file lists all the subjects and information on these relevant to the Stroop Effect analysis, whose purpose was to find out whether native multilingualism has some effect on executive control in native multilinguals at their late age.
DOI: 10.5281/zenodo.5517358
title: Does Native Multilingualism Lead to Enhanced Executive Functioning in Adulthood? - A Study Examining Inhibitory Control (Stroop Effect) in University Students

description: The SPSS file lists all the subjects and information on these relevant to the Stroop Effect analysis, whose purpose was to find out whether native multilingualism has some effect on executive control in native multilinguals at their late age.
DOI: 10.6084/m9.figshare.15135426.v1
title: On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls

description: We consider the asymptotic properties of the Synthetic Control (SC) estimator when both the number of pre-treatment periods and control units are large. If potential outcomes follow a linear factor model, we provide conditions under which the SC unit asymptotically recovers the factor structure of the treated unit, even when the pre-treatment fit is imperfect. This happens when there are weights diluted among an increasing number of control units such that a weighted average of the factor structure of the control units asymptotically reconstructs the factor structure of the treated unit. In this case, the SC estimator is asymptotically unbiased even when treatment assignment is correlated with time-varying unobservables.
DOI: 10.6084/m9.figshare.16649176
title: Accompanying data for Journal of Ecology Article "A soil-nesting invasive ant disrupts carbon dynamics in saplings of a foundational ant-plant"

description: Data used for analyses described in the Journal of Ecology article "A soil-nesting invasive ant disrupts carbon dynamics in saplings of a foundational ant-plant" by Patrick Milligan, Timothy Martin, Elizabeth Pringle, Corinna Riginos, Gabriella Mizell, and Todd Palmer. Contact patrickdmilligan@gmail.com for any further questions regarding the data, and refer to main text for a description of statistical analyses.
DOI: 10.6084/m9.figshare.16649176.v1
title: Accompanying data for Journal of Ecology Article "A soil-nesting invasive ant disrupts carbon dynamics in saplings of a foundational ant-plant"

description: Data used for analyses described in the Journal of Ecology article "A soil-nesting invasive ant disrupts carbon dynamics in saplings of a foundational ant-plant" by Patrick Milligan, Timothy Martin, Elizabeth Pringle, Corinna Riginos, Gabriella Mizell, and Todd Palmer. Contact patrickdmilligan@gmail.com for any further questions regarding the data, and refer to main text for a description of statistical analyses.
DOI: 10.5281/zenodo.5516556
title: Turbine Upgrade Dataset

description: This dataset includes three sets, one corresponding to an actual vortex generator installation and two corresponding to an artificial pitch angle adjustment. Two pairs of wind turbines from the same inland wind farm, as used in Chapter 5 of the Data Science for Wind Energy book, are chosen to provide the data, each pair consisting of two wind turbines, together with a nearby met mast. The turbine that undergoes an upgrade in a pair is referred to as the experimental turbine, the reference turbine, or the test turbine, whereas the one that does not have the upgrade is referred to as the control turbine. In both pairs, the test turbine and the control turbine are practically identical and were put into service at the same time. This wind farm is on a reasonably flat terrain. The power output, y, is measured on individual turbines, whereas the environmental variables in x (i.e., the weather covariates) are measured by sensors at the nearby mast. For this dataset, there are five variables in x and they are the same as those in the Inland Wind Farm Dataset1. For the vortex generator installation pair, there are 14 months' worth of data in the period before the upgrade and around eight weeks of data after the upgrade. For the pitch angle adjustment pair, there are about eight months of data before the upgrade and eight and a half weeks after the upgrade. Note that the pitch angle adjustment is not physically carried out, but rather simulated on the respective test turbine. The following data modification is done to the test turbine data. The actual test turbine data, including both power production data and environmental measurements, are taken from the actual turbine pair operation. Then, the power production from the designated test turbine on the range of wind speed over 9 m/s is increased by 5%, namely multiplied by a factor of 1.05, while all other variables are kept the same. No data modification of any kind is done to the data affiliated with the control turbine in the pitch angle adjustment pair. The third column of a respective dataset is the upgrade status variable, of which a zero means the test turbine is not modified yet, while a one means that the test turbine is modified. The upgrade status has no impact on the control turbine, as the control turbine remains unmodified throughout. The vortex generator installation takes effect on June 20, 2011, and the pitch angle adjustment takes effect on April 25, 2011.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516555
title: Turbine Upgrade Dataset

description: This dataset includes three sets, one corresponding to an actual vortex generator installation and two corresponding to an artificial pitch angle adjustment. Two pairs of wind turbines from the same inland wind farm, as used in Chapter 5 of the Data Science for Wind Energy book, are chosen to provide the data, each pair consisting of two wind turbines, together with a nearby met mast. The turbine that undergoes an upgrade in a pair is referred to as the experimental turbine, the reference turbine, or the test turbine, whereas the one that does not have the upgrade is referred to as the control turbine. In both pairs, the test turbine and the control turbine are practically identical and were put into service at the same time. This wind farm is on a reasonably flat terrain. The power output, y, is measured on individual turbines, whereas the environmental variables in x (i.e., the weather covariates) are measured by sensors at the nearby mast. For this dataset, there are five variables in x and they are the same as those in the Inland Wind Farm Dataset1. For the vortex generator installation pair, there are 14 months' worth of data in the period before the upgrade and around eight weeks of data after the upgrade. For the pitch angle adjustment pair, there are about eight months of data before the upgrade and eight and a half weeks after the upgrade. Note that the pitch angle adjustment is not physically carried out, but rather simulated on the respective test turbine. The following data modification is done to the test turbine data. The actual test turbine data, including both power production data and environmental measurements, are taken from the actual turbine pair operation. Then, the power production from the designated test turbine on the range of wind speed over 9 m/s is increased by 5%, namely multiplied by a factor of 1.05, while all other variables are kept the same. No data modification of any kind is done to the data affiliated with the control turbine in the pitch angle adjustment pair. The third column of a respective dataset is the upgrade status variable, of which a zero means the test turbine is not modified yet, while a one means that the test turbine is modified. The upgrade status has no impact on the control turbine, as the control turbine remains unmodified throughout. The vortex generator installation takes effect on June 20, 2011, and the pitch angle adjustment takes effect on April 25, 2011.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516558
title: Wake Effect Dataset

description: This dataset includes data from six pairs of wind turbines (or, 12 wind turbines in total) and three met masts. The turbine pairs are chosen such that no other turbines except the pair are located within 10 times the turbine's rotor diameter. Such arrangement is to find a pair of turbines that are free of other turbines' wake, so that the wake analysis result can be reasonably attributed to the wake of its pair turbine. The operational data for the six pairs of turbines are taken during roughly a yearlong period between 2010 and 2011. The datasets include wind power output, wind speed, wind direction, air pressure, and temperature, of which air pressure and temperature data are used to calculate air density. The wind power outputs and wind speeds are measured on the turbine, and all other variables are measured at the met masts. The data from Mast 1 are associated with the data for Turbine Pairs 1 and 2, Mast 2 with Pairs 3 and 4, and Mast 3 with Pairs 5 and 6. Figure 8.6 of the Data Science for Wind Energy book shows the relative locations of the six pairs of turbines and three met masts.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516557
title: Wake Effect Dataset

description: This dataset includes data from six pairs of wind turbines (or, 12 wind turbines in total) and three met masts. The turbine pairs are chosen such that no other turbines except the pair are located within 10 times the turbine's rotor diameter. Such arrangement is to find a pair of turbines that are free of other turbines' wake, so that the wake analysis result can be reasonably attributed to the wake of its pair turbine. The operational data for the six pairs of turbines are taken during roughly a yearlong period between 2010 and 2011. The datasets include wind power output, wind speed, wind direction, air pressure, and temperature, of which air pressure and temperature data are used to calculate air density. The wind power outputs and wind speeds are measured on the turbine, and all other variables are measured at the met masts. The data from Mast 1 are associated with the data for Turbine Pairs 1 and 2, Mast 2 with Pairs 3 and 4, and Mast 3 with Pairs 5 and 6. Figure 8.6 of the Data Science for Wind Energy book shows the relative locations of the six pairs of turbines and three met masts.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516561
title: Turbine Bending Moment Dataset

description: his dataset includes two parts. The first part is three sets of physically measured blade-root flapwise bending moments on three respective turbines, courtesy of Riso-DTU (Technical University of Denmark). The basic characteristics of the three turbines can be found in Table 10.1 of the Data Science for Wind Energy book. These datasets include three columns. The first column is the 10-min average wind speed, the second column is the standard deviation of wind speed within a 10-min block, and the third column is the maximum bending moment, in the unit of MN-m, recorded in a 10-min block. The second part of the dataset is the simulated load data used in Section 10.6.5 of the same book. This part has two sets. The first set is the training data that has 1,000 observations and is used to fit an extreme load model. The second set is the test data that consists of 100 subsets, each of which has 100,000 observations. In other words, the second dataset for testing has a total of 10,000,000 observations, which are used to verify the extreme load extrapolation made by a respective model. Both simulated datasets have two columns: the first is the 10-min average wind speed and the second is the maximum bending moment in the corresponding 10-min block. While all other datasets are saved in CSV file format, this simulated test dataset is saved in a text file format, due to its large size. The data simulation procedure is explained in Section 10.6.5.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516560
title: Turbine Bending Moment Dataset

description: his dataset includes two parts. The first part is three sets of physically measured blade-root flapwise bending moments on three respective turbines, courtesy of Riso-DTU (Technical University of Denmark). The basic characteristics of the three turbines can be found in Table 10.1 of the Data Science for Wind Energy book. These datasets include three columns. The first column is the 10-min average wind speed, the second column is the standard deviation of wind speed within a 10-min block, and the third column is the maximum bending moment, in the unit of MN-m, recorded in a 10-min block. The second part of the dataset is the simulated load data used in Section 10.6.5 of the same book. This part has two sets. The first set is the training data that has 1,000 observations and is used to fit an extreme load model. The second set is the test data that consists of 100 subsets, each of which has 100,000 observations. In other words, the second dataset for testing has a total of 10,000,000 observations, which are used to verify the extreme load extrapolation made by a respective model. Both simulated datasets have two columns: the first is the 10-min average wind speed and the second is the maximum bending moment in the corresponding 10-min block. While all other datasets are saved in CSV file format, this simulated test dataset is saved in a text file format, due to its large size. The data simulation procedure is explained in Section 10.6.5.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516565
title: Simulated Bending Moment Dataset

description: This dataset includes two sets. One set has 600 data records, corresponding to the training set referred to in Section 11.4.1 of the Data Science for Wind Energy book, whereas the other set has 10,000 data records. Each set has three columns of data (other than the serial number). The first column is the wind speed, simulated using a Rayleigh distribution, and the second and third columns are, respectively, the simulated flapwise and edgewise bending moments, in the unit of kN-m. The flapwise and edgewise bending moments are simulated from TurbSim and FAST. The simulations were conducted by Dr. Eunshin Byon at the University of Michigan.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516564
title: Simulated Bending Moment Dataset

description: This dataset includes two sets. One set has 600 data records, corresponding to the training set referred to in Section 11.4.1 of the Data Science for Wind Energy book, whereas the other set has 10,000 data records. Each set has three columns of data (other than the serial number). The first column is the wind speed, simulated using a Rayleigh distribution, and the second and third columns are, respectively, the simulated flapwise and edgewise bending moments, in the unit of kN-m. The flapwise and edgewise bending moments are simulated from TurbSim and FAST. The simulations were conducted by Dr. Eunshin Byon at the University of Michigan.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5516549
title: Wind Spatio-Temporal Dataset2

description: This dataset is used in Chapter 4 of the Data Science for Wind Energy book. The data used in this study consists of one year of spatio-temporal measurements at 200 randomly selected turbines on a flat terrain inland wind farm, between 2010 and 2011. The data consists of turbine-specific hourly wind speeds measured by the anenometers mounted on each turbine. In addition, one year of hourly wind speed and direction measurements are available at three met masts on the same wind farm. Column B through Column OK are the wind speed and wind power associated with each turbine, followed by Column OL through Column OQ, which are for wind speed and wind direction associated with each mast. The coordinates of the turbines and masts are listed in the top rows, preceding the wind speed, direction, and power data. The coordinates are shifted by a constant, so that while the relative positions of the turbines and the met masts remain faithful to the actual layout, their true geographic information is kept confidential. This anemometer network provides a coverage of a spatial resolution of one mile and a temporal resolution of one hour.
description: {"references": ["Ding, Y. (2019) Data Science for Wind Energy, Chapman & Hall/CRC Press, Boca Raton, FL"]}
DOI: 10.5281/zenodo.5517869
title: gooreal

description: gooreal